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GEP- and ANN-based tool wear monitoring: a virtually sensing predictive platform for MQL-assisted milling of Inconel 690

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Abstract

Inconel 690 is an extensively used superalloy in the aerospace and nuclear industries. Owing to the low thermal conductivity and poor machinability, the cutting tools are severely affected during the milling of Inconel 690. Thus, the machining of such superalloys consumes an extensive cost and time. In this context, an artificial intelligence–assisted cost-effective meta-model has been established in this manuscript for the accurate prediction of maximum flank wear. Here, a series of experiment was conducted on Inconel 690 using a TiAlN-coated solid carbide insert. Afterward, using the main effect plot (MEP) diagram, the effects of machining parameters on flank wear were evaluated. Additionally, the analysis of variance (ANOVA) demonstrated that the MQL flow rate is the most significant parameter affecting the flank wear. In the second part, 70% of machining output has been selected for training the gene expression programming (GEP) and artificial neural network (ANN) model, and the rest 30% data has been used for testing purpose. Furthermore, considering a statistical platform, the GEP model has been compared with an ANN model. The comparative analysis demonstrated that the GEP meta-model is superior to the ANN model in predicting the maximum flank wear under MQL environment. The outcomes of this study can aid the metal cutting industries to better plan the machining system on the perspective of the tool wear reduction with optimized parameters levels.

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Authors and Affiliations

  1. Production Engineering, National Institute of Technology, Agartala, 799046, India

    Binayak Sen & Uttam Kumar Mandal

  2. Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh

    Mozammel Mia

  3. Department of Natural Science, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, 741249, West Bengal, India

    Sankar Prasad Mondal

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  1. Binayak Sen
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Correspondence to Mozammel Mia.

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Sen, B., Mia, M., Mandal, U.K. et al. GEP- and ANN-based tool wear monitoring: a virtually sensing predictive platform for MQL-assisted milling of Inconel 690. Int J Adv Manuf Technol 105, 395–410 (2019). https://doi.org/10.1007/s00170-019-04187-y

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