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Remaining useful lifetime prediction for milling blades using a fused data prediction model (FDPM)

Author

Listed:
  • Teemu Mäkiaho

    (Tampere University)

  • Jouko Laitinen

    (Tampere University)

  • Mikael Nuutila

    (Pemamek Oy)

  • Kari T. Koskinen

    (Tampere University)

Abstract

In various industry sectors, predicting the real-life availability of milling applications poses a significant challenge. This challenge arises from the need to prevent inefficient blade resource utilization and the risk of machine breakdowns due to natural wear. To ensure timely and accurate adjustments to milling processes based on the machine's cutting blade condition without disrupting ongoing production, we introduce the Fused Data Prediction Model (FDPM), a novel temporal hybrid prediction model. The FDPM combines the static and dynamic features of the machines to generate simulated outputs, including average cutting force, material removal rate, and peripheral milling machine torque. These outputs are correlated with real blade wear measurements, creating a simulation model that provides insights into predicting the wear progression in the machine when associated with real machine operational parameters. The FDPM also considers data preprocessing, reducing the dimensional space to an advanced recurrent neural network prediction algorithm for forecasting blade wear levels in milling. The validation of the physics-based simulation model indicates the highest fidelity in replicating wear progression with the average cutting force variable, demonstrating an average relative error of 2.38% when compared to the measured mean of rake wear during the milling cycle. These findings illustrate the effectiveness of the FDPM approach, showcasing an impressive prediction accuracy exceeding 93% when the model is trained with only 50% of the available data. These results highlight the potential of the FDPM model as a robust and versatile method for assessing wear levels in milling operations precisely, without disrupting ongoing production.

Suggested Citation

  • Teemu Mäkiaho & Jouko Laitinen & Mikael Nuutila & Kari T. Koskinen, 2024. "Remaining useful lifetime prediction for milling blades using a fused data prediction model (FDPM)," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4035-4054, December.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-024-02398-z
    DOI: 10.1007/s10845-024-02398-z
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    References listed on IDEAS

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    1. Hamed Khosravi & Taofeeq Olajire & Ahmed Shoyeb Raihan & Imtiaz Ahmed, 2024. "A data driven sequential learning framework to accelerate and optimize multi-objective manufacturing decisions," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4087-4112, December.
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    Cited by:

    1. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

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    1. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

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