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Prediction of cutting tool wear during a turning process using artificial intelligence techniques

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Abstract

In the manufacturing industry, cutting tool failure is a serious event which causes damage to the cutting tool and reduces the quality of the product, which increases the cost of production. A reliable, intelligent, tool wear monitoring system is required in the metal cutting manufacturing process to mitigate these negative effects. This study presents a model-based approach for tool wear monitoring based on an adaptive neuro-fuzzy inference system (ANFIS) for a cold-finished steel bar 1215 turning process. A three-input cutting force (Fx, Fy and Fz) and single-output (tool flank wear) model was designed and implemented using the ANFIS approach. The forces were measured using a piezoelectric dynamometer and data acquisition system. Flank wear was also monitored using a tool maker’s microscope. The model prediction results show that it is accurate enough to perform online monitoring of the turning process and can detect wear while operating.

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Authors and Affiliations

  1. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, Ontario, K7L 3N6, Canada

    Mohsen Marani & Chris K. Mechefske

  2. Malaysia–Japan International Institute of Technology, Universiti Teknologi (UTM), 54100, Kuala Lumpur, Malaysia

    Mohammadjavad Zeinali

  3. École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montreal, QC, H3C 1K3, Canada

    Jules Kouam & Victor Songmene

Authors
  1. Mohsen Marani
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  2. Mohammadjavad Zeinali
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  3. Jules Kouam
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  4. Victor Songmene
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  5. Chris K. Mechefske
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Correspondence to Mohsen Marani.

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Marani, M., Zeinali, M., Kouam, J. et al. Prediction of cutting tool wear during a turning process using artificial intelligence techniques. Int J Adv Manuf Technol 111, 505–515 (2020). https://doi.org/10.1007/s00170-020-06144-6

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  • DOI: https://doi.org/10.1007/s00170-020-06144-6

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