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Machine Learning for Industrial Manufacturing: A Case Study on Injection Molding Machine Selection Support

In: Information Systems and Technological Advances for Sustainable Development

Author

Listed:
  • Faouzi Tayalati

    (Abdelmalek Essaadi University)

  • Ikhlass Boukrouh

    (Abdelmalek Essaadi University)

  • Abdellah Azmani

    (Abdelmalek Essaadi University)

  • Monir Azmani

    (Abdelmalek Essaadi University)

Abstract

Selecting the right injection molding machine for new products is a significant challenge for manufacturers. The traditional approach involves detailed calculations of clamping force, mechanical mold evaluations, and hands-on trials. This method is time-consuming, costly, and requires expert skills. This paper explores how machine learning can enhance machine selection efficiency and aid decision-making using criteria such as product type, material properties, and mold specifications. Two machine learning models, Support Vector Machine (SVM) and Random Forest, were applied using real data from the automotive plastics industry. Results show machine learning accurately predicts machine selection, with Random Forest outperforming SVM (Accuracy: 81%, F1: 78%, Precision: 85%, Recall: 81% vs. SVM’s Accuracy: 67%, F1: 66%, Precision: 66%, Recall: 67%). These findings highlight the potential benefits of integrating classification algorithms into injection molding workflows.

Suggested Citation

  • Faouzi Tayalati & Ikhlass Boukrouh & Abdellah Azmani & Monir Azmani, 2024. "Machine Learning for Industrial Manufacturing: A Case Study on Injection Molding Machine Selection Support," Lecture Notes in Information Systems and Organization, in: Mohamed Ben Ahmed & Anouar Abdelhakim Boudhir & Hany Farhat Abd Elhamid Attia & Adriana Eštoková & M (ed.), Information Systems and Technological Advances for Sustainable Development, pages 283-291, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-75329-9_31
    DOI: 10.1007/978-3-031-75329-9_31
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