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Time-Series Pattern Verification in CNC Machining Data

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Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14115))

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Abstract

Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series’ target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % ± 1.2 and an F1-score of 95.4 % ± 1.3.

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Acknowledgments

This work is co-financed by the ERDF - European Regional Development Fund, through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme under the Portugal 2020 Partnership Agreement within project PRODUTECH4SC, with reference POCI-01-0247-FEDER-046102 and through the Norte Portugal Regional Operational Programme - NORTE 2020 under the Portugal 2020 Partnership Agreement, within project SADCoPQ, with reference NORTE-01-0247-FEDER-069725, and co-financed by Component 5 - Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021–2026, within project Produtech_R3, with reference 60. Additionally, we thank Jasil and Vanguarda for the data and data infrastructure provided.

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Authors and Affiliations

  1. INESC TEC, Rua Dr Roberto Frias, 4200 - 465, Porto, Portugal

    João Miguel Silva, Ana Rita Nogueira, José Pinto, António Correia Alves & Ricardo Sousa

  2. Faculdade de Engenharia da Universidade do Porto, Rua Dr Roberto Frias, 4200 - 465, Porto, Portugal

    João Miguel Silva, Ana Rita Nogueira & Ricardo Sousa

Authors
  1. João Miguel Silva
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  2. Ana Rita Nogueira
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  3. José Pinto
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  4. António Correia Alves
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  5. Ricardo Sousa
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Corresponding author

Correspondence to João Miguel Silva .

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Editors and Affiliations

  1. Lucy Family Institute for Data & Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA - LIACC, University of Azores, Ponta-Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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Silva, J.M., Nogueira, A.R., Pinto, J., Alves, A.C., Sousa, R. (2023). Time-Series Pattern Verification in CNC Machining Data. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14115. Springer, Cham. https://doi.org/10.1007/978-3-031-49008-8_24

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  • DOI: https://doi.org/10.1007/978-3-031-49008-8_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49007-1

  • Online ISBN: 978-3-031-49008-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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