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A Genetic-Based SVM Approach for Quality Data Classification

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Artificial Intelligence and Industrial Applications (A2IA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1193))

Abstract

With the emergence of the Industry 4.0 and the big data era, many organizations had recourse to data-based approaches for Quality management. One of the main aims of the data-based approaches in manufacturing industries is quality classification. Classification methods provide many solutions related to quality problems such a defect detection and conformity prediction. In that context, this paper identifies a suitable technique (Support Vector Machine) for quality data classification, as it proposes an appropriate approach to optimize its performances. The proposed approach is tested on a chemical manufacturing dataset and a rolling process dataset, in order to evaluate its efficiency.

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Author information

Authors and Affiliations

  1. Arts et Metiers Institute of Technology, Université de Lorraine, LCFC, HESAM University, 57070, Metz, France

    Wahb Zouhri, Lazhar Homri & Jean-Yves Dantan

  2. Data science and Engineering group, ASML, De Run 6501, 5504 DR, Veldhoven, The Netherlands

    Hamideh Rostami

Authors
  1. Wahb Zouhri
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  2. Hamideh Rostami
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  3. Lazhar Homri
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  4. Jean-Yves Dantan
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Corresponding author

Correspondence to Wahb Zouhri .

Editor information

Editors and Affiliations

  1. Laboratory of Mathematical Modeling, Simulation and Smart Systems (L2M3S), Department of Mathematics and Computer Science, Artificial Intelligence for Engineering Sciences Team (IASI), National Graduate School for Arts and Crafts, My Ismail University, Meknes, Morocco

    Tawfik Masrour

  2. Laboratory of Mathematical Modeling, Simulation and Smart Systems (L2M3S), Department of Industrial and Manufacturing Engineering, Modeling and Optimization of Industrial Systems Team (MOSI), National Graduate School for Arts and Crafts, My Ismail University, Meknes, Morocco

    Anass Cherrafi

  3. Laboratory of Mathematical Modeling, Simulation and Smart Systems (L2M3S), Department of Industrial and Manufacturing Engineering, Artificial Intelligence for Engineering Sciences Team (IASI), National Graduate School for Arts and Crafts, My Ismail University, Meknes, Morocco

    Ibtissam El Hassani

Appendix

Appendix

See Tables 4, 5 and 6.

Table 4 Optimal sigmoid and polynomial kernel functions
Full size table
Table 5 Average fitness of every generation—Chemical Data
Full size table
Table 6 Average fitness of every generation—Rolling Data
Full size table

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Zouhri, W., Rostami, H., Homri, L., Dantan, JY. (2021). A Genetic-Based SVM Approach for Quality Data Classification. In: Masrour, T., Cherrafi, A., El Hassani, I. (eds) Artificial Intelligence and Industrial Applications. A2IA 2020. Advances in Intelligent Systems and Computing, vol 1193. Springer, Cham. https://doi.org/10.1007/978-3-030-51186-9_2

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