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Artificial intelligence based system to improve the inspection of plastic mould surfaces

Author

Listed:
  • André. F. H. Librantz

    (Nove de Julho University - UNINOVE)

  • Sidnei A. Araújo

    (Nove de Julho University - UNINOVE)

  • Wonder A. L. Alves

    (Nove de Julho University - UNINOVE)

  • Peterson A. Belan

    (Nove de Julho University - UNINOVE)

  • Rafael A. Mesquita

    (Nove de Julho University - UNINOVE)

  • Antonio H. P. Selvatici

    (Nove de Julho University - UNINOVE)

Abstract

Plastic industry is today in a constant growth, demanding several products from other segments, which includes the plastic moulds, used mainly in the injection moulding process. This paper presents a methodology for the surface evaluation of plastic moulds, aiming the automation of the polishing surface analysis. Provided that this type of analysis by traditional procedures can be slow and expensive, the development of automatic system could lead to considerable improvements regarding the speed and reliability of information. The starting point of the evaluation procedure is the image generated by the laser light scattered over the sample mould surface that could be captured and analysed by image processing and artificial intelligence techniques. The results showed that the proposed system is able to mapping and classifying several damages over the polished surface and could be an alternative to reduce efficiently the costs and the spending time in mould surface inspection tasks.

Suggested Citation

  • André. F. H. Librantz & Sidnei A. Araújo & Wonder A. L. Alves & Peterson A. Belan & Rafael A. Mesquita & Antonio H. P. Selvatici, 2017. "Artificial intelligence based system to improve the inspection of plastic mould surfaces," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 181-190, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0969-5
    DOI: 10.1007/s10845-014-0969-5
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    Cited by:

    1. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    2. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.

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