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Identification and classification of materials using machine vision and machine learning in the context of industry 4.0

Author

Listed:
  • Durga Prasad Penumuru

    (Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram)

  • Sreekumar Muthuswamy

    (Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram)

  • Premkumar Karumbu

    (Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram)

Abstract

Manufacturing has experienced tremendous changes from industry 1.0 to industry 4.0 with the advancement of technology in fast-developing areas such as computing, image processing, automation, machine vision, machine learning along with big data and Internet of things. Machine tools in industry 4.0 shall have the ability to identify materials which they handle so that they can make and implement certain decisions on their own as needed. This paper aims to present a generalized methodology for automated material identification using machine vision and machine learning technologies to contribute to the cognitive abilities of machine tools as wells as material handling devices such as robots deployed in industry 4.0. A dataset of the surfaces of four materials (Aluminium, Copper, Medium density fibre board, and Mild steel) that need to be identified and classified is prepared and processed to extract red, green and blue color components of RGB color model. These color components are used as features while training the machine learning algorithm. Support vector machine is used as a classifier and other classification algorithms such as Decision trees, Random forests, Logistic regression, and k-Nearest Neighbor are also applied to the prepared data set. The capability of the proposed methodology to identify the different group of materials is verified with the images available in an open source database. The methodology presented has been validated by conducting four experiments for checking the classification accuracies of the classifier. Its robustness has also been checked for various camera orientations, illumination levels, and focal length of the lens. The results presented show that the proposed scheme can be implemented in an existing manufacturing setup without major modifications.

Suggested Citation

  • Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01508-6
    DOI: 10.1007/s10845-019-01508-6
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    References listed on IDEAS

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    1. Masoud Vejdannik & Ali Sadr, 2018. "Automatic microstructural characterization and classification using probabilistic neural network on ultrasound signals," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1923-1940, December.
    2. D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
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    7. Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
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    9. George Lãzãroiu & Armenia Androniceanu & Iulia Grecu & Gheorghe Grecu & Octav Neguri?ã, 2022. "Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1047-1080, December.
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