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A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters

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Abstract

The abrupt changes in tool-workpiece interaction during machining process induce variation in the surface quality of work material. These interactions include built-up edge formation and their break-off, environmental conditions (use of coolant, rise of temperature etc.), material imperfections, improper structural fitness of machine & tool components, etc. This study presents prediction of surface roughness in turning of EN353 steel implementing the variational mode decomposition (VMD) for processing the vibration data, followed by estimation of the surface roughness using the relevance vector regression (RVR) optimized by particle swarm optimization (PSO). The raw vibration data has been decomposed in five discrete sets of frequency components known as variational mode functions (VMFs). A set of twenty-one statistical features in each three axes have been extracted for raw data and each VMF. The RVR has been trained using these 21×3 = 63 features and 3 cutting parameters — cutting speed, feed depth of cut. The RVR has also been trained separately using top 5 features selected through RreliefF algorithm. The optimal decomposition level has been determined to minimize the noise and predict the surface finish accurately. The results obtained in 1st VMF (high frequency, low amplitude) using its top 5 features for prediction have been found to be reliable with higher prediction accuracy.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, 148106, India

    Vikrant Guleria, Vivek Kumar & Pradeep K. Singh

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  1. Vikrant Guleria
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  2. Vivek Kumar
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  3. Pradeep K. Singh
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Corresponding author

Correspondence to Pradeep K. Singh.

Additional information

Vikrant Guleria received the B.Tech. in Mechanical Engineering from Himachal Pradesh University, Shimla, India, in 2010, and the M.E. in Manufacturing Technology from the National Institute of Technical Teachers Training and Research, Chandigarh, India, in 2017. He is currently pursuing the Ph.D. from Sant Longowal Institute of Engineering and Technology, Longowal, India. Mr. Guleria has around 6 years of professional experience in teaching and industry. His current research interests include signal processing, condition monitoring, vibration analysis, machine learning, and conventional machining processes.

Vivek Kumar is an Assistant Professor in the Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur, India. He obtained his Ph.D. in the Mechanical Engineering in field of railway dynamics. Dr. Kumar has around 15 years of professional experience in teaching and research. His research interest includes Rail and vehicle dynamics, structural dynamics, metal machining, fault diagnosis & Vibration analysis.

Pradeep Kumar Singh is a Professor of Mechanical Engineering, at Sant Longowal Institute of Engineering & Technology, Longowal. He has also served Encardio-rite Electronics (P) Ltd., Lucknow, and Scooters India Ltd., Lucknow for a small span of time. He received B. Tech. in Mechanical Engineering from the Institute of Engineering & Technology (IET), Lucknow, in 1990, M. Tech. in Mechanical Engineering (specialization in Production Engineering) from the Institute of Technology, Banaras Hindu University (IT-BHU, now IIT), Varanasi, in 1992, and Ph.D. in Mechanical Engineering from the Indian Institute of Technology (IIT), Roorkee, in 2005. Dr. Singh has about 30-year of professional experience in teaching, industry and research. He is an Associate Editor of the “Insight Mechanical Engineering”, and has been the guest editor to International Journal of Applied Engineering Research, and International Journal of Engineering Studies, in the past. His research interests include tolerance design, metal machining, advanced optimization and modeling & simulation of mechanical systems, waste management & energy recovery, aluminum composites, polymer composites, etc.

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Guleria, V., Kumar, V. & Singh, P.K. A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters. J Mech Sci Technol 36, 2775–2785 (2022). https://doi.org/10.1007/s12206-022-0510-2

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  • DOI: https://doi.org/10.1007/s12206-022-0510-2

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