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Building a Decision Support System for Determining Cutting Parameters in Turning:A Case-Based Reasoning Approach


Affiliations
1 Mechanical Engineering Department, National Institute of Technology, Durgapur- 713209, West Bengal, India
2 Materials Processing & Microsystems Laboratory, CSIR-Central Mechanical Engineering Research Institute, Council of Scientific & Industrial Research (CSIR), Durgapur 713209, West Bengal, India
     

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Determining proper cutting parameters is key to an effective and economical machining operation, especially turning on a lathe. The complex nature of the domain involving multiple physics at play necessitates the use of alternative models using mathematical, statistical, and computational methods. Decision support systems based on experimental data involving these models help to specify the proper levels of cutting parameters in order to achieve desired machinability outcomes. In this work, case-based reasoning approach is adopted, which tries to estimate solution based on past similar but non-identical records stored as cases. The database of cases better known as case base, which is core to this paradigm is created by virtual data generated by statistical regression model, which is initially built by experimental data while turning EN24 steel with uncoated carbide tool insert. The searching of the best similar matching case is done using a well-established k-NN algorithm. The results obtained by this approach are validated by confirmatory runs and benefits of this over counter proposition of using exact search from database are discussed.

Keywords

Decision Support System, Turning, Regression, Case-Based Reasoning, k-NN.
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  • Rao, R.V., Advanced modeling and optimization of manufacturing processes, Springer Series in Advanced Manufacturing, 2011.
  • Singh, B. K., Mondal, B., and Mandal, N., Machinability evaluation and desirability function optimization of turning parameters for Cr2O3 doped zirconia toughened alumina (Cr-ZTA) cutting insert in high speed machining of steel, Ceramic International, Vol. 42, pp.3338-3350, 2015.
  • Chinchanikar, S. and Choudhury, S.K., Investigations on machinability aspect of hardened AISI 4340 steel different levels of hardness using coated carbide tools, International Journal of Refractory Metals and Hard Materials, Vol.38, pp.124-133, 2013.
  • Salvatore, F., Saad, S. and Hamdi, H., Modelling and simulation of tool wear during the cutting process, Proceedings of the 14th CIRP Conference on Modelling of Machining Operations (CIRP CMMO), Procedia CIRP 8, pp.305-310, 2013.
  • Muthukrishnan, M. and Davim, J.P., Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis, J. Mater. Process. Technol., Vol. 209, pp.225-232,2009.
  • Pratihar, D.K., Soft Computing, Narosa Publishing House, India, 2008.
  • Datta, S., Deepanshu and Pratihar, D.K., Modelling of input-output relationships of metal inert gas welding process using soft computing-based approaches, International Journal of Computational Intelligence Studies, Vol. 6, No.1, pp.1-28, 2017.
  • Kolodner, J.L., Case-Based Reasoning, Morgan Kaufmann Publishers, Inc., 1993.
  • Dhar, A.R., Connectionist learning of weights for k-NN retrieval, Proceedings of the 3rd International Conference on Electronics Computer Technology, Kanyakumari, doi:10.1109/ICECTECH.2011.594205, pp. 81-85, 2011.
  • Ociepka, P. and Herbuoe, K., Application ofthe CBR method for adding the process of cutting tools and parameters selection, IOP Conference Series: Mater Sci. Eng., Vol.145, pp.022-029, 2016.

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  • Building a Decision Support System for Determining Cutting Parameters in Turning:A Case-Based Reasoning Approach

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Authors

Ananda Rabi Dhar
Mechanical Engineering Department, National Institute of Technology, Durgapur- 713209, West Bengal, India
Nilrudra Mandal
Materials Processing & Microsystems Laboratory, CSIR-Central Mechanical Engineering Research Institute, Council of Scientific & Industrial Research (CSIR), Durgapur 713209, West Bengal, India
Shibendu Shekhar Roy
Mechanical Engineering Department, National Institute of Technology, Durgapur- 713209, West Bengal, India

Abstract


Determining proper cutting parameters is key to an effective and economical machining operation, especially turning on a lathe. The complex nature of the domain involving multiple physics at play necessitates the use of alternative models using mathematical, statistical, and computational methods. Decision support systems based on experimental data involving these models help to specify the proper levels of cutting parameters in order to achieve desired machinability outcomes. In this work, case-based reasoning approach is adopted, which tries to estimate solution based on past similar but non-identical records stored as cases. The database of cases better known as case base, which is core to this paradigm is created by virtual data generated by statistical regression model, which is initially built by experimental data while turning EN24 steel with uncoated carbide tool insert. The searching of the best similar matching case is done using a well-established k-NN algorithm. The results obtained by this approach are validated by confirmatory runs and benefits of this over counter proposition of using exact search from database are discussed.

Keywords


Decision Support System, Turning, Regression, Case-Based Reasoning, k-NN.

References





DOI: https://doi.org/10.22485/jaei%2F2019%2Fv89%2Fi1-2%2F185675