The Modelling of Surface Roughness after the Ball Burnishing Process with a High-Stiffness Tool by Using Regression Analysis, Artificial Neural Networks, and Support Vector Regression

Zeljko Kanovic,Djordje Vukelic,Branko Tadic,Miljana Prica,Goran Simunovic,Tomislav Saric,Katica Simunovic

doi:10.3390/met12020320

Zeljko Kanovic, Djordje Vukelic + Show 5 more

Open Access

https://doi.org/10.3390/met12020320

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Abstract

Surface roughness is an important indicator of the quality of the machined surface. One of the methods that can be applied to improve surface roughness is ball burnishing. Ball burnishing is a finishing process in which a ball is rolled over the workpiece surface. Defining adequate input variables of the ball burnishing process to ensure obtaining the required surface roughness is a typical problem in scientific research. This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball. The experiments were conducted following a randomized full factorial design for different levels of input variables. The input variables included the initial arithmetic mean roughness (the initial surface roughness), the depth of ball penetration, the burnishing feed, and the burnishing ball diameter, while the output variable was the arithmetic mean roughness after ball burnishing (the final surface roughness). The surface roughness modeling was performed based on the experimental results, using regression analysis (RA), artificial neural network (ANN), and support vector regression (SVR). The regression model displayed large prediction errors at low surface roughness values (below 1 μm), but it proved to be reliable for higher roughness values. The ANN and SVR models have excellently predicted roughness across a range of input variables. Mean percentage error (MPE) during the experimental training research was 29.727%, 0.995%, and 1.592%, and MPE in the confirmation experiments was 34.534%, 1.559%, and 2.164%, for RA, ANN, and SVR, respectively. Based on the obtained MPEs, it can be concluded that the application of ANN and SVR was adequate for modeling the ball burnishing process and prediction of the roughness of the treated surface in terms of the possibility of practical application in real industrial conditions.

Highlights

Ball burnishing is a process in which the ball rolls on the surface and causes elastic, elastoplastic, and plastic deformations of the surface layer of the workpiece [1]
This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball
This study aims to evaluate the surface roughness after the ball burnishing process with a high-stiffness tool based on a constant penetration depth of the ceramic ball

Summary

Introduction

Ball burnishing is a process in which the ball rolls on the surface and causes elastic, elastoplastic, and plastic deformations of the surface layer of the workpiece [1]. The study of ball burnishing processes based on experimental data in combination with modeling methods is the most promising for future research This can reduce costs, minimize the subjective influence of the operator, and improve the performance of the ball burnishing process in the context of obtaining the necessary output variables. The surface roughness was modeled, and its prediction was performed in a wide range of input variables, even for those processing parameters for which no experimental research was conducted. These three methods were compared based on the accuracy of the prediction of the output variable of the ball burnishing process.

Regression Modelling

Artificial Neural Networks

Support Vector Regression

Training Experiment

Confirmation Experiment

Findings

Discussion