Machine vision for the measurement of machining parameters: A review

Hashmi, Abdul Wahab; Mali, Harlal Singh; Meena, Anoj; Khilji, Irshad Ahamad; Hashmi, Mohammad Farukh; Saffe, Siti Nadiah binti Mohd

doi:10.1016/j.matpr.2021.11.271

Cited by 22 publications

(16 citation statements)

References 60 publications

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“…They have discussed the importance of surface characteristics measurement for metal AM parts. Similarly, Authors have also discussed the importance of machine vision techniques and artificial intelligence (AI) based technique for implementation of intelligent manufacturing [306,307]. Farukh et al [308][309][310][311][312][313][314] have compressively emphasized the importance of compute vision techniques using image processing for various field of engineering applications.…”

Section: Inspection Of Surface Defectsmentioning

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: Inspection Of Surface Defectsmentioning

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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“…For example, Sun et al [6] compared focusing functions in terms of accuracy, noise immunity, number and width of peaks, and analyzed the advantages and disadvantages of each focusing function. Hobson, DM et al [7] introduced texture, a visual feature represented by the distribution of greyscale of pixels and their surrounding spatial neighborhoods, and used it to distinguish between coal and gangue; Debi Prasad Tripathy et al [8] proposed three symbiotic matrix-based extension methods. methods and combined with a classification model of neural networks for gangue sorting.…”

Section: Introductionmentioning

confidence: 99%

“…The total intraclass variance is: (7) The interclass variance between the two classes is: (8) The threshold T that minimizes the intra-class variance or maximizes the inter-class variance, or minimizes the ratio of intra-and inter-class variance, is the optimal threshold. Entropy is a measure of uncertainty in information theory, a measure of the amount of information contained in the data, and when entropy takes the maximum value, it indicates that the amount of information obtained is the maximum.…”

mentioning

confidence: 99%

Adaptive analysis of rapid prediction of coal quality information based on image analysis

Liu

Zhang

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Machine vision technology has shown great potential for development and application in the coal heat utilization and coal chemical production process It is important to carry out image analysis-based stability adaptation analysis of coal heat utilization and coal chemical equipment. To further expand its application, this work reviewed the characteristics of different vision sensor systems and image processing algorithms in the process of image acquisition and processing, conducted applicability analysis for rapid prediction of coal quality, presented the current problems and future development trends of image analysis technology in the coal heat utilization and coal chemical production process. It provided theoretical guidance and reference for promoting automation, intelligence, and mechanization in the coal industry.

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“…Machine vision-based roughness measurement technology has the advantages of acquiring a large amount of information, high precision, non-contact, high efficiency, good flexibility, and low cost. Therefore, it has been applied to roughness measurement by many scholars [6][7][8][9][10][11][12]. The machine vision-based roughness measurement method is essentially based on principles of optics [13,14].…”

Section: Introductionmentioning

confidence: 99%

A new grinding surface roughness measurement method based on image quality algorithm and BP neural network

Guo²,

Zhu³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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Due to the problem of single factor and no benchmark in the design of image characteristic index, the measurement results of machine vision-based grinding surface roughness measurement are greatly influenced by the light source brightness, and the accuracy is limited. To address this problem, a method based on the combination of a full-reference (FR) image quality algorithm visual saliency-induced index (VSI) and a backward propagation (BP) neural network for grinding surface roughness measurement is proposed. First, the VSI is applied to characterize grinding surface roughness. Its performance is then compared with the mainstream color image-based indices. Meanwhile, the ability of anti-interference to light source is analyzed for the VSI. Furthermore, in order to improve the prediction accuracy, the BP neural network is selected and applied to construct the roughness prediction model. The experimental results show that the VSI has significant advantages in the abilities of measurement accuracy and anti-interference to light source brightness levels. BP can significantly improve the prediction accuracy for VSI.

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Machine vision for the measurement of machining parameters: A review

Cited by 22 publications

References 60 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

Adaptive analysis of rapid prediction of coal quality information based on image analysis

A new grinding surface roughness measurement method based on image quality algorithm and BP neural network

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