Machining process classification using PCA reduced histogram features and the Support Vector Machine

Ashour, Mohammed Waleed; Khalid, Fatimah; Halin, Alfian Abdul; Abdullah, Lili Nurliyana

doi:10.1109/icsipa.2015.7412226

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…The smooth zone (characterized by less ruggedness) is increased with the rate of feed decrease. In [157], the following outcomes were inferred from the analysis of the results using the ANOVA procedure and the S/N ratio methodology.…”

Section: Surface Characteristics Measurement For Part's Surface Gener...mentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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Section: Surface Characteristics Measurement For Part's Surface Gener...mentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

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“…e larger the separating or functional margin, the lower the classification error [46]. Originally, SVMs were designed for binary classification; however, they can also be used for nonlinear classification problems with the help of kernels [47], hence making them more versatile for classification problems [46]. Linear SVM is used since it is simple to implement.…”

Section: Support Vector Machine Support Vector Machinesmentioning

confidence: 99%

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Pule

Matsebe

Samikannu

2022

Mathematical Problems in Engineering

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Vibration analysis is widely used as an efficient condition monitoring (CM) tool for rotating machines in various industries. Fault detection and diagnosis (FDD) models play an important role in the development of any CM system. The use of artificial intelligence (AI) has since gained recognition in the development of fault detection and diagnosis systems. In this paper, a combination of principal component analysis (PCA) which is used for reducing the data dimensionality, and support vector machine (SVM) which is adopted for classification to carry out fault detection and diagnosis of faults in bearings using vibrations. The diagnostic feature design and machine learning toolbox in MATLAB are used to develop features and train the models, respectively. Real data from the Mendeley data depository is used to test and evaluate the models. Model training is carried out using data with varying speeds representing different conditions of bearing making it different from similar approaches involving SVM. The choice of data used proves that SVM can be able to classify faults with consideration of the varying operating speeds. Results have shown that the combination of PCA and SVM is effective in fault diagnosis of bearing faults under varying speeds such that a 97.4% classification accuracy was achieved. The result implies that PCA and SVM can be implemented in various industrial setups where variable speeds can occur both intentionally or nonintentionally. Furthermore, the method was able to differentiate between compounding faults and faults that occur at different times. The confusion matrix further proves the quality and accuracy of the trained model. Future work will focus on the development of models that can carry out the prognosis of faults in bearings as well as to model for other faults other than bearing faults.

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“…The results of the experiments showed that the proposed contrast-adjusting methods have performance similar to minimum error thresholding (MET) and are generally better than Otsu's method. In 2015, M. W. Ashour et al [9] proposed the approach of the Support Vector Machine adaptive threshold segmentation are used. And then, the contours from the binary image are extracted.…”

Section: Introductionmentioning

confidence: 99%

Study on Machine Learning Based Intelligent Defect Detection System

Huang¹,

Lin

2018

MATEC Web Conf.

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In the paper, it is proposed to develop a machine learning based intelligent defect detection system for metal products. The common machine vision system has the surface (stain, shallow pit, shallow tumor, scratches, Edge defects, pattern defects) detection, or for the processing of the size, diameter, diameter, eccentricity, height, thickness and other parts of the non-contact numerical parameters of detection. Considering the quality of the work piece and the defects of the standard, so for the quality of customized testing requirements, the study is the development of machine vision and machine learning metal products defect detection system, mainly composed of three procedures: Image preprocessing, training procedures and testing procedures. The system architecture consists of three parts: (1) Image preprocessing: we first use the machine vision. OPENCV to carry out the image pre-processing part of the product before the detection. (2) Training procedures: The algorithm of the machine learning includes the convolution neural network (CNN), chunk-max pooling is used to train the program, and the generative adversarial network (GAN) based architecture is used to solve the problem of small datasets for surface defects. (3) Testing procedures：The Python language is used to write the program and implement the testing procedures with the GPU-Based embedded hardware In industries, collecting training dataset is usually costly and related methods are highly dataset-dependent. So most companies cannot provide Big-data to be analyzed or applied. By the experimental results, the recognition accuracy can be obviously improved as increasing data augmentation by GAN-Based samples maker. Manual inspection is labor intensive, costly and less in efficiency. Therefore, this study will contribute to technological innovation, industry, national development and other applications. (1) The use of intelligent machine learning technology will make the industry 4.0 technology more sophisticated. (2) It will make the development of equipment industry be better by the machine learning applications. (3) It will increase the economics and productivity of countries for the aging of the population by machine learning.

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Machining process classification using PCA reduced histogram features and the Support Vector Machine

Cited by 12 publications

References 14 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Study on Machine Learning Based Intelligent Defect Detection System

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