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Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Author

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  • K. Venkata Rao

    (PBR VITS)

  • P. B. G. S. N. Murthy

    (Vignan’s University)

Abstract

In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.

Suggested Citation

  • K. Venkata Rao & P. B. G. S. N. Murthy, 2018. "Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1533-1543, October.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-016-1197-y
    DOI: 10.1007/s10845-016-1197-y
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    References listed on IDEAS

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    1. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    2. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    3. Vadlamani, Satish & Hosseini, Seyedmohsen, 2014. "A novel heuristic approach for solving aircraft landing problem with single runway," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 144-148.
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    Cited by:

    1. Juan Lu & Xiaoping Liao & Steven Li & Haibin Ouyang & Kai Chen & Bing Huang, 2019. "An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes," Complexity, Hindawi, vol. 2019, pages 1-13, June.
    2. Oussama Ghermoul & Hani Benguesmia & Loutfi Benyettou, 2022. "Development of a Flashover Voltage Prediction Model with the Pollution and Conductivity as Factors Using the Response Surface Methodology," Energies, MDPI, vol. 15(19), pages 1-11, September.
    3. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    4. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Pengcheng Shen, 2020. "Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1429-1441, August.

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