Wear Measurements Research Articles

ONLINE WEAR ANALYSIS OF CARD CLOTHINGS

The processing of abrasive fibres in the carding process, in particular high-performance fibres such as glass, carbon or aramid fibres, can cause increased wear of the card clothing. In the FutureTex project ‘HPFGarnitur’, the wear of card clothing was investigated and an online wear measurement system has been developed. The aim of the project was both, to optimize the clothings to enable gentler processing of the fibres, and to develop a digital monitoring system to observe the degree of wear of the clothings, which offers a new possibility for maintenance prediction and production planning in the sense of Industry 4.0.

A Fast Global Optimal Strategy for Iteration Closest Point Using 2D‐BnB and Its Application to Rail Profile Registration

Profile registration is critical to rail wear measurement with line structured light, and the most popular registration method is iteration closest point (ICP). Unfortunately, ICP is often invalid in actual applications because it is easy to trap into local minima. To solve this problem, we propose a hybrid 2D‐point‐set registration method which combined ICP to branch and bound. In this way, we can ensure that the ICP algorithm converges to the global optimum. This strategy can achieve high‐registration precision, but it suffers from large computation costs. To address this issue, we propose an acceleration scheme by sparsely sampling the point‐set before registration to relieve computation burden. Extensive experiments are conducted to verify the precision, stability, and efficiency of our method. The results show that our method has superior precision and stability compared to the other typical profile registration methods. The ability to achieve fast registration speed which is suitable for dynamic measurement is another contribution of our work.

A CONTRIBUTION TO THE KNOWLEDGE OF SYNERGY IN TRIBOLOGY

The article contains information about the knowledge of synergism in tribology systems. Two examples of analyses of synergism in tribology systems are presented in the article. In the first example, the interaction coefficient (synergy coefficient) was used to evaluate a set of engine and gear oils composed of special lubricating (surface-active) additives to improve the galling load – a measure of boundary layer strength, evaluated on a 4-ball apparatus. Using the interaction coefficient, a 12-element set of oils (compositions) was "separated" into three groups: synergism with additives (KS>1), sometimes strong, at Ks=2, neutral interaction (KN=1), antagonism with additives (KA<1). Analysis of test results using the synergism coefficient also makes it possible to select the optimal additive concentration in commercial oil. In the second case, a three-factor system was analysed, in which the resultant (undesirable) characteristic was a measure of mechanicalcorrosion- abrasive wear of metal parts. In specially designed experiments, the components of total wear derived from the three underlying factors (mechanical, corrosion and environmental), and the interaction between them was determined. The contribution of the sum component of the mechanical-corrosion-abrasive interactions was found to range from 40 to 50% of the total wear (at 50%, there is a strong synergism KS=1).

An Index for Rail Weld Health Assessment in Urban Metro Using In-Service Train

Rail welds are considered as the weak part of a railway track. Their defects and health can directly affect wheel-rail interaction, track safety, and reliability. Current practices for rail welds health assessment are based on 2D vertical and lateral wear measurement which needs time and track blocking. The development of inertia-based condition monitoring methods such as measuring axle box acceleration (ABA) comes with a crucial question on criteria or index for each rail track component health monitoring. In this study, an index for evaluation of rail weld health is proposed through integrated numerical and field experiment data within a metro line using the ABA technique. The relationship between the speed, wheel structural vibration, and acceleration amplitude is investigated using fast Fourier transformation (FFT) and a nonlinear neural network principal component analysis (PCA) model. An index is introduced to assess weld severity level based on the statistical method. This index is simple and applicable for maintenance practice.

Vital protocols for PolyWare™ measurement reliability and accuracy.

PolyWare™ software (PW) has been exclusively used in the majority of polyethylene wear studies of total hip arthroplasty (THA). PW measurements can be significantly inaccurate and unrepeatable, depending on imaging conditions or subjective manipulation choices. In this regard, this study aims to shed light on the conditions needed to achieve the best accuracy and reliability of PW measurements. The experiment looked at how PW fluctuated based on several measurement conditions. x-ray images of in-vitro THA prostheses were acquired under a clinical x-ray scanning condition. A linear wear rate of 6.67 mm was simulated in combination with an acetabular lateral inclination of 36.6° and anteversion of 9.0°. Among all the imported x-ray images, those with a resolution of 1,076 × 1,076 exhibited the best standard deviation in wear measurements as small as 0.01 mm and the lowest frequencies of blurriness. The edge detection area specified as non-square and off the femoral head center exhibited the most blurriness. The x-ray image that scans a femoral head eccentrically placed by 15 cm superior to the x-ray beam center led to a maximum acetabular anteversion measurement error of 5.3°. Because PW has been the only polyethylene wear measurement tool used, identifying its sources of error and devising a countermeasure are of the utmost importance. The results call for PW users to observe the following measurement protocols: (1) the original x-ray image must be a 1,076 × 1,076 square; (2) the edge detection area must be specified as a square with edge lengths of 5 times the diameter of the femoral head, centered at the femoral head center; and (3) the femoral head center or acetabular center must be positioned as close to the center line of the x-ray beam as possible when scanning.

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid.

An atomic force microscope (AFM) fundamentally measures the interaction between a nanoscale AFM probe tip and the sample surface. If the force applied by the probe tip and its contact area with the sample can be quantified, it is possible to determine the nanoscale mechanical properties (e.g., elastic or Young's modulus) of the surface being probed. A detailed procedure for performing quantitative AFM cantilever-based nanoindentation experiments is provided here, with representative examples of how the technique can be applied to determine the elastic moduli of a wide variety of sample types, ranging from kPa to GPa. These include live mesenchymal stem cells (MSCs) and nuclei in physiological buffer, resin-embedded dehydrated loblolly pine cross-sections, and Bakken shales of varying composition. Additionally, AFM cantilever-based nanoindentation is used to probe the rupture strength (i.e., breakthrough force) of phospholipid bilayers. Important practical considerations such as method choice and development, probe selection and calibration, region of interest identification, sample heterogeneity, feature size and aspect ratio, tip wear, surface roughness, and data analysis and measurement statistics are discussed to aid proper implementation of the technique. Finally, co-localization of AFM-derived nanomechanical maps with electron microscopy techniques that provide additional information regarding elemental composition is demonstrated.

45 YEARS OF DELIBERATIONS ON THE THERMO-MECHANICAL INTERPRETATION OF FRICTION AND WEAR. PART II – THE MICROSCOPIC INTERPRETATION OF THE TRIBOLOGICAL PROCESS

In this second part, the pulse sources of heat, dependences among the intensive parameters of the friction process – temperature, unit pressures, friction velocity – and wear intensity, and some physical properties of the materials of solids in friction are presented. The flux densities are described in extensive quantities across the elementary surface dF of a nominal contact of solids. The thermodynamic criterion of galling is formulated, and the temperature characteristic of minimum wear and maximum resistance to wear is established. Minimum and maximum wear intensities and the specific work of wear are determined. It is proven that a temperature measured in a selected point of friction couple element does not uniquely characterise thermal processes in a tribological system. However, it does characterise the maximum gradient of temperatures measured in at least two points situated as close to a friction contact as possible. A method of determining unit real pressures is proposed. The presence of a cooling effect in the process of tribological wear is disclosed. Wear is interpreted as a disintegration of a solid caused by volumetric and superficial mechanical work. A system of new wear measures is suggested.

Measurement and analysis of tool wear and surface characteristics in micro turning of SLM Ti6Al4V and wrought Ti6Al4V

Parts produced using additive manufacturing (AM) have a significant transition in the features such as surface quality, residual stresses, microhardness, etc. To make the AM component suitable for an application, post-processing of the parts is required to remove the non-desirable features. In this regard, the micro turning of selective laser melted (SLM) Ti6Al4V are conducted to examine the surface characteristics, tool wear and chip morphology. The results obtained for SLM Ti6Al4V are compared with the results of wrought Ti6Al4V. Attributed to the higher strength and hardness, the SLM Ti6Al4V shows a higher tool wear and surface roughness than the wrought Ti6Al4V. The main tool wear mechanisms involved are built-up edge formation, adhesion, abrasion, and edge chipping for both the materials. The surface roughness is found increased with increasing cutting speed and feed. The wrought Ti6Al4V shows less feed marks and chip adhesion compared to SLM Ti6Al4V. Moreover, the materials side flow is observed in SLM Ti6Al4V only. The chips produced are long and continuous for wrought Ti6Al4V, whereas they are discontinuous for SLM Ti6Al4V. Morphology of the chips shows that they are of scaly structure for SLM Ti6Al4V for all the parameters, whereas they are of lamella structure for wrought Ti6Al4V, particularly at low feed and cutting speed.

Hybrid machine learning-enabled multi-information fusion for indirect measurement of tool flank wear in milling

To increase the utilization efficiency of multi-sensor signals and improve the measurement accuracy of tool wear, this study proposes an indirect tool wear measurement method based on multi-information fusion by hybrid machine learning technologies. In this method, triaxial cutting forces and vibration signals are collected and then preprocessed by wavelet packet denoising. The time, frequency, and time–frequency domain features are extracted to reduce the information redundancy, and the kernel principal component analysis is applied to fuse the most sensitive characteristics. A fusion model of least squares support vector regression and the particle swarm optimization algorithm is established to learn the dependency relationship between fused features and tool flank wear. Milling experiments under multiple working conditions are implemented to verify the effectiveness of the proposed method. Experimental results sufficiently demonstrated overall performance of the proposed method is better than that of other comparison methods.

Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision

One of the essential requirements for intelligent manufacturing is the low cost and reliable predictions of the tool life during machining. It is crucial to monitor the condition of the cutting tool to achieve cost-effective and high-quality machining. Tool conditioning monitoring (TCM) is essential to determining the remaining useful tool life to assure uninterrupted machining to achieve intelligent manufacturing. The same can be done by direct and indirect tool wear measurement and prediction techniques. In indirect methods, the data is acquired from the sensors resulting in some ambiguity, such as noise, reliability, and complexity. However, in direct methods, the data is available in images resulting in significantly less chances of ambiguity with the proper data acquisition system. The direct methods, which provide higher accuracy than indirect methods, involve collecting images of worn tools at different stages of the machining process to predict the tool life. In this context, a novel tool wear prediction system is proposed to examine the progressive tool wear utilizing the artificial neural network (ANN). Experiments were performed on AISI 4140 steel material under dry cutting conditions with carbide inserts. The cutting speed, feed, depth of cut, and white pixel counts are considered as input parameters for the proposed model, and the flank wear along with remaining tool life is predicted as the output. The worn tool images were captured using an industrial camera during the turning operation at regular intervals. The ANN training set predicts the remaining useful tool life, especially the sigmoid function and rectified linear unit (ReLU) activation function of ANN. The sigmoid function showed an accuracy of 86.5%, and the ReLU function resulted in 93.3% accuracy in predicting tool life. The proposed model’s maximum and minimum root mean square error (RMSE) is 1.437 and 0.871 min. The outcomes showcased the ability of image processing and ANN modeling as the potential approach for developing a low-cost industrial tool condition monitoring system that can measure tool wear and predict tool life in turning operations.

Abrasion testing of fibre rope on impermeable fabrics for wave energy converter systems

The NetBuoy system is being developed as means of providing low cost buoyancy, typically for use in the wave energy converter (WEC) sector where many devices rely on large, heavy and expensive fabricated steel hulls/structures to absorb the incoming wave energy.The NetBuoy system is an elegant concept in that it utilises an air pressurised impermeable buoyant pod to provide structural rigidity, which is restrained by a synthetic fibre rope net. This provides a structure that is cheaper to produce than steel equivalents and has additional benefits in terms of ease of transport to site and maintenance. However, there are a number of considerations relating to the performance of the buoy and the containing net. It is important that the net does not damage the buoy (which might lead to loss of buoyancy) as wave action may cause relative movement between net and buoy. Equally, the net must retain integrity so that the buoy does not come free from its tether (creating a shipping hazard and loss of device).This paper describes a novel test method developed to investigate the performance of a range of impermeable elastomer membranes (buoy materials) when abraded against a polyester fibre rope. It discusses the methodology of the tests and provides results of testing on a number of elastomer membrane materials, considering both wear volumes and measurement of friction between the rope and the membrane material as a means of assessing performance. Of the five materials assessed, a clear ranking of abrasive resistance in the elastomers was found on test with EPDM and Neoprene being clearly better than all other materials. Natural rubber performed well, whilst Hypalon and Nitrile were found to be least resistant to abrasion from the rope. In all tests the rope was largely unaffected by the abrasion.

2D and 3D Wear Analysis of 3D Printed and Prefabricated Artificial Teeth

This study aimed to assess the 3-body wear of prefabricated and 3D-printed artificial denture teeth. Four groups of artificial teeth were used; 3D-printed polymethylmethacrylate (PMMA) teeth (PR) and 3 prefabricated commercially available denture teeth: PMMA (Gnathostar, GN), PMMA (SR Orthotyp PE, SR), and Nanohybrid composite (SR Phonares NHC, PH). The 3-body wear test was performed using a steatite ceramic antagonist in a chewing simulator with 750,000 cycles, temperature 23 ± 2 ˚C, and force of 50 N. The abrasive medium was composed of ground millet seeds and white rice mixed with distilled water. The teeth were 3D-scanned before and after the wear test. The 3D images were assessed for teeth wear by measuring the volumetric (3D wear) and the vertical (2D wear) substance loss. The one-way analysis of variance followed by Tukey post hoc test was used to statistically obtain the data analysis. Maximum 3D wear was observed in the PR (51.05 ± 4.53 mm³), followed by GN (20.22 ± 6.29 mm³) and SR (12.12 ± 6.29 mm³) artificial teeth. Minimum wear occurred in the PH teeth (6.24 ± 0.87 mm³). The analytical differences amongst the groups were statistically significant (P < .05) except between PH and SR teeth. For 2D wear measurement, the maximum was seen in the GN teeth (6.29 ± 1.64 mm), followed by PR (5.04 ± 0.83 mm) and then SR (4.53 ± 0.87 mm). The PH teeth (3.09 ± 0.68 mm) again showed minimum wear. Statistically, amongst the groups, the major observable differences (P < .05) were between PH and GN, PH and PR, and SR and GN. Composite resin teeth had a greater wear resistance than acrylic resin teeth and 3D-printed resin teeth, both of which were comparable. Due to the advancement of digital workflows, manufacturers should devote effort to enhancing 3D-printed teeth.

Prediction Model for the Teeth Hob Cutter of Tunnel Boring Machines in High-content Quartzite Strata

Teeth hob cutter is widely used in cutting through hard rock. Cutter wear is inevitable during the strata with a large quartzite content, and excessive wear can lead to a decrease in tunneling efficiency and increase construction risks. Therefore, the importance of predicting the teeth hob cutter wear accurately and arranging the cutter change plan can never be overemphasized. In this paper, the rock breaking mechanism of teeth hob cutter was first analyzed. Based on this, combined with the theory of metal tribology theory, the wear prediction model for teeth hob cutters in high-content quartzite strata was derived. To verify the accuracy of the prediction model, the teeth hob cutter wear data for two cases were measured, which were collected from a cross-river shield tunnel project in Guangdong, China. Based on the observed wear data, it is found that the relative error of predicted wear and actual measurement was below 10%. The result shows that the prediction model can accurately reflect the real wear situation of teeth hob cutter in high-content quartzite strata.

Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear

Cutting tool wear strongly affects machining processes. Conventionally, multiple types of sensors are installed on the main rotating axis and platform of a machine, and signal analysis is used to determine the condition of the tool in real time through machine learning. With the traditional approach, to measure tool wear, it is required to remove the tool and place it underneath the microscope for measurement to obtain relatively precise results. The time cost, however, is relatively high; it is not cost-effective. With the charge-coupled device (CCD) camera installed in the machine tool, tool wear may be estimated through programed processing. The CCD camera, however, will affect spatial configuration in the machine tool and additional time is needed for tool wear measurement upon completion of processing each time. With the accelerometer sensor combining an offline or online tool wear measurement system and using the training method in this research model upon completion of the dataset, forecasting can be done with the precompleted model, which saves the time needed for offline or online wear measurement. However, the deployment of multiple sensors is difficult and expensive in practice. Used in this study were milling data, namely acoustic emission (AE), vibration, and current data from sensors installed on the rotating axis and platform of a machine. The data were from a National Aeronautics and Space Administration (NASA) dataset. The accelerometer data were used to develop a novel machine-learning algorithm. The vibration signals were integrated with other machining parameters. Signal preprocessing was used to reduce interference from environmental noise, and parameter records and feature signals were analyzed. A 1-D convolutional neural network (CNN) model similar to the DenseNet framework was developed. The model was optimized with framework and parameter adjustment and verified using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -fold cross-validation. The model had a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.09. Compared with other machine-learning models, the developed model has higher accuracy.

Robust Registration of Rail Profile and Complete Detection of Outliers in Complex Field Environment

The accurate measurement of rail wear is critical in track quality inspection. Usually, by matching the measured profile with the standard one based on the unworn rail waist double circle segment (DCS), and comparing their railhead differences, we obtain the rail wear. However, in the complex field environment, this task becomes tricky. For profile registration, one is that the location of rail waist becomes difficult with lots of outliers mixed in the profile, and the other is that the obtained rail waist could be polluted or incomplete. For wear measurement, the outliers scattered on the railhead could cause serious errors. This paper is devoted to solving the above problems. For problem 1, firstly, the rail waist is located after a preprocessing procedure. Then, utilizing the proposed hybrid model called R-H-ICP, we realize the profile registration with a process from coarse to fine. For problem 2, by using the standard profile and the reconstructed one with wear constraints to serve as the template separately, and computing the distance from each point of railhead on measured profile to the template, both the distinct and the inapparent outliers are detected precisely. The efficiency and superiority of proposed methods were verified by vast experiments. For the former, compared with DCS and ICP, the R-H-ICP not only improves the profile utilization ratio obviously, also guarantees the registration accuracy as far as possible. For the latter, the F1-Measure average score of outlier detection reaches 0.998, which outperforms some classical models markedly.

Surface Properties and Wear Resistance of Injectable and Computer-Aided Design/Computer Aided Manufacturing-Milled Resin Composite Thin Occlusal Veneers.

This study was conducted to investigate the microhardness, surface roughness (Ra), and wear behavior of thin occlusal veneers (TOV) fabricated from different injectable composite materials and compare them to a Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM) resin-based material. A 1-mm occusal veneer preparation was done in a mandibular right second molar typodont tooth. The prepared model was duplicated to fabricate 32 replicas and divided into four groups (n = 8). Standard TOV were fabricated either indirectly from Cerasmart blocks, Cerasmart, GC (CS), or directly from Beautifil Injectable X, Shofu (BF), G-ænial Universal injectable, GC (GU), or SonicFill 2, Kerr (SF) using the injection molding technique. All the specimens were subjected to both thermomechanical cyclic loading (TMC) in a chewing simulator. Wear measurement was conducted by three-dimensional (3D) scanning of the veneered models before and after TMC, and the difference in the volume of the sample was recorded as the volumetric material loss due to wear. Ra before and after TMC and Vickers microhardness (VHN) of the tested materials were measured using standardized samples (n = 8). Representative samples from each group were investigated under a stereomicroscope and a scanning electron microscope. One-way analysis of variance (ANOVA) was applied to detect the effect of material on VHN and wear. Two-way ANOVA was utilized to examine the impact of material and TMC on Ra. Multiple comparisons between the groups were conducted using Tukey's post hoc test (α = 0.05). The Pearson's correlation coefficient was used to determine the relationship between hardness and wear and between roughness and wear (α = 0.05). CS exhibited the highest mean VHN (p ≤ 0.001), followed by GU and SF which were statistically similar (p = 0.883) but significantly higher than BF (p < 0.001). After TMC, GU revealed the lowest Ra and volumetric wear (VW), followed by CS, BF, and SF (p < 0.5). A highly significant correlation existed between Ra and VW (p = 0.001, R 2 = 0.9803). The effect of TMC on the surface properties and wear resistance of the investigated TOV is material-dependent. GU injectable TOV are less influenced by TMC than CS milled TOV. In contrast, BF and SF demonstrated significant VW and Ra which might limit their clinical use as TOV.

Measuring tooth wear with regular intra-oral 3D scanners: for still in the future, or is it already possible?

Quantitative tooth wear measurement is a method of increasing importance when measuring tooth wear progression. The Radboud university medical center has developed a protocol that measures height and volumetric differences on regular 3D-scans. Intra-oral scans were made on patients with tooth wear and superimposed. To assess reliability the precision , as well as intra- and inter-rater precision of the protocol was tested. T-tests were performed to determine the structural and random error. Our findings indicate that the method is precise enough to measure height differences in patients with severe tooth wear progression, or tooth wear with an interval longer than 1 year. The method is not precise enough to measure volumetric changes.

Online tool wear monitoring by super-resolution based machine vision

The tool condition has been a major concern in modern computer numerical control (CNC) machining due to its direct effects on the quality of final product both in the surface and dimensional integrity. The conventional machine vision-based tool condition monitoring (TCM) approaches cannot meet the high precision requirement in micro machining, as the cutting parameters are in micro scale and the spindle works in high rotation speed which makes online tool wear measurement quite difficult. To meet these challenges, this study develops a single image super-resolution (SISR) approach for direct tool wear estimation in micro-milling. Motivated by the self-similarity of tool wear image morphology, this study proposes a sparse decomposition framework by learning dictionaries from the tool wear image pyramid. Based on their multi-scale invariant properties, the similar image patches of coarse scales can be retrieved from fine scales to reconstruct the high-resolution image fastly and in high quality. The reconstructed high-resolution image then can be conveniently applied to wear monitoring, and overcomes the image acquisition deficiencies of the conventional machine vision-based monitoring approaches. Experimental results validate this approach for the tool wear area estimation as well as its generalization of the wear width with regarding to the conventional manual measurements.

Design of a Tester for In Situ Simultaneous Measurement of the Wear of Two Different Film Materials

The amount of wear is one of the most important indicators for assessing the wear resistance of materials, controlling product quality and studying the mechanisms of frictional wear of materials. Due to the limitations of the friction and wear test equipment currently in use for measuring material wear resistance, it is not possible to accurately compare the wear resistance of two different materials. To solve this problem, this paper proposes a new type of friction and wear tester. With the newly designed friction and wear tester, it is possible to perform friction tests and in situ measurements of wear on two different materials at the same time. This will significantly reduce the measurement errors of currently used friction and wear test equipment that requires adjustment in order to measure the amount of wear; it is particularly suitable for accurately comparing the wear resistance of two different materials. The newly designed friction and wear tester can be used extensively to test and analyse the wear resistance of solid materials such as metals, ceramic materials and engineering plastics.

Dental hygiene students' participation as subjects in a randomized controlled trial: A full evidence-based dentistry experience.

Demonstrate that dental hygiene students' participation in a randomized controlled trial comparing the plaque-removing capability of two toothbrushes provides a fuller understanding of the factors affecting the potential application of research to practice. All students (N=18) in a baccalaureate dental hygiene class were engaged in the design of a randomized controlled trial using the Consolidated Standards of Reporting Trials (CONSORT) standard and then participated as subjects in a staggered, repeated measures trial using plaque removal as the dependent variable and brush type, brush head wear, and time in study as independent variables. A debriefing of student participation and lessons learned from analyzing the results was conducted. The study found statistically significant differences in plaque removal capability. Brush type accounted for only 4% of the variance, while measures of brush head wear were inconsistent, and time in the study ("experimental fatigue") accounted for the most variance (9%). Students recognized and confirmed by their personal experiences that research that fails to focus on variance can create an overly optimistic impression of research effectiveness. There was strong agreement that subjects/patients vary widely and that performance depends on multiple factors. Dental hygiene students who participated as subjects in a randomized controlled trial comparing toothbrushes for plaque removal capacity felt that full analysis to account for all sources of variance and estimate the magnitude of measures of effect add to the value of reported research. Variation across patients is important in practice, if often overlooked as an "error" in the literature.