Category: Profilometry | Geometry and Shape

Pacing Lead Insulation Wear Testing in Hanks’ Solution

Application Note | Medical Device Tribology

Nano-Friction and Wear Testing of Pacing Lead Insulation in Hanks’ Solution

Tribological analysis of silicone and polyether-polyurethane endocardial lead materials

Research & Experimental Testing

Duanjie Li, PhD

Visual Design & Editorial

Andrew Shore

Introduction

A pacemaker is a medical device used to regulate heart rhythm and maintain an adequate heart rate. It is typically implanted in the chest or abdomen and sends electrical impulses to the heart muscle through endocardial pacing leads.

As pacemakers remain a widely used treatment for cardiac rhythm disorders, the quality and service life of pacing leads are critical to long-term device performance. Lead failures can create serious risks for patients and may require surgical replacement, making pacing lead insulation wear testing an important part of material evaluation for implantable cardiac devices.^1–5

Endocardial pacing leads transmit electrical impulses from a pacemaker to the heart while operating in a dynamic body-fluid environment.

Why Friction and Wear Matter for Endocardial Lead Insulation

The outer insulation material of an endocardial lead requires several key properties, including biological inertness, high flexibility, fracture toughness, and long service life. Low friction can reduce interaction between the lead and the blood vessel, helping minimize vessel irritation during implantation and movement.

Wear resistance is also critical. Endocardial leads experience continuous movement from the heart and surrounding body structures, while operating in a body-fluid environment that can influence friction, wear, and material response.

Because of this complex environment, endocardial lead insulation should be evaluated using controlled tribological methods that simulate relevant contact conditions. Testing in Hanks’ solution allows the friction and wear behavior of lead insulation materials to be compared under a simulated body-fluid condition rather than relying only on dry testing.

Nano-friction test setup used to evaluate endocardial pacing lead insulation materials under low-load contact conditions.

Measurement Objective

This study compares the nano-friction and wear behavior of endocardial pacing lead insulation materials in Hanks’ solution. Silicone and polyether-polyurethane lead materials were evaluated to determine how each material responds under simulated body-fluid conditions.

Low-load nano-friction testing was performed using the Nano Module of the NANOVEA Mechanical Tester to measure coefficient of friction at controlled contact force. Reciprocating wear testing was then performed using a NANOVEA Tribometer to compare wear resistance under linear sliding contact.

NANOVEA T50 Compact
Free Weight Tribometer

NANOVEA PB1000 Large Platform Mechanical Tester

Measurement Principle

Nano-Friction Measurement Principle

Nano-friction testing measures the coefficient of friction (COF) between the test surface and a controlled counter material under very low applied load. In this study, the indenter made contact with the pacing lead insulation surface while the Nano Module maintained a constant load throughout the measurement.

The Nano Module uses a fast piezoelectric system and load cell to adjust the ball position and keep the applied load stable during sliding. The sample is moved at a controlled speed while lateral force is measured and plotted against displacement.

A stainless steel ball with a 6 mm diameter is commonly used for this type of measurement, although other counter materials, shapes, and sizes can be selected to simulate different contact conditions. This allows pacing lead insulation materials to be evaluated under controlled low-load friction conditions relevant to biomedical device applications.

Nano-friction measurement schematic showing controlled low-load sliding contact and lateral force measurement during reciprocating motion.

Reciprocating Wear Principle

Reciprocating wear testing evaluates material response under repeated linear sliding contact. A flat or spherical counter material is loaded against the test sample with a precisely known force, while the sample moves back and forth in a controlled reciprocating motion.

The counter material, such as a pin or ball, is mounted on a stiff lever that functions as a low-friction force transducer. As the sample moves, frictional forces between the counter material and the sample are measured using a strain gauge sensor on the tribometer arm.

After the test, the resulting wear track can be examined to compare material damage, wear scar geometry, and surface response. This method allows friction and wear behavior to be studied under controlled conditions, including variations in time, contact pressure, sliding speed, temperature, humidity, and lubrication environment.

Linear reciprocating wear schematic showing a pin or ball counterface sliding across the sample to generate a wear track under controlled load.

Test Procedure

The coefficient of friction (COF) of the pacing lead materials was measured against a stainless steel 440 ball with a 6 mm diameter. Testing was performed using the Nano Module of the Nanovea Mechanical Tester.

The sample was immersed in Hanks’ solution to simulate a body-fluid environment. A low applied load of 50 mN was maintained throughout the test, while the ball slid against the lead surface at a constant speed of 20 mm/min over a total sliding distance of 10 mm.

Wear resistance was evaluated using a Nanovea Tribometer with the Linear Reciprocating Wear Module. During the wear test, a stainless steel 303 block measuring 10 × 10 mm² was used as the counter material, and the coefficient of friction was recorded in situ at 0.1 s intervals.

After testing, the resulting wear tracks were examined under an optical microscope to compare surface damage on the silicone and polyether-polyurethane lead materials. Stainless steel was used as the counter material in this study; however, custom fixtures and alternative counter materials can be used to simulate specific application conditions.

Parameter	Value
Sample	Leads made of silicone or polyether-polyurethane (PP)
Normal force	1 N
Speed	200 cycles/min
Duration of test	5 h
Environment	Hanks’ solution

Wear test parameters used to evaluate silicone and polyether-polyurethane pacing lead materials in Hanks’ solution.

Results and Discussion

Nano-Friction Test

The nano-friction behavior of the silicone and polyether-polyurethane (PP) pacing lead materials was first evaluated using the Nano Module of the Nanovea Mechanical Tester. The coefficient of friction was measured in both dry conditions and Hanks’ solution to compare material response under ambient and simulated body-fluid environments.

Both materials showed significantly lower coefficient of friction in Hanks’ solution than under dry conditions. In Hanks’ solution, the silicone lead exhibited a COF of approximately 0.15, while the polyether-polyurethane lead exhibited a lower COF of approximately 0.05. Under dry conditions, the values were substantially higher, at approximately 0.6 for silicone and 0.5 for polyether-polyurethane.

These results demonstrate the importance of testing pacing lead insulation materials under application-relevant environmental conditions. Hanks’ solution had a strong effect on the measured friction behavior, showing that dry testing alone may not represent the tribological response of lead insulation materials in a simulated body-fluid environment.

The low-load control of the Nano Module allowed the applied force to remain constant at 50 mN during the measurement. This enabled controlled simulation of low-contact-force interaction between the lead material and surrounding biological structures.

Coefficient of friction comparison for silicone and polyether-polyurethane pacing lead materials in dry conditions and Hanks’ solution.

Wear Test

Wear resistance was evaluated using a Nanovea Tribometer to compare the silicone and polyether-polyurethane pacing lead materials in Hanks’ solution. After testing, the lead surfaces were examined visually and under optical microscopy to compare the extent of wear damage.

The silicone lead showed a large wear scar with a width of approximately 1.2 mm. Microscopic observation indicated severe wear on the silicone lead, with parallel deep grooves formed along the movement direction of the rubbing block.

In comparison, the polyether-polyurethane lead showed a narrower wear scar of approximately 0.6 mm. The observed wear was milder, with only several small scratches visible on the shallow surface.

Wear of the lead outer insulation can contribute to pacing and sensing abnormalities, making wear resistance an important factor in endocardial lead material selection.⁶ These results indicate that polyether-polyurethane provided lower friction and better wear resistance than silicone under the tested conditions.

Before-and-after wear comparison of silicone and polyether-polyurethane pacing lead surfaces, including 400x microscope images showing more severe wear on silicone and milder wear on polyether-polyurethane.

Silicone pacing lead surface before reciprocating wear testing.

Polyether-polyurethane pacing lead surface before reciprocating wear testing.

Silicone pacing lead surface after wear testing, showing a pronounced wear scar.

Polyether-polyurethane pacing lead surface after reciprocating wear testing.

400x microscope image of the silicone pacing lead after wear testing, showing deep parallel wear grooves.

400x microscope image of the polyether-polyurethane pacing lead after wear testing, showing comparatively mild surface wear.

Conclusion

This study demonstrated the use of low-load nano-friction testing and reciprocating wear testing to evaluate endocardial pacing lead insulation materials in Hanks’ solution. The Nano Module of the NANOVEA Mechanical Tester measured coefficient of friction under controlled low-load contact, while the NANOVEA Tribometer simulated wear behavior under reciprocating sliding motion.

Testing in Hanks’ solution showed a clear difference between silicone and polyether-polyurethane lead materials. Polyether-polyurethane exhibited lower coefficient of friction and better wear resistance than silicone under the tested conditions, making it the stronger candidate for the outer insulation material of endocardial pacing leads in this study.

These results highlight the importance of evaluating biomedical materials under application-relevant environments rather than relying only on dry testing. Controlled nano-friction and tribology testing can help compare candidate materials, quantify friction response, and evaluate wear resistance for implantable medical device components.

The NANOVEA Mechanical Tester’s Nano, Micro, and Macro modules operate within a single ISO and ASTM compliant platform, enabling consistent evaluation of hardness, elastic modulus, fracture toughness, and wear from a single system. The NANOVEA Tribometer similarly supports rotative and linear wear modes with optional high-temperature, corrosion, and liquid environment modules.

References

[1] Magney JE, Flynn DM, Parsons JA, Staplin DH, Chin-Purcell MV, Milstein S, Hunter DW. Pacing Clin Electrophysiol. 1993; 16:445–457.
[2] Jacobs DM, Fink AS, Miller RP, Anderson WR, McVenes RD, Lessar JF, Cobian KE, et al. Pacing Clin Electrophysiol. 1993; 16:434–444.
[3] Gupta K, Villareal RP, Rasekh A, Massumi A. Tex Heart Inst J. 2003; 30:84–85.
[4] Magney JE, Parsons JA, Flynn DM, Hunter DW. Pacing Clin Electrophysiol. 1995; 18:1509–1517.
[5] Kazama S, Nishiyama K, Machii M, Tanaka K, Amano T, Nomura T, Ohuchi M, et al. Jpn Heart J. 1993; 34:193–200.
[6] Andrzej K, Barbara M, Agnieszka K, Marcin G. Pacing Clin Electrophysiol. 2013; 36(12):1503–1511.

Frequently Asked Questions About Pacing Lead Insulation Wear Testing

How do you evaluate friction and wear behavior of pacing lead insulation?

Pacing lead insulation can be evaluated using low-load friction testing and reciprocating wear testing. These methods measure coefficient of friction, wear scar formation, and surface damage under controlled load, motion, and environmental conditions.

Why is low-load friction testing important for endocardial leads?

Endocardial leads operate under relatively low contact forces while interacting with blood vessels, tissue, and surrounding structures. Low-load friction testing helps evaluate how insulation materials behave under contact conditions that are closer to the application than high-force mechanical testing alone.

What does coefficient of friction indicate in pacing lead material testing?

Coefficient of friction indicates how much resistance occurs during sliding contact between the lead insulation and a counter material. In this study, lower COF values in Hanks’ solution showed that the test environment had a strong effect on the measured friction behavior of silicone and polyether-polyurethane materials.

Why compare silicone and polyether-polyurethane lead insulation materials?

Silicone and polyether-polyurethane are commonly considered for flexible biomedical insulation applications because they can provide different combinations of flexibility, durability, and surface response. Comparing them under the same test conditions helps identify which material provides lower friction and better wear resistance for the intended application.

Which NANOVEA instruments are used for low-load friction and wear testing?

Low-load coefficient of friction can be measured using the Nano Module of a NANOVEA Mechanical Tester, while reciprocating wear behavior can be evaluated using a NANOVEA Tribometer. Together, these systems allow controlled evaluation of friction, wear, and material response for biomedical components.

How is reciprocating wear testing used for pacing lead materials?

Reciprocating wear testing repeatedly slides a counter material across the sample surface under controlled load to create and evaluate a wear track. For pacing lead insulation materials, this allows comparison of wear scar width, surface damage, and material durability under simulated sliding contact.

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Climbing Hold Surface Roughness Analysis

Application Note | 3D Optical Profilometry

Climbing Hold Surface Roughness Analysis Using 3D Optical Profilometry

Measuring Texture, Porosity, and Topography on Bouldering Holds

Research & Experimental Testing

Walter Alabiso, PhD

Visual Design & Editorial

Andrew Shore

Introduction

Bouldering is a demanding discipline that combines physical strength, precise body positioning, and an understanding of how the human body interacts with climbing surfaces. On slab routes, where the wall is angled below vertical and positive holds are limited or absent, a climber’s stability depends almost entirely on the tribological interaction between the body and the climbing hold surface.

Climbing hold surface roughness plays a central role in this contact. Roughness provides the microtexture needed for smearing, a technique where high-friction rubber soles are pressed firmly against the surface to expand the effective contact area and generate adherence. A similar mechanism occurs at the fingers, where the ridges of fingerprints and the pliability of skin deform slightly against the hold’s surface features, creating grip through microscopic interlocking.

Porosity contributes to grip performance by absorbing moisture, sweat, or chalk at the contact interface, preventing the formation of a thin lubricating film that would reduce friction. Micro-cracks and surface flaws act as additional friction points, helping the climber maintain lateral tension against the hold surface. Because these features (roughness, porosity, and surface morphology) operate at different scales and interact differently depending on the hold, quantitative 3D surface measurement is essential for comparing how different climbing hold textures perform under real contact conditions.

Bouldering grips used to compare surface roughness, pore morphology, and grip-related topography.

Why Use Non-Contact Profilometry for Climbing Hold Surface Analysis

Climbing holds and rock-like surfaces can include deep pores, steep asperities, sharp valleys, and irregular texture. These features are difficult to measure accurately with contact-based profilometry because a physical stylus can lose contact, deform local surface features, or fail to reach narrow cavities.

NANOVEA’s non-contact optical profilometry uses chromatic light technology to capture surface height data without touching the sample. This makes it suitable for reconstructing complex climbing hold topography, including deep nooks, pores, and surface flaws, while avoiding measurement artifacts caused by local plastic deformation.

In this study, the NANOVEA JR25 Optical Profiler was used to measure two bouldering grips: a yellow block with a smoother, flatter surface and a green block with a rougher tactile texture. Both samples were scanned using a PS4-MG35 single-point optical sensor with a 3000 µm Z-range and a 4 µm acquisition step in X and Y.

Dual-frequency acquisition was used to reduce light sensor saturation from localized bright spots on the grip surfaces, allowing the profiler to capture roughness and pore morphology across the scanned areas.

Measurement Objective

The objective of this study was to demonstrate how non-contact 3D optical profilometry can be used to reconstruct and compare the surface roughness, topography, and pore morphology of climbing holds.

Two bouldering grip samples were analyzed: a yellow hold with a smoother, flatter surface and a blue hold with a rougher tactile texture and sharper grip features. The analysis focused on surface height variation, areal roughness parameters, pore coverage, pore size, pore depth, and functional surface behavior.

The NANOVEA JR25 Optical Profilometer measuring the climbing hold samples using an optical sensor.

Measurement Method

The NANOVEA JR25 Optical Profiler was used to measure the yellow and blue bouldering grip samples. Each surface was scanned with a PS4-MG35 single-point optical sensor with an enhanced 3000 µm Z-range, allowing the system to capture deep pores, sharp valleys, and irregular surface texture while maintaining a 4 µm acquisition step in X and Y.

Dual-frequency acquisition was used to reduce light sensor saturation from localized bright spots on the grip surfaces, improving data capture across rough, porous, and uneven areas.

NANOVEA JR25 Portable

Optical Profilometer

Test Parameters

Measurement Setting	Optical Profilometry Setup
Samples measured	Yellow and blue bouldering grip samples
Optical pen	PS4-MG35
Z-range	3000 µm
Scan area	5.00 mm × 5.00 mm
X-step size	4.00 µm
Y-step size	4.00 µm
Averaging	1
Measurement type	Direct
Acquisition mode	Dual frequency
Acquisition rate	100–400 Hz
Light intensity	100%

Optical profilometry test conditions used to measure the bouldering grip samples.

Optical Profilometry Results

Yellow Grip Sample

Surface Roughness Analysis

The 3D rendering below shows the reconstructed surface topography of the yellow climbing grip sample.

A total least-squares plane was removed to study surface properties. The roughness filters S-Gaussian 2.5 µm was applied following ISO 25178 (1/2 cut-off removed at each side). However, the sharp density of pores and asperities and the elevated average roughness make the use of a Gaussian L-filter (8 mm cut off) inapplicable. Therefore, the primary surface was considered, and the roughness parameters are listed in the table below, alongside the 2D false-color map of the filtered surface.

ISO 25178-2 – Primary Surface
S-filter (λs): Gaussian, 2.5 µm, 1/2 cut-off
F-operation: [Workflow] Leveled (TLSPL)
Height Parameters
Sq	168.970	µm	Root-mean-square height
Ssk	-0.927		Skewness
Sku	4.117		Kurtosis
Sp	320.530	µm	Maximum peak height
Sv	868.116	µm	Maximum pit depth
Sz	1188.645	µm	Maximum height
Sa	132.953	µm	Arithmetic mean height

The average surface roughness Sa is 132.953 µm, whereas the peak-to-valley roughness, Sz amounts to 1188.645 µm. The surface morphology is skewed towards deep valleys (Ssk < 0, Sv > Sp), with a leptokurtotic (Sku > 3) distribution of peaks and valleys relative to the average plane.

The following picture shows a 2D photo-simulation of the area under artificial lighting, highlighting the region’s morphology.

Pore Morphology Analysis

A pore analysis was performed across the full scanned area using a semi-automated edge-detection algorithm. The analysis identified recessed surface features to quantify pore coverage, pore density, radius, void volume, and maximum depth.

The detected pore locations were then mapped across the scanned 5 mm × 5 mm area to evaluate pore coverage, density, and size distribution.

Information
Method	Circle detection
Features detected	Pores, recessed objects
Minimum detection diameter	0.150 mm
Maximum detection diameter	2.000 mm
Number of detected pores	206
Surface coverage	47.395%
Pore density	8.203 particles/mm²

Parameter	Unit	Mean	Std. Dev.	Min	Max
Global Statistics
Radius	mm	0.127	0.049	0.076	0.275
Void volume	µm³	4,724,770.705	6,748,143.925	23,594.172	4.422 × 10⁷
Maximum depth	µm	173.729	94.942	28.153	716.480

Pores covered nearly half of the yellow grip’s scanned surface, with a measured coverage of 47.395% and a pore density of 8.203 particles/mm². The detected pores and cracks were highly heterogeneous in size, volume, and depth, ranging from large crater-like features with a maximum radius of 0.275 mm and void volume above 4.4 × 10⁷ µm³ to smaller pores with a minimum radius of 0.076 mm and void volume of 23,594.172 µm³. This uneven pore distribution is reflected in the large standard deviation measured for void volume and maximum depth.

Functional Surface Parameters (Abbott-Firestone curve)

The Abbott-Firestone curve shows the cumulative areal material distribution of the yellow climbing grip sample. This analysis defines functional surface parameters including Sk, Spk, and Svk according to ISO 25178-2.

Information
Standard	ISO 25178-2
Parameter	Value	Unit
Sk	409.738	µm
Spk	45.480	µm
Svk	233.446	µm
Smrk1	3.976	%
Smrk2	85.005	%

The chart below shows the peak-valley distribution from the mean plane based on the functional parameters derived from the Abbott-Firestone curve. Valleys are shown in purple, the mean plane in green, and peaks in orange.

Information
1st threshold	Height – c1: 229.209 µm
2nd threshold	Height – c2: -180.424 µm
Parameters	Unit
Projected area (in %)	%	14.995	81.029	3.976
Projected area	mm²	3.772	20.381	1.000
Volume of material (in %)	%	97.451	48.100	0.973
Volume of material	µm³	1.684 × 10¹⁰	4.956 × 10⁹	2.275 × 10⁷

The yellow grip sample shows a dominant mean-plane region with scattered recessed pores and a smaller population of raised peaks. This indicates a surface texture characterized mainly by average-sized pores distributed across the scanned area.

Blue Grip Sample

Surface Roughness Analysis

The 3D rendering below shows the reconstructed surface topography of the blue climbing grip sample.

A total least-squares plane was removed to evaluate the blue grip’s surface properties. An S-Gaussian 2.5 µm roughness filter was applied following ISO 25178, with 1/2 cut-off removed at each side.

Because of the dense pores, asperities, and elevated average roughness, a Gaussian L-filter with an 8 mm cut-off was not applied. The primary surface was used for roughness analysis, with the roughness parameters listed alongside the 2D false-color map of the filtered surface.

ISO 25178-2 – Primary Surface
S-filter (λs): Gaussian, 2.5 µm, 1/2 cut-off
F-operation: [Workflow] Leveled (TLSPL)
Height Parameters
Sq	211.440	µm	Root-mean-square height
Ssk	-0.682		Skewness
Sku	3.672		Kurtosis
Sp	522.404	µm	Maximum peak height
Sv	720.164	µm	Maximum pit depth
Sz	1242.568	µm	Maximum height
Sa	166.719	µm	Arithmetic mean height

The blue grip sample had an average surface roughness, Sa, of 166.719 µm and a peak-to-valley roughness, Sz, of 1242.568 µm. The negative skewness value, Ssk < 0, indicates that the surface morphology is skewed toward deep valleys, while Sv > Sp shows that the maximum pit depth exceeded the maximum peak height.

The kurtosis value, Sku > 3, indicates a leptokurtotic height distribution, meaning the blue grip surface contains sharper or more extreme peaks and valleys relative to the average plane.

The 2D photo simulation below highlights the blue climbing grip’s surface morphology under artificial lighting.

Pore Morphology Analysis

The detected pore locations were mapped across the scanned 5 mm × 5 mm area to evaluate pore coverage, density, and size distribution.

Information
Method	Circle detection
Features detected	Pores, recessed objects
Minimum detection diameter	0.040 mm
Maximum detection diameter	2.000 mm
Number of detected pores	794
Surface coverage	24.208%
Pore density	31.355 particles/mm²

Parameter	Unit	Mean	Std. Dev.	Min	Max
Global Statistics
Radius	mm	0.035	0.035	0.020	0.218
Void volume	µm³	821,872.849	2,495,310.021	11,009.819	2.929 × 10⁷
Maximum depth	µm	476.053	305.830	16.132	1044.045

Pores covered 24.208% of the blue grip’s scanned surface, with a pore density of 31.355 particles/mm². The detected pores and cracks were highly heterogeneous in size, volume, and depth, ranging from large crater-like features with a maximum radius of 0.218 mm and void volume greater than 2.9 × 10⁷ µm³ to small pores with a minimum radius of 0.020 mm and void volume of approximately 1.1 × 10⁴ µm³.

This uneven distribution is reflected in the large standard deviation measured for void volume and maximum depth. The pore distribution is bimodal, with one population of fine, deep pores and another population of larger crater-like valleys.

Functional Surface Parameters (Abbott-Firestone curve)

The Abbott-Firestone curve shows the cumulative areal material distribution of the blue climbing grip sample. This analysis defines functional surface parameters including Sk, Spk, and Svk according to ISO 25178-2.

Information
Standard	ISO 25178-2
Parameter	Value	Unit
Sk	522.359	µm
Spk	117.670	µm
Svk	295.209	µm
Smrk1	6.122	%
Smrk2	87.456	%

Information
1st threshold	Height – c1: 283.646 µm
2nd threshold	Height – c2: -238.619 µm
Parameters	Unit
Projected area (in %)	%	12.544	81.334	6.122
Projected area	mm²	3.182	20.629	1.553
Volume of material (in %)	%	96.079	48.546	1.514
Volume of material	µm³	1.151 × 10¹⁰	6.431 × 10⁹	9.142 × 10⁷

The blue grip sample shows a dominant mean-plane region with fine, deep pores distributed across the surface and localized peak features. Compared with the yellow grip, the blue grip contains a higher projected peak area and a bimodal pore structure, combining fine recessed pores with larger crater-like valleys.

Conclusion

In this application, the NANOVEA JR25 Non-Contact Optical Profiler was used to measure the surface roughness, topography, and pore morphology of yellow and blue bouldering grip samples.

Topographic analysis showed that both grip samples had high surface roughness, with Sa values above 100 µm and Sz values above 1000 µm. Both surfaces also showed an asymmetric height distribution skewed toward valleys, indicating that recessed features played a major role in the measured surface morphology.

The yellow grip sample showed higher pore coverage, with pores covering 47.395% of the scanned surface. Its surface was mainly characterized by average-sized pores distributed across the measured area.

The blue grip sample showed lower pore coverage at 24.208%, but a much higher pore density of 31.355 particles/mm². Its pore distribution was bimodal, with a population of fine, deep pores and a separate population of larger crater-like valleys.

These results show how non-contact 3D optical profilometry can quantify climbing hold surface features that are difficult to evaluate from visual inspection alone, including roughness, pore coverage, pore depth, surface height distribution, and functional topography. The blue grip’s higher porosity and bimodal pore structure make it more likely to absorb moisture and chalk at the contact interface, while its elevated roughness and surface morphology support stable friction for shoe rubber and finger contact. The yellow grip’s lower roughness and flatter profile suggest it is better suited for use as a foothold in slab climbing, where broad surface contact matters more than deep textural engagement.

Frequently Asked Questions About Climbing Hold Surface Roughness

What is climbing hold surface roughness?

Climbing hold surface roughness describes the height variation, texture, pores, asperities, and valleys present on the surface of a climbing grip. These features can influence contact behavior between the hold, shoe rubber, skin, chalk, and moisture.

How can climbing hold surface roughness be measured?

Climbing hold surface roughness can be measured using non-contact 3D optical profilometry. This method reconstructs the surface topography and calculates areal roughness parameters such as Sa, Sz, Sp, Sv, Ssk, and Sku without touching or deforming the sample.

Why use non-contact optical profilometry for climbing hold analysis?

Non-contact optical profilometry is useful for climbing hold analysis because climbing grips can contain deep pores, sharp valleys, rough asperities, and irregular surface texture. A contact stylus may lose contact, fail to reach recessed features, or introduce artifacts on complex surfaces.

What does Sa mean in surface roughness analysis?

Sa is the arithmetic mean height of a surface and is commonly used to describe average areal surface roughness. In this app note, both climbing grip samples showed high Sa values above 100 µm, indicating strongly textured surfaces.

What does Sz mean in optical profilometry results?

Sz is the maximum height of the measured surface, calculated from the highest peak to the deepest valley. In climbing hold surface roughness analysis, Sz helps describe the full vertical range of the grip’s surface texture.

Why is pore morphology important for climbing grips?

Pore morphology can affect how a climbing grip interacts with chalk, sweat, humidity, skin, and shoe rubber. Measuring pore coverage, density, depth, and volume helps quantify surface features that are difficult to evaluate by visual inspection alone.

Need Reliable Surface Roughness Analysis?

Stent Coating Adhesion and Delamination Analysis Using Nano Scratch Testing

Application Note | Stent Coating Adhesion Testing

Stent Coating Adhesion and Delamination Analysis Using Nano Scratch Testing

Quantifying Coating Failure and Adhesion Performance on Drug-Eluting Stents

Research & Experimental Testing

Duanjie Li, PhD

Visual Design & Editorial

Andrew Shore

Introduction

Blood is carried through arteries from the heart to the rest of the body. Any weakening or blockage of these vessels can pose significant health risks and may become life-threatening. A stent is a small mesh tube inserted into the lumen of a blood vessel to treat narrowed or weakened arteries. Stent implantation is now a widely used procedure to support the arterial wall and restore blood flowᶦ.

Metal stent mesh geometry illustrating the structural complexity of vascular implant design.

Why coating adhesion matters in drug-eluting stents

Drug-eluting stents represent a major advancement in stent technology. They incorporate a biodegradable, biocompatible polymer coating that enables controlled drug release at the arterial site, helping to inhibit intimal thickening and reduce the risk of restenosisᶦᶦ.

A critical concern in these systems is the delamination of the polymer coating from the metallic stent substrate. This coating carries the drug-eluting layer, and its adhesion directly impacts device performance and reliability.

To improve coating adhesion, stents are often designed with complex geometries. In this study, the polymer coating is located at the bottom of grooves within the stent mesh. This configuration presents a significant challenge for adhesion measurement.

A reliable method is required to quantitatively evaluate the interfacial strength between the polymer coating and the metal substrate. The small diameter of the stent mesh, comparable to a human hair, combined with its three-dimensional geometry, requires:

ultrafine X-Y positioning accuracy
precise control of applied load
accurate depth measurement during testing

ℹ️ Learn more about nanoindentation and scratch testing lab services for coating adhesion and failure analysis.

Measurement Method

Nano scratch testing is performed using the NANOVEA PB1000 Mechanical Tester, in Nano Scratch Mode, to evaluate the cohesive and adhesive strength of the polymer coating on the metal mesh of stent samples.

Controlled scratch measurements are carried out on stent geometries with dimensions comparable to a human hair, enabling precise evaluation of coating adhesion on complex stent structures.

NANOVEA PB1000 Advanced

Mechanical Tester

Test Conditions

1. Regular Stent Samples

The stent is fixed on the sample stage, with a support wire inserted inside the stent tube to ensure stability during nano scratch testing. The NANOVEA Mechanical Tester is used to perform nano scratch measurements using the parameters summarized in Table 1, to evaluate the cohesive and adhesive strength of the polymer coating on the metal substrate.

Parameter	Value
Load type	Progressive
Initial load	0.05 mN
Final load	300 and 100 mN
Sliding speed	0.5 mm/min
Sliding distance	0.5 mm
Indenter geometry	Conical
Indenter material (tip)	Diamond
Indenter tip radius	20 µm
Temperature	24°C (room)

Table 1: Test parameters for nano scratch measurements on regular stent samples

2. Grooved Stent Samples

The SEM image in Fig. 1 shows the cross-section of the stent sample. The stent features a groove with a depth of approximately 30 µm. The polymer coating, with a thickness of 10.8 µm, is located at the bottom of the groove.

Standard 60° conical diamond tips are not sharp enough to reach the bottom of the groove without contacting the sidewalls. Therefore, a sharper 40° conical diamond tip is used in this study (Fig. 2).

Nano scratch measurements are performed using the parameters summarized in Table 2.

Parameter	Value
Load type	Progressive
Initial load	0.1 mN
Final load	300 mN
Loading rate	300 mN/min
Scratch length	0.25 mm
Scratch speed	0.25 mm/min
Indenter geometry	40° cone
Indenter material (tip)	Diamond
Indenter tip radius	5 µm

Table 2: Test parameters for nano scratch measurements on grooved stent samples

Fig. 1: SEM cross-section of a grooved stent showing polymer coating located at the bottom of the groove, highlighting the challenge of coating adhesion measurement in recessed geometries.

Fig. 2: Schematic of a 40° conical diamond tip designed for nano scratch testing inside stent grooves, enabling accurate adhesion measurement without sidewall interference.

Results and Discussion

The stent mesh has a diameter of approximately 100 μm, comparable to a human hair. Precise positioning is therefore critical to ensure the scratch test is performed at the center of the stent mesh. The NANOVEA Mechanical Tester provides X–Y positioning accuracy down to 0.25 μm, enabling accurate test placement under the integrated optical microscope.

1. Regular Stent Samples

Nano scratch testing is performed with a progressively increasing load up to 300 mN. The full scratch track on the stent is shown in Fig. 3a, while failure behavior at different stages is presented in Fig. 3b and 3c.

Two critical loads are identified:

Lc1: the load at which the first visible damage appears on the coating
Lc2: the load at which the coating is fully removed and the substrate is exposed

The evolution of coefficient of friction (COF) and penetration depth is shown in Fig. 4, providing insight into the progression of coating failure during the test.

The first signs of coating damage appear at Lc1 ≈ 14.5 mN. As the applied load increases, the diamond tip progressively penetrates the polymer coating, resulting in a wider and deeper scratch track. During this phase, the COF increases from approximately 0.05 to 0.7.

At Lc2 ≈ 78.1 mN, the coating is fully delaminated from the metal substrate. Beyond this point, as the load continues to increase, both COF and penetration depth remain relatively stable due to the mechanical support of the underlying metal substrate.

(a) Full Scratch Track

(b) Lc1 ≈ 14.5 mN

Fig. 3: Nano scratch track on a stent coating under progressively increasing load, showing (a) full scratch path, (b) initial coating failure at Lc1 ≈ 14.5 mN, and (c) complete coating delamination at Lc2 ≈ 78.1 mN.

Fig. 4: Evolution of coefficient of friction (COF) and penetration depth during nano scratch testing of a stent coating under progressively increasing load, showing the progression of coating failure and transition to substrate support.

Failures during nano scratch testing up to a maximum load of 300 mN occur at critical loads below 100 mN. To enable a more quantitative comparison of coating performance, additional tests are performed with a maximum load of 100 mN on two stent samples, referred to as Sample 1 and Sample 2.

Fig. 5 compares the scratch tracks of Sample 1 and Sample 2 after nano scratch testing. Sample 1 exhibits the first sign of coating damage at a critical load of Lc1 ≈ 13.2 mN, while Sample 2 shows initial failure at a higher load of Lc1 ≈ 21.1 mN.

Coating delamination occurs at 62.5 mN for Sample 1. In contrast, the coating on Sample 2 remains intact throughout the test, continuing to protect the metal substrate under the same loading conditions.

This behavior is further reflected in the evolution of coefficient of friction (COF) and penetration depth, as shown in Fig. 6. When the diamond tip penetrates through the coating and contacts the metal substrate in Sample 1, the COF reaches a peak while the penetration depth decreases due to the increased stiffness of the underlying substrate.

(a) Sample 1 – Early Coating Failure

(b) Sample 2 – Improved Coating Integrity

Fig. 5: Comparison of nano scratch tracks for two stent coatings, showing (a) early coating failure and delamination in Sample 1, and (b) improved coating integrity in Sample 2 under the same loading conditions.

Fig. 6: Comparison of coefficient of friction (COF) and penetration depth for Sample 1 and Sample 2 during nano scratch testing, showing earlier substrate contact and higher friction response in Sample 1, indicating weaker coating adhesion.

2. Grooved Stent Samples

As shown in Fig. 1 and Fig. 7, the grooved stent mesh has a diameter of approximately 90 μm, comparable to a human hair. The groove has a width of ~50 μm and a depth of 30 μm. This geometry presents a significant challenge for nano scratch testing, particularly for evaluating coating adhesion at the bottom of the groove.

Precise positioning is critical to locate the scratch test within the groove. The nano scratch test is performed with a progressively increasing load up to 300 mN. The full scratch tracks of grooved stent Samples 3 and 4 are compared in Fig. 7.

The critical load Lc is defined as the load at which the coating fails and the substrate becomes exposed. The evolution of normal load and penetration depth, shown in Fig. 8, provides further insight into the progression of coating failure during testing.

As the applied load increases, the diamond tip progressively penetrates the polymer coating, resulting in a deeper scratch track. When the critical load Lc is reached, the coating delaminates from the metal substrate.

Sample 3 exhibits coating failure at Lc ≈ 126 mN, while Sample 4 fails at a higher load of Lc ≈ 173 mN. This difference indicates stronger adhesion of the coating in Sample 4.

The measured critical loads enable quantitative comparison of coating adhesion performance. Under the same testing conditions, the coating on Sample 4 demonstrates higher resistance to delamination, making it the better-performing candidate in this study.

(d) Sample 4 – Higher Adhesion in Groove (Lc ≈ 173 mN)

Fig. 7: Nano scratch tracks inside stent grooves for Samples 3 and 4, showing (c) coating failure at Lc ≈ 126 mN in Sample 3 and (d) higher adhesion with delayed failure at Lc ≈ 173 mN in Sample 4.

(a) Sample 3 – Earlier Coating Failure (Lc ≈ 126 mN)

(b) Sample 4 – Delayed Failure and Higher Adhesion (Lc ≈ 173 mN)

Fig. 8: Evolution of normal load and penetration depth during nano scratch testing inside stent grooves for Samples 3 and 4, showing earlier coating failure in Sample 3 and delayed failure at higher load in Sample 4. The vertical green line indicates the critical load (Lc) where coating delamination occurs.

Conclusion

This study demonstrates the ability of the NANOVEA Mechanical Tester to quantitatively evaluate the cohesive and adhesive strength of polymer coatings on both regular and grooved stent geometries using nano scratch testing.

The recessed geometry of the stent grooves, approximately 50 μm wide and 30 μm deep, presents a significant challenge for coating adhesion measurement. The high X–Y positioning accuracy of 0.25 μm enables precise placement of the scratch test within these confined regions, allowing direct evaluation of coating performance where failure is most critical.

By applying a controlled, progressively increasing load, critical loads associated with coating failure can be identified and compared across samples. This approach enables reliable differentiation of coating adhesion performance and interfacial integrity, even on small, complex stent structures.

References

[I] http://www.nhlbi.nih.gov/health/health-topics/topics/stents
[II] http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-99402006000300008

Frequently Asked Questions About Stent Coating Adhesion Testing

What is stent coating adhesion testing?

Stent coating adhesion testing evaluates how strongly a polymer coating is bonded to the metal substrate of a stent. Techniques such as nano scratch testing quantify the load at which coating damage and delamination occur, providing measurable indicators of adhesion strength.

What is critical load (Lc) in nano scratch testing?

Critical load (Lc) is the applied load at which a coating fails during a scratch test.

Lc1 corresponds to the first visible damage in the coating
Lc2 indicates complete coating removal and exposure of the substrate

These values are used to quantify and compare coating adhesion performance.

Why is coating adhesion important in drug-eluting stents?

Coating adhesion directly affects the reliability of drug-eluting stents. Poor adhesion can lead to coating delamination, which may compromise controlled drug release and increase the risk of device failure.

How do you measure coating adhesion inside stent grooves?

Measuring adhesion inside stent grooves requires high positioning accuracy and appropriate indenter geometry. Nano scratch testing with sharp diamond tips allows access to recessed coating regions, enabling direct evaluation of adhesion within complex stent geometries.

What does coefficient of friction (COF) indicate in scratch testing?

The coefficient of friction (COF) reflects changes in surface interaction during the scratch test. A sudden increase in COF often indicates coating failure and contact between the indenter and the underlying metal substrate.

How can nano scratch testing compare different coating formulations?

Nano scratch testing enables direct comparison of coatings by measuring critical loads under controlled conditions. Higher critical loads indicate stronger adhesion and improved resistance to delamination, allowing selection of better-performing coating systems.

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Dental Surface Roughness Measurement & 3D Tooth Topography

Application Note | Dental Surface Characterization

Dental Surface Roughness Measurement and Full 3D Tooth Topography

Surface Roughness Analysis Using Non-Contact Optical Profilometry

Prepared by

Walter Alabiso, PhD; Davide Morrone, MPhys; Andrew Shore, MA

Introduction

The ability to accurately characterize tooth surfaces, including micro-roughness and 3D surface topography at the nanometer scale, enables advanced research and applications in orthodontics and dental materials science. Non-contact optical profilometry provides a precise method for measuring dental surface roughness and analyzing tooth surface morphology without damaging delicate structures. These measurements support the development of composite dental materials that replicate the natural surface roughness of enamel, as well as the design and fabrication of patient-specific dental casts and restorative components.

Low surface roughness plays a primary role in limiting bacterial adhesion and plaque formation, thereby reducing the risk of cavities. An increase in average roughness (Ra) above 2 µm leads to a steep increase in biofilm formation in vivo.¹ An Ra of 0.2 µm is considered the threshold value below which no further reduction in bacterial adhesion can be expected.²

Reconstruction of the tooth’s 3D surface topography enables the fabrication of dental casts, which are essential for accurate diagnosis, treatment planning, and the fabrication of dental appliances.

Non-Contact Optical Profilometry for Dental Surface Analysis

The present study illustrates the potential of NANOVEA’s high-precision non-contact optical profilometers for dental surface roughness measurement and 3D tooth topography analysis. Chromatic Light technology offers significant advantages over classical touch probe techniques. It acquires data points from deep crevices and complex geometries without introducing measurement errors or artifacts caused by local plastic deformation and without requiring extensive data manipulation.

Compared to focus variation systems, single-point optical sensing provides superior lateral and height accuracy, with X/Y resolution below 0.5 µm, maximum vertical resolution of 1.9 nm, and the ability to measure surface angles up to 87°. The technique is effective on transparent, opaque, specular, diffusive, polished, and rough dental surfaces, making it well suited for comprehensive dental surface characterization.

ℹ️ Learn more about non-contact optical profilometry and surface roughness measurement services.

Measurement Method

In this application, the NANOVEA JR25 Non-Contact Optical Profiler was used to analyze the surface roughness and 3D surface topography of an adult human molar previously affected by tooth decay. The side of the tooth was scanned using a PS2–MG140 single-point optical sensor to measure surface roughness parameters over a defined region of interest and along multiple line profiles.

The crown of the tooth was then scanned and reconstructed using a PS5–MG35 single-point optical sensor, which is suited for larger-area acquisition and full 3D tooth topography measurement.

NANOVEA JR25 Portable
Optical Profilometer

Surface Measurement Using NANOVEA Optical Profilometer

Surface roughness measurements were performed on the lateral side of the molar crown, followed by full 3D reconstruction of the crown surface. Separate single-point optical sensors were used to optimize measurement accuracy for both localized roughness analysis and large-area surface topography acquisition.

PS2 – MG140

Surface roughness analysis by area and parallel line profiles on the side of the tooth’s crown.

PS5 – MG35

Full 3D surface reconstruction of the tooth’s crown.

Measurement Parameters

The following measurement parameters were used for localized surface roughness analysis and full 3D surface reconstruction of the molar crown using NANOVEA single-point optical sensors.

Parameter	Roughness Analysis (Area)	Roughness Analysis (Profiles)	Full 3D Reconstruction
Optical Pen	PS2-MG140	PS2-MG140	PS5-MG35
Z-Range [µm]	300	300	10000
X-Distance [mm]	2.00	3.00	7.50
X-Step Size [µm]	1.70	1.70	10.00
Y-Distance [mm]	2.00	1.00	7.00
Y-Step Size [µm]	1.70	100.00	10.00
Averaging (Avg)	1	1	1
Measurement Type	Direct	Direct	Direct
Acquisition Mode	Single Frequency	Single Frequency	Dual Frequency
Acquisition Rate [Hz]	200	200	100–400
Light Intensity [%]	100	100	100

Optical Profilometry Results

Yellow Grip Sample

Surface Roughness Analysis

The image below shows a full 3D rendering of the yellow block’s surface.

A least-squares degree-8 polynomial form removal was applied to isolate the surface roughness component. The roughness filters S-Gaussian 2.5 µm and L-Gaussian 0.8 mm were then applied according to ISO 25178. The resulting filtered surface and corresponding roughness parameters are presented below.

ISO 25178 – Roughness (S-L)
S-filter (λs): Gaussian, 2.5 µm
F: [Workflow] Form removed (LS-poly 8)
L-filter (λc): Gaussian, 0.8 mm
Height Parameters
Sq	2.433	µm	Root-mean-square height
Ssk	-0.102		Skewness
Sku	3.715		Kurtosis
Sp	18.861	µm	Maximum peak height
Sv	16.553	µm	Maximum pit depth
Sz	35.414	µm	Maximum height
Sa	1.888	µm	Arithmetic mean height

The average surface roughness Sa is 1.888 µm, while the peak-to-valley height Sz reaches 35.414 µm.

A 3D surface rendering of the filtered area is shown below for visualization.

Roughness Analysis (Profiles)

Surface roughness profiles were measured using a series of 11 parallel line scans along the X direction on the side of the tooth. The false-color 2D surface map of the raw scan is shown below.

The surface form was removed using a least-squares 8-degree polynomial prior to applying the metrological filters, leaving the residual surface shown below.

A statistical analysis of the measured surface roughness profiles reveals the following line roughness parameters.

		Description	Mean	Std dev	Min	Max
ISO 4287 – Roughness (S-L)
F: None
S-filter (λs): Gaussian, 2.5 µm
L-filter (λc): Gaussian, 0.8 mm
Evaluation length: All λc (3)
Amplitude Parameters – Roughness Profile
Rp	µm	Maximum peak height of the roughness profile	5.683	0.761	4.315	6.610
Rv	µm	Maximum valley depth of the roughness profile	6.242	1.009	4.701	8.438
Rz	µm	Maximum height of roughness profile	11.925	1.676	9.123	15.048
Ra	µm	Arithmetic mean deviation of the roughness profile	2.063	0.297	1.710	2.629
Rq	µm	Root-mean-square (RMS) deviation of the roughness profile	2.523	0.361	2.057	3.175

F: None

S-filter (λs): Gaussian, 2.5 µm

L-filter (λc): Gaussian, 0.8 mm

Evaluation length: All λc (3)

The value of Ra is consistent with the Sa value extracted from the surface area measurement.

Different metrological filters can be applied to distinguish between macroscopic waviness and microscopic surface roughness. For example, a coarser filter cut-off, such as the 8 mm cut-off used with the Robust Gaussian order-2 filter, produces a smoother waviness profile (red) that is less sensitive to sharp local variations and follows the original surface profile more loosely.

Alternatively, a finer cut-off (e.g., 0.08 mm) enables the analysis of micro-roughness by removing the waviness component that follows the original profile at a larger scale, leaving the finer surface roughness features of the tooth visible.

The microroughness analysis obtained using a 0.08 mm L-Gaussian filter is presented below.

		Description	Mean	Std dev	Min	Max
ISO 4287 – Roughness (S-L)
F: None
S-filter (λs): Gaussian, 2.5 µm
L-filter (λc): Gaussian, 0.08 mm
Evaluation length: All λc (37)
Amplitude Parameters – Roughness Profile
Rp	µm	Maximum peak height of the roughness profile	1.582	0.122	1.342	1.748
Rv	µm	Maximum valley depth of the roughness profile	1.466	0.119	1.254	1.661
Rz	µm	Maximum height of roughness profile	3.049	0.196	2.820	3.409
Ra	µm	Arithmetic mean deviation of the roughness profile	0.495	0.047	0.423	0.597
Rq	µm	Root-mean-square (RMS) deviation of the roughness profile	0.643	0.056	0.562	0.762

F: None

S-filter (λs): Gaussian, 2.5 µm

L-filter (λc): Gaussian, 0.8 mm

Evaluation length: All λc (3)

Full 3D Tooth Surface Topography Reconstruction

The extended Z-scan range of the PS5 optical sensor enables high-fidelity scanning of the entire tooth crown surface. The resulting 3D surface topography is shown below.

2D VIEW: 2D surface map of the tooth crown measured with optical profilometry

3D VIEW: High-fidelity 3D rendering of the molar crown surface obtained with optical profilometry

Conclusion

In this application, the NANOVEA JR25 Non-Contact Optical Profiler was used to measure the surface roughness and 3D surface topography of an adult human molar.

Both the area scan and the line profile analysis revealed a roughness Rq of approximately 2.5 µm and an Ra of about 1.9–2.0 µm. These values are consistent with results reported in the literature.³ The use of a narrower L-Gaussian filter with an 80 µm cut-off enabled further investigation of micro-roughness, revealing an Rq of 0.643 µm and an Ra of 0.495 µm.

The full 3D surface topography of the molar crown was reconstructed with high fidelity. The high measurement resolution allows detection of fine surface features and crevices. The resulting surface data can be easily processed and exported as STL files, enabling the design and fabrication of customized dental devices and restorative components.

References

[1] Shin, B.W., et al. Surface Roughness of Prefabricated Pediatric Zirconia Crowns Following Simulated Toothbrushing. Pediatric Dentistry 44.5 (2022): 363–367.
[2] Bollen, C.M.L., Paul Lambrechts, and Marc Quirynen. Comparison of surface roughness of oral hard materials to the threshold surface roughness for bacterial plaque retention: A review of the literature. Dental Materials 13.4 (1997): 258–269.
[3] Suputtamongkol, K., et al. Surface roughness resulting from wear of lithia-disilicate-based posterior crowns. Wear 269.3–4 (2010): 317–322.

Frequently Asked Questions About Dental Surface Roughness Measurement

What is dental surface roughness measurement?

Dental surface roughness measurement quantifies the microscopic texture of tooth surfaces using parameters such as Ra, Rq, and Sa. Optical profilometers measure these features without contacting the surface, allowing accurate analysis of enamel, restorative materials, and dental crowns.

Why use optical profilometry to measure tooth roughness?

Optical profilometry provides non-contact surface measurement with nanometer-scale vertical resolution. It captures 2D surface maps and full 3D surface topography of dental structures without damaging soft or polished surfaces.

What roughness parameters are used for dental surface analysis?

Common roughness parameters include Ra (arithmetic mean roughness), Rq (root mean square roughness), Sa (areal roughness), and Sz (maximum surface height). These parameters help evaluate enamel wear, plaque adhesion risk, and the performance of restorative materials.

Why is surface roughness important in dentistry?

Surface roughness affects plaque retention, wear resistance, and the long-term performance of dental restorations. Controlling micro-roughness can reduce bacterial adhesion and improve the durability of dental materials.

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Weld Surface Inspection Using a Portable 3D Profilometer

WELd surface inspection

using a portable 3d profilometer

Prepared by

CRAIG LEISING

INTRODUCTION

It may become critical for a particular weld, typically done by visual inspection, to be investigated with an extreme level of precision. Specific areas of interest for precise analysis include surface cracks, porosity and unfilled craters, regardless of subsequent inspection procedures. Weld characteristics such as dimension/shape, volume, roughness, size etc. can all be measured for critical evaluation.

IMPORTANCE OF 3D NON-CONTACT PROFILOMETER FOR WELD SURFACE INSPECTION

Unlike other techniques such as touch probes or interferometry, the NANOVEA 3D Non-Contact Profilometer, using axial chromatism, can measure nearly any surface, sample sizes can vary widely due to open staging and there is no sample preparation needed. Nano through macro range is obtained during surface profile measurement with zero influence from sample reflectivity or absorption, has advanced ability to measure high surface angles and there is no software manipulation of results. Easily measure any material: transparent, opaque, specular, diffusive, polished, rough etc. The 2D and 2D capabilities of the NANOVEA Portable Profilometers make them ideal instruments for full complete weld surface inspection both in the lab and in the field.

MEASUREMENT OBJECTIVE

In this application, the NANOVEA JR25 Portable Profiler is used to measure the surface roughness, shape and volume of a weld, as well as the surrounding area. This information can provide critical information to properly investigate the quality of the weld and weld process.

NANOVEA

JR25

TEST RESULTS

The image below shows the full 3D view of the weld and the surrounding area along with the surface parameters of the weld only. The 2D cross section profile is shown below.

the sample

With the above 2D cross section profile removed from the 3D, dimensional information of the weld is calculated below. Surface area and volume of material calculated for the weld only below.

	HOLE	PEAK
SURFACE	1.01 mm²	14.0 mm²
VOLUME	8.799e-5 mm³	23.27 mm³
MAX DEPTH/HEIGHT	0.0276 mm	0.6195 mm
MEAN DEPTH/HEIGHT	0.004024 mm	0.2298 mm

CONCLUSION

In this application, we have shown how the NANOVEA 3D Non-Contact Profiler can precisely characterize critical characteristics of a weld and the surrounding surface area. From the roughness, dimensions and volume, a quantitative method for quality and repeatability can be determined and or further investigated. Sample welds, such as the example in this app note, can be easily analyzed, with a standard tabletop or portable NANOVEA Profiler for in-house or field testing

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Fractography Analysis Using 3D Profilometry

FRACTOGRAPHY ANALYSIS

USING 3D PROFILOMETRY

Prepared by

CRAIG LEISING

INTRODUCTION

Fractography is the study of features on fractured surfaces and has historically been investigated via Microscope or SEM. Depending on the size of the feature, a microscope (macro features) or SEM (nano and micro features) are selected for the surface analysis. Both ultimately allowing for the identification of the fracture mechanism type. Although effective, the Microscope has clear limitations and the SEM in most cases, other than atomic-level analysis, is unpractical for fracture surface measurement and lacks broader use capability. With advances in optical measurement technology, the NANOVEA 3D Non-Contact Profilometer is now considered the instrument of choice, with its ability to provide nano through macro-scale 2D & 3D surface measurements

IMPORTANCE OF 3D NON-CONTACT PROFILOMETER FOR FRACTURE INSPECTION

Unlike an SEM, a 3D Non-Contact Profilometer can measure nearly any surface, sample size, with minimal sample prep, all while offering superior vertical/horizontal dimensions to that of an SEM. With a profiler, nano through macro range features are captured in a single measurement with zero influence from sample reflectivity. Easily measure any material: transparent, opaque, specular, diffusive, polished, rough etc. The 3D Non-Contact Profilometer provides broad and user-friendly capability to maximize surface fracture studies at a fraction of the cost of an SEM.

MEASUREMENT OBJECTIVE

In this application, the NANOVEA ST400 is used to measure the fractured surface of a steel sample. In this study, we will showcase a 3D area, 2D profile extraction and surface directional map of the surface.

NANOVEA

ST400

RESULTS

TOP SURFACE

3D Surface Texture Direction

Isotropy	51.26%
First Direction	123.2º
Second Direction	116.3º
Third Direction	0.1725º

Surface Area, Volume, Roughness and many others can be automatically calculated from this extraction.

2D Profile Extraction

RESULTS

SIDE SURFACE

3D Surface Texture Direction

Isotropy	15.55%
First Direction	0.1617º
Second Direction	110.5º
Third Direction	171.5º

Surface Area, Volume, Roughness and many others can be automatically calculated from this extraction.

2D Profile Extraction

CONCLUSION

In this application, we have shown how the NANOVEA ST400 3D Non-Contact Profilometer can precisely characterize the full topography (nano, micro and macro features) of a fractured surface. From the 3D area, the surface can be clearly identified and subareas or profiles/cross-sections can be quickly extracted and analyzed with an endless list of surface calculations. Sub nanometer surface features can be further analyzed with an integrated AFM module.

Additionally, NANOVEA has included a portable version to their Profilometer line-up, especially critical for field studies where a fracture surface is immovable. With this broad list of surface measurement capabilities, fracture surface analysis has never been easier and more convenient with a single instrument.

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Polymer Belt Wear and Friction using a Tribometer

POLYMER BELTS

WEAR AND FRICTION USING a TRIBOMETER

Prepared by

DUANJIE LI, PhD

INTRODUCTION

Belt drive transmits power and tracks relative movement between two or more rotating shafts. As a simple and inexpensive solution with minimal maintenance, belt drives are widely used in a variety of applications, such as bucksaws, sawmills, threshers, silo blowers and conveyors. Belt drives can protect the machinery from overload as well as damp and isolate vibration.

IMPORTANCE OF WEAR EVALUATION FOR BELT DRIVES

Friction and wear are inevitable for the belts in a belt-driven machine. Sufficient friction ensures effective power transmission without slipping, but excessive friction may rapidly wear the belt. Different types of wear such as fatigue, abrasion and friction take place during the belt drive operation. In order to extend the lifetime of the belt and to cut the cost and time on belt repairing and replacement, reliable evaluation of the wear performance of the belts is desirable in improving belt lifespan, production efficiency and application performance. Accurate measurement of the coefficient of friction and wear rate of the belt facilitates R&D and quality control of belt production.

MEASUREMENT OBJECTIVE

In this study, we simulated and compared the wear behaviors of belts with different surface textures to showcase the capacity of the NANOVEA T2000 Tribometer in simulating the wear process of the belt in a controlled and monitored manner.

NANOVEA

T2000

TEST PROCEDURES

The coefficient of friction, COF, and the wear resistance of two belts with different surface roughness and texture were evaluated by the NANOVEA High-Load Tribometer using Linear Reciprocating Wear Module. A Steel 440 ball (10 mm diameter) was used as the counter material. The surface roughness and wear track were examined using an integrated 3D Non-Contact profilometer. The wear rate, K, was evaluated using the formula K=Vl(Fxs), where V is the worn volume, F is the normal load and s is the sliding distance.

Please note that a smooth Steel 440 ball counterpart was used as an example in this study, any solid material with different shapes and surface finish can be applied using custom fixtures to simulate the actual application situation.

RESULTS & DISCUSSION

The Textured Belt and Smooth Belt have a surface roughness Ra of 33.5 and 8.7 um, respectively, according to the analyzed surface profiles taken with a NANOVEA 3D Non-Contact Optical profiler. The COF and wear rate of the two tested belts were measured at 10 N and 100 N, respectively, to compare the wear behavior of the belts at different loads.

FIGURE 1 shows the evolution of COF of the belts during the wear tests. The belts with different textures exhibit substantially different wear behaviors. It is interesting that after the run-in period during which the COF progressively increases, the Textured Belt reaches a lower COF of ~0.5 in both the tests conducted using loads of 10 N and 100 N. In comparison, the Smooth Belt tested under the load of 10 N exhibits a significantly higher COF of~ 1.4 when the COF gets stable and maintains above this value for the rest of the test. The Smooth Belt tested under the load of 100 N rapidly was worn out by the steel 440 ball and formed a large wear track. The test was therefore stopped at 220 revolutions.

FIGURE 1: Evolution of COF of the belts at different loads.

FIGURE 2 compares the 3D wear track images after the tests at 100 N. The NANOVEA 3D non-contact profilometer offers a tool to analyze the detailed morphology of the wear tracks, providing more insight in fundamental understanding of wear mechanism.

TABLE 1: Result of wear track analysis.

FIGURE 2: 3D view of the two belts
after the tests at 100 N.

The 3D wear track profile allows direct and accurate determination of the wear track volume calculated by the advanced analysis software as shown in TABLE 1. In a wear test for 220 revolutions, the Smooth Belt has a much larger and deeper wear track with a volume of 75.7 mm3, compared to a wear volume of 14.0 mm3 for the Textured Belt after a 600-revolution wear test. The significantly higher friction of the Smooth Belt against the steel ball leads to a 15 fold higher wear rate compared to the Textured Belt.

Such a drastic difference of COF between the Textured Belt and Smooth Belt is possibly related to the size of the contact area between the belt and the steel ball, which also leads to their different wear performance. FIGURE 3 shows the wear tracks of the two belts under the optical microscope. The wear track examination is in agreement with the observation on COF evolution: The Textured Belt, which maintains a low COF of ~0.5, exhibits no sign of wear after the wear test under a load of 10 N. The Smooth Belt shows a small wear track at 10 N. The wear tests carried out at 100 N create substantially larger wear tracks on both the Textured and Smooth Belts, and the wear rate will be calculated using 3D profiles as will be discussed in the following paragraph.

FIGURE 3: Wear tracks under optical microscope.

CONCLUSION

In this study, we showcased the capacity of the NANOVEA T2000 Tribometer in evaluating the coefficient of friction and wear rate of belts in a well-controlled and quantitative manner. The surface texture plays a critical role in the friction and wear resistance of the belts during their service performance. The textured belt exhibits a stable coefficient of friction of ~0.5 and possesses a long lifetime, which results in reduced time and cost on tool repairing or replacement. In comparison, the excessive friction of the smooth belt against the steel ball rapidly wears the belt. Further, the loading on the belt is a vital factor of its service lifetime. Overload creates very high friction, leading to accelerated wear to the belt.

The NANOVEA T2000 Tribometer offers precise and repeatable wear and friction testing using ISO and ASTM compliant rotative and linear modes, with optional high temperature wear, lubrication and tribocorrosion modules available in one pre-integrated system. NANOVEA’s unmatched range is an ideal solution for determining the full range of tribological properties of thin or thick, soft or hard coatings, films and substrates.

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Fossil Microstructure Using 3D Profilometry

FOSSIL MICROSTRUCTURE

USING 3D PROFILOMETRY

Prepared by

DUANJIE LI, PhD

INTRODUCTION

Fossils are the preserved remains of traces of plants, animals and other organisms buried in sediment under ancient seas, lakes and rivers. The soft body tissue usually decays after death, but the hard shells, bones and teeth fossilize. Microstructure surface features are often preserved when mineral replacement of the original shells and bones takes place, which provides an insight into the evolution of weather and the formation mechanism of fossils.

IMPORTANCE OF A 3D NON-CONTACT PROFILOMETER FOR FOSSIL EXAMINATION

3D profiles of the fossil enable us to observe the detailed surface features of the fossil sample from a closer angle. The high resolution and accuracy of the NANOVEA profilometer may not be discernible by the naked eye. The profilometer’s analysis software offers a wide range of studies applicable to these unique surfaces. Unlike other techniques such as touch probes, the NANOVEA 3D Non-Contact Profilometer measures the surface features without touching the sample. This allows for the preservation of the true surface features of certain delicate fossil samples. Moreover, the portable model Jr25 profilometer enables 3D measurement on fossil sites, which substantially facilitates fossil analysis and protection after excavation.

MEASUREMENT OBJECTIVE

In this study, the NANOVEA Jr25 Profilometer is used to measure the surface of two representative fossil samples. The entire surface of each fossil was scanned and analyzed in order to characterize its surface features which include roughness, contour and texture direction.

NANOVEA

Jr25

BRACHIOPOD FOSSIL

The first fossil sample presented in this report is a Brachiopod fossil, which came from a marine animal that has hard “valves” (shells) on its upper and lower surfaces. They first appeared in the Cambrian period, which is more than 550 million years ago.

The 3D View of the scan is shown in FIGURE 1 and False Color View is shown in FIGURE 2.

FIGURE 1: 3D View of the Brachiopod fossil sample.

FIGURE 2: False Color View of the Brachiopod fossil sample.

The overall form was then removed from the surface in order to investigate the local surface morphology and contour of the Brachiopod fossil as shown in FIGURE 3. A peculiar divergent groove texture can now be observed on the Brachiopod fossil sample.

FIGURE 3: False Color View and Contour Lines View after form removal.

A line profile is extracted from the textured area to show a crossectional view of the fossil surface in FIGURE 4. The Step Height study measures precise dimensions of the surface features. The grooves possess an average width of ~0.38 mm and depth of ~0.25 mm.

FIGURE 4: Line profile and Step Height studies of the textured surface.

CRINOID STEM FOSSIL

The second fossil sample is a Crinoid stem fossil. Crinoids first appeared in the seas of the Middle Cambrian Period, about 300 million years before dinosaurs.

The 3D View of the scan is shown in FIGURE 5 and False Color View is shown in FIGURE 6.

FIGURE 5: 3D View of the Crinoid fossil sample.

The surface texture isotropy and roughness of the Crinoid stem fossil are analyzed in FIGURE 7.

This fossil has a preferential texture direction in the angle close to 90°, leading to texture isotropy of 69%.

FIGURE 6: False Color View of the Crinoid stem sample.

FIGURE 7: Surface texture isotropy and roughness of the Crinoid stem fossil.

The 2D profile along the axial direction of the Crinoid stem fossil is shown in FIGURE 8.

The size of the peaks of the surface texture is fairly uniform.

FIGURE 8: 2D profile analysis of the Crinoid stem fossil.

CONCLUSION

In this application, we comprehensively studied the 3D surface features of a Brachiopod and Crinoid stem fossil using the NANOVEA Jr25 Portable Non-Contact Profilometer. We showcase that the instrument can precisely characterize the 3D morphology of the fossil samples. The interesting surface features and texture of the samples are then further analyzed. The Brachiopod sample possesses a divergent groove texture, while the Crinoid stem fossil shows preferential texture isotropy. The detailed and precise 3D surface scans prove to be ideal tools for palaeontologists and geologists to study the evolution of lives and the formation of fossils.

The data shown here represent only a portion of the calculations available in the analysis software. NANOVEA Profilometers measure virtually any surface in fields including Semiconductor, Microelectronics, Solar, Fiber Optics, Automotive, Aerospace, Metallurgy, Machining, Coatings, Pharmaceutical, Biomedical, Environmental and many others.

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Surface Boundary Measurement

Surface Boundary Measurement Using 3D Profilometry

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SURFACE BOUNDARY MEASUREMENT

USING 3D PROFILOMETRY

Prepared by

Craig Leising

INTRODUCTION

In studies where the interface of surface features, patterns, shapes etc., are being evaluated for orientation, it will be useful to quickly identify areas of interest over the entire profile of measurement. By segmenting a surface into significant areas the user can quickly evaluate boundaries, peaks, pits, areas, volumes and many others to understand their functional role in the entire surface profile under study. For example, like that of a grain boundary imaging of metals, the importance of analysis is the interface of many structures and their overall orientation. By understanding each area of interest defects and or abnormalities within the overall area can be identified. Although grain boundary imaging is typically studied at a range surpassing Profilometer capability, and is only 2D image analysis, it is a helpful reference to illustrate the concept of what will be shown here on a larger scale along with 3D surface measurement advantages.

IMPORTANCE OF 3D NON CONTACT PROFILOMETER FOR SURFACE SEPARATION STUDY

Unlike other techniques such as touch probes or interferometry, the 3D Non Contact Profilometer, using axial chromatism, can measure nearly any surface, sample sizes can vary widely due to open staging and there is no sample preparation needed. Nano through macro range is obtained during surface profile measurement with zero influence from sample reflectivity or absorption, has advanced ability to measure high surface angles and there is no software manipulation of results. Easily measure any material: transparent, opaque, specular, diffusive, polished, rough etc. The technique of the Non Contact Profilometer provides an ideal, broad and user friendly capability to maximize surface studies when surface boundary analysis will be needed; along with the benefits of combined 2D & 3D capability.

MEASUREMENT OBJECTIVE

In this application the Nanovea ST400 Profilometer is used to measure the surface area of Styrofoam. Boundaries were established by combining a reflected intensity file along with the topography, which are simultaneously acquired using the NANOVEA ST400. This data was then used to calculate different shape and size information of each Styrofoam “grain”.

NANOVEA

ST400

RESULTS & DISCUSSION: 2D Surface Boundary Measurement

Topography image(below left) masked by reflected intensity image(below right) to clearly define grain boundaries. All grains below 565µm diameter have been ignored by applying filter.

Total number of grains: 167
Total projected area occupied by the grains: 166.917 mm² (64.5962 %)
Total projected area occupied by boundaries: (35.4038 %)
Density of grains: 0.646285 grains / mm2

Area = 0.999500 mm² +/- 0.491846 mm²
Perimeter = 9114.15 µm +/- 4570.38 µm
Equivalent diameter = 1098.61 µm +/- 256.235 µm
Mean diameter = 945.373 µm +/- 248.344 µm
Min diameter = 675.898 µm +/- 246.850 µm
Max diameter = 1312.43 µm +/- 295.258 µm

RESULTS & DISCUSSION: 3D Surface Boundary Measurement

By using the 3D topography data obtained, the volume, height, peak, aspect ratio and general shape information can be analyzed on each grain. Total 3D area occupied: 2.525mm3

CONCLUSION

In this application, we have shown how the NANOVEA 3D Non Contact Profilometer can precisely characterize the surface of Styrofoam. Statistical information can be gained over the entire surface of interest or on individual grains, whether they are peaks or pits. In this example all grains larger than a user defined size were used to show the area, perimeter, diameter and height. The features shown here can be critical to research and quality control of natural and pre fabricated surfaces ranging from bio medical to micromachining applications along with many others.

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Tire Tread Depth & Rubber Surface Roughness Measurement | 3D Optical Profiler

TIRE TREAD DEPTH & RUBBER SURFACE ROUGHNESS MEASUREMENT using 3D Optical Profiler

Prepared by

ANDREA HERRMANN

While tire tread depth is commonly measured with handheld gauges for consumer safety, industrial R&D and tire manufacturers require more advanced methods. This application note demonstrates how a 3D optical profilometer provides precise tire tread depth measurement, contour mapping, and rubber surface roughness analysis for high-accuracy studies.

INTRODUCTION

Like all materials, rubber’s coefficient of friction is related in part to its surface roughness. In vehicle tires, both tread depth and surface roughness directly affect traction, braking, and wear performance. In this study, the rubber surface and tread’s roughness and dimensions are analyzed using 3D non-contact profilometry.

THE SAMPLE

IMPORTANCE OF 3D NON-CONTACT PROFILOMETRY FOR TIRE TREAD DEPTH MEASUREMENT

Unlike other techniques such as touch probes or interferometry, NANOVEA’s 3D Non-Contact Optical Profilers use axial chromatism to measure nearly any surface.

The Profiler system’s open staging allows for a wide variety of sample sizes and requires zero sample preparation. With a single scan, users can capture both overall tire tread depth and micro-level surface roughness, with zero influence from sample reflectivity or absorption. Plus, these profilers have the advanced ability to measure high surface angles without requiring software manipulation of results.

This versatility makes NANOVEA profilers ideal for both tire tread wear testing and advanced rubber material research.

MEASUREMENT OBJECTIVE

In this application, we showcase the NANOVEA ST400, a 3D Non-Contact Optical Profiler measuring tire tread depth, contour geometry, and rubber surface roughness. A sample surface area large enough to represent the entire tire surface was selected at random for this study. To quantify the rubber’s characteristics, we used the NANOVEA Ultra 3D analysis software to measure groove dimensions, tread depth, surface roughness, and developed vs. projected area.

NANOVEA ST400 Standard
Optical 3D Profilometer

ANALYSIS: TIRE TREAD

The 3D View and False Color View of the treads show the value of mapping 3D surface designs. This provides engineers with a straightforward tool to evaluate tread depth uniformity, groove design, and wear from multiple angles. The Advanced Contour Analysis and Step Height Analysis are both extremely powerful tools for measuring precise dimensions of sample shapes and design.

ADVANCED CONTOUR ANALYSIS

STEP HEIGHT ANALYSIS

ANALYSIS: RUBBER SURFACE

The rubber surface can be quantified in numerous ways using built-in software tools as shown in the following figures. It can be observed that the surface roughness is 2.688 μm, and the developed area vs. projected area is 9.410 mm² vs. 8.997 mm². These results demonstrate how rubber surface roughness affects traction and performance, enabling comparisons between different rubber formulations or varying levels of surface wear.

CONCLUSION

In this application, we have shown how the NANOVEA 3D Non-Contact Optical Profiler can precisely characterize tire tread depth, contour dimensions, and rubber surface roughness. The data shows a surface roughness of 2.69 µm and a developed area of 9.41 mm² with a projected area of 9 mm². Various dimensions and radii of the rubber treads were measured as well. This information can be used by tire manufacturers, automotive researchers, and materials engineers to compare tread designs, rubber formulations, or tires with varying degrees of wear. The data shown here represents only a portion of the calculations available in the Ultra 3D analysis software.

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Indentation | Hardness & Elastic

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Indentation | Creep & Relaxation

Indentation | Loss & Storage

Indentation | Fracture Toughness

Indentation | Yield Strength & Fatigue

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