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Surface quality analysis of polished surface has been the subject of many classic studies in surface polishing technology and is a key indicator for evaluating the polishing path. However, as an important part of surface quality, surface texture features have been ignored in many related researches. In this paper, a new method to analyze the texture features of polishing based on co-occurrence matrix is proposed. It provides a new perspective of surface quality analysis focusing on surface texture features. It extends the previous approach termed the residual level co-occurrence matrix (RLCM) focusing on the distribution of surface polishing residues, leading to more targeted and stabilized evaluation results. This method can be used in the path planning stage to estimate the polishing quality of the planned path without physical processing, which can avoid resource waste in the physical world. Furthermore, in this method, the usage of images is avoided, which can ensure that the results are not affected by the light and image quality. Simulation experiments as well as empirical investigations were conducted to verify the feasibility of the method. The results of both consistently reveal that the proposed method is able to accurately describe the texture feature and correctly analyze the texture feature. [ABSTRACT FROM AUTHOR]

This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy. [ABSTRACT FROM AUTHOR]

A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes. [ABSTRACT FROM AUTHOR]

Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identification of chatter is performed by evaluating the surface roughness of a turned workpiece undergoing chatter and chatter-free processes. In this paper, an image-processing approach for the identification of chatter vibration in a turning process was investigated. Chatter is identified by first establishing the correlation between the surface roughness and the level of vibration or chatter in the turning process. Images from chatter-free and chatter-rich turning processes are analyzed. Several quantification parameters are utilized to differentiate between chatter and chatter-free processes. The arithmetic average of gray level G a is computed. Intensity histograms are constructed and then the variance, mean, and optical roughness parameter of the intensity distributions are calculated. The surface texture analysis is carried out on the images using a second-order histogram or co-occurrence matrix of the images. Analysis is performed to investigate the ability of each technique to differentiate between a chatter-rich and a chatter-free process. Finally, a machine vision system is proposed to identify the presence of chatter vibration in a turning process. [ABSTRACT FROM AUTHOR]