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Process monitoring of the AISI D6 steel turning using artificial neural networks

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Abstract

This work implements artificial neural networks (ANNs) to monitor tool wear during turning of AISI D6 steel. The goal is to detect wear, prevent tool breakage, and enhance productivity. Experimental tests involve using polycrystalline cubic boron nitride tools to turn AISI D6 steel, measuring wear and surface roughness. ANNs are trained with machining forces, electric current, and acoustic emission data. Findings showed that cutting and machining forces, along with motor current, increased as the wear progresses. The dominant frequencies of the acoustic emission signal range from 30 to 40 kHz, with decreasing RMS as wear increases. The ANN achieved satisfactory results, with wear estimation deviations below 0.09 mm. However, its predictive capability beyond the training range is limited. Workpiece surface roughness estimation showed around 8% error compared to the experimental Ra values. Overall, the ANN-based approach enables real-time tool wear monitoring, reducing the risk of damage and optimizing productivity.

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Authors and Affiliations

  1. School of Mechanical Engineering, Federal University of Uberlandia, Av. João Naves de Ávila, 2121, Bloco 1M, Uberlandia, MG, 38400-902, Brazil

    Victor Hugo Serafim Rubin, Leonardo Rosa Ribeiro da Silva, Kenji Fabiano Ávila Okada, Felipe Chagas Rodrigues de Souza & Alisson Rocha Machado

  2. Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080, Chelyabinsk, Russia

    Danil Yurievich Pimenov

  3. Villares Metals S.A, R. Alfredo Dumont Vilares, 155-Jardim Santa Carolina (Nova Veneza), Sumare, SP, CEP: 13178-902, Brazil

    Marcio Tadeu Gravalos

  4. Graduate Program in Mechanical Engineering, Pontifícia Universidade Católica do Paraná – PUC-PR, R. Imaculada Conceição, 1155, Bairro Prado Velho, CEP, Curitiba, PR, 80215-901, Brazil

    Alisson Rocha Machado

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  1. Victor Hugo Serafim Rubin
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  2. Leonardo Rosa Ribeiro da Silva
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The authors declare that all co-authors participated in all the development stages of this article.

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Correspondence to Leonardo Rosa Ribeiro da Silva.

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Rubin, V.H.S., da Silva, L.R.R., Okada, K.F.Á. et al. Process monitoring of the AISI D6 steel turning using artificial neural networks. Int J Adv Manuf Technol 127, 3569–3584 (2023). https://doi.org/10.1007/s00170-023-11745-y

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