A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study

In the wear and tear process of synovial joints, wear particles generated and released from articular cartilage within the joints have surface topography and mechanical property which can be used to reveal wear conditions. Three-dimensional (3D) particle images acquired using laser scanning confocal microscopy (LSCM) contain appropriate surface information for quantitatively characterizing the surface morphology and changes to seek a further understanding of the wear process and wear features. This paper presents a new attempt on the 3D numerical characterisation of wear particle surfaces using the field and feature parameter sets which are defined in ISO/FDIS 25178-2. Based on the innovative pattern recognition capability, the feature parameters are, for the first time, employed for quantitative analysis of wear debris surface textures. Through performing parameter classification, ANOVA analysis and correlation analysis, typical changing trends of the surface transformation of the wear particles along with the severity of wear conditions and osteoarthritis (OA) have been observed. Moreover, the feature parameters have shown a significant sensitivity with the wear particle surfaces texture evolution under OA development. A correlation analysis of the numerical analysis results of cartilage surface texture variations and that of their wear particles has been conducted in this study. Key surface descriptors have been determined. Further research is needed to verify the above outcomes using clinic samples

The Segmentation of Wear Particles Images Using J

This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system

Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Effect of fullerene containing lubricants on wear resistance of machine components in boundary lubrication

Fullerenes, a new form of carbon nanomaterials, possess unique physical and mechanical properties that make their use as additives to liquid lubricants potentially beneficial. The goal of this study was to investigate the effect of fullerene containing lubricants on wear resistance of steel-bronze couples operating under boundary lubrication conditions. A mathematical model of deformed asperity contact was built to calculate real contact area and real contact pressure. Computer controlled wear friction testing methodology and equipment were designed, developed and implemented for obtaining reliable and objective experimental data. In addition, optical and scanning electron microscopy and standard surface texture analysis were employed. Heavy duty motor oil SAE 10 was modified by admixing fullerenes C60, a fullerene mixture of C60 and C70, fullerene containing soot, and graphite powder. The experiments showed that all of the selected fullerene additives dissolved in liquid lubricants reduce wear of the tested materials. In addition, it was found that despite improvements in wear resistance, the selected modified lubricants did not significantly change friction characteristics. Improvement of wear resistance of contact surfaces operating with fullerene modified lubricants can be explained by the presence of fullerenes in real contact while the liquid lubricant is squeezed out. Fullerenes are considered to function as minute hard particles that do not break down under applied normal force, and tend to separate direct contact of functional surfaces of selected materials

Computer vision-based monitoring of abrasive loading during wood machining

Surface quality is an important characteristic commonly assessed in wooden products. Sanding relies on coated abrasives as tooling for both dimensioning and surface finishing but their performance is dependent on chip loading and grit wear. Traditionally, the useful life of abrasive belts in sanding operation has been manually assessed. This type of inspection is highly subjective and dependent upon individual expertise, consequently leading to under utilization or over utilization of the abrasive. This, in turn, affects the production costs and quality of the product. In this work, an intelligent classification method that determines the optimal replacement policy for a belt exposed to known manufacturing parameters is developed. Controlled experiments were conducted to develop abrasive belts of known exposure, followed with digital microscopy to capture images and process them with pattern recognition and classification algorithms. Grit size and machining time were the parameters of interest while response of the experiments included image information from the abrasive sheets after every experimental run. These images were used in training an artificial neural network that in turn, help in determining data to categorize the useful life of the abrasive. The results show a 95% success rate in accurately classifying abrasive images of similarly conditioned abrasives. Also, the results show that the classification of interpolated and extrapolated times of abrasive usage are classified with a 95% success rate. A classification of abrasive images is proposed to be used as one of the inputs to a decision system that would help in evaluating the life of the abrasive and replacement policies. Further research on the relationship between the different parameters affecting the useful life of the abrasive is proposed