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

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 K. Dragomiretskiy, "Variational mode decomposition" 62 (62): 531-544, 2014

2 C. Beggan, "Using acoustic emission to predict surface quality" 15 (15): 737-742, 1999

3 W. A. Woyczyński, "Uncertainty principle and wavelet transforms" 57-90, 2019

4 M. E. Tipping, "The relevance vector machine" 653-658, 2000

5 S. W. Fei, "The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction" 17 (17): 583-590, 2020

6 K. Kira, "The feature selection problem:traditional methods and a new algorithm" 129-134, 1992

7 C. P. Jesuthanam, "Surface roughness prediction using hybrid neural networks" 11 (11): 271-286, 2007

8 B. Samanta, "Surface roughness prediction in machining using soft computing" 22 (22): 257-266, 2009

9 E. G. Plaza, "Surface roughness monitoring by singular spectrum analysis of vibration signals" 84 : 516-530, 2017

10 R. J. Urbanowicz, "Relief-based feature selection : introduction and review" 85 : 189-203, 2018

11 D. Kong, "Relevance vector machine for tool wear prediction" 127 : 573-594, 2019

12 X. Wang, "Predictive modeling of surface roughness in lenses precision turning using regression and support vector machines" 87 (87): 1273-1281, 2016

13 S. V. Prasad, "Prediction of surface roughness in turning of EN19 steel using acoustic emission" Springer Singapore 113-122, 2019

14 B. Bhardwaj, "Prediction of surface roughness in turning of EN 353 using response surface methodology" 67 (67): 305-313, 2014

15 H. H. Shahabi, "Prediction of surface roughness and dimensional deviation of workpiece in turning : a machine vision approach" 48 (48): 213-226, 2010

16 K. A. Risbood, "Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process" 132 (132): 203-214, 2003

17 H. Fattahi, "Prediction of blast-induced ground vibration in a mine using relevance vector regression optimized by metaheuristic algorithms" 30 : 1849-1863, 2021

18 G. Vashishtha, "Pelton wheel bucket fault diagnosis using improved shannon entropy and expectation maximization principal component analysis" 10 : 335-349, 2022

19 S. Kiranyaz, "Particle swarm optimization" 15 : 45-82, 2014

20 Z. Hessainia, "On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations" 46 (46): 1671-1681, 2013

21 S. Chauhan, "Mutation-based arithmetic optimization algorithm for global optimization" 1-6, 2021

22 K. He, "Modeling and predicting surface roughness in hard turning using a bayesian inference-based HMM-SVM model" 12 (12): 1092-1103, 2015

23 M. K. Gupta, "Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum-quantity lubrication environment" 31 (31): 1671-1682, 2016

24 S. Tamang, "Integrated optimization methodology for intelligent machining of Inconel 825 and its shop-floor application" 39 (39): 865-877, 2017

25 C. L. He, "Influencing factors and theoretical modeling methods of surface roughness in turning process : state-of-the-art" 129 : 15-26, 2018

26 D. R. Salgado, "In-process surface roughness prediction system using cutting vibrations in turning" 43 (43): 40-51, 2009

27 V. Upadhyay, "In-process prediction of surface roughness in turning of Ti-6Al-4V alloy using cutting parameters and vibration signals" 46 (46): 154-160, 2013

28 M. S. Chen, "Fuzzy clustering analysis for optimizing fuzzy membership functions" 103 (103): 239-254, 1999

29 I. Kononenko, "Estimating attributes: analysis and extensions of RELIEF" 171-182, 1994

30 E. D. Kirby, "Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations" 30 (30): 592-604, 2006

31 M. Pour, "Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform" 97 (97): 2603-2619, 2018

32 V. Guleria, "Classification of surface roughness during turning of forged EN8 steel using vibration signal processing and support vector machine" 4 (4): 015029-, 2022

33 S. Chauhan, "Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy" 179 : 109445-, 2021

34 E. G. Plaza, "Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations" 98 : 902-919, 2018

35 Y. V. Deshpande, "Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718" 1 (1): 1-9, 2019

36 E. G. Plaza, "Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning" 98 : 634-651, 2018

37 N. Xie, "An energy-based modeling and prediction approach for surface roughness in turning" 96 (96): 2293-2306, 2018

38 G. Vashishtha, "An effective health indicator for the pelton wheel using a levy flight mutated genetic algorithm" 32 (32): 094003-, 2021

39 S. Chauhan, "An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm" 2020

40 G. Vashishtha, "An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel" 187 : 110272-, 2022

41 M. Robnik-Šikonja, "An adaptation of relief for attribute estimation in regression" 5 : 296-304, 1997

42 A. Kumar, "Adaptive sensitive frequency band selection for VMD to identify defective components of an axial piston pump" 35 (35): 250-265, 2022

43 H. Wang, "A theoretical and experimental investigation of the tool-tip vibration and its influence upon surface generation in single-point diamond turning" 50 (50): 241-252, 2010

44 S. Chauhan, "A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem" 78 : 6234-6274, 2022

45 S. C. Lin, "A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning" 38 (38): 763-782, 1998

46 W. Duch, "A new methodology of extraction, optimization and application of crisp and fuzzy logical rules" 12 (12): 277-306, 2001

47 L. J. Song, "A dynamic multi-swarm particle swarm optimizer for multi-objective optimization of machining operations considering efficiency and energy consumption" 13 (13): 2616-, 2020