Artificial Neural Network (ANN) Approach for Predicting Friction Coefficient of Roller Burnishing AL6061

Abstract

Abstract-Artificial Neural Network (ANN) approach is a fascinating mathematical tool, which can be used to simulate a wide variety of complex scientific and engineering problems. Due to its highly reliable prediction quality, the usage of it is growing rigorously and had already become an ultimate tool for various applications in the field of engineering. In this study an ANN technique was used to predict friction coefficient of roller burnishing AL6061 for two orientations which is parallel burnishing orientation (PB) and cross burnishing orientation (CB). The input parameters were defined by widths of roller curvature (7.5mm, 8mm and 8.5mm), burnishing speeds (110rpm, 230rpm, 330rpm and 490rpm), and burnishing forces (155.06N, 197.45N, 239.83N and 282.22N) while the output parameter was friction coefficient. 173 data was used for training the ANN and another 115 data was used to test the ANN. 60 different configurations of ANN was trained by using 6 different training algorithms. It was found that feed-forward back-propagation network with 15 neurons in hidden layer that was trained by Levenberg-Marquardt training algorithm gave the best result when compared to other training algorithms used. From the results it was found that the training performance and prediction performance was 0.000809 and 0.710 respectively. From this study, it became obvious that the selected ANN with the configuration and training algorithm proved to be the most suitable among the other ANN investigated for similar applications.Index Terms-Friction coefficient; neural network; roller burnishing AL6061.

Enhancing Surface Integrity and Quality through Roller Burnishing: A Comprehensive Review of Parameters Optimization, and Applications.

“…Stainless steel is widely used in the automotive and aeronautics industries. The impact of roller burnishing on the mechanical properties and surface quality of o1 alloy steel was examined by Khalid S. Rababa et al [34]. Following the experiment, the material's actual stress increased by roughly 150 MPa, and the surface quality increased by 12.5%.…”

Deep Rolling Techniques: A Comprehensive Review of Process Parameters and Impacts on the Material Properties of Commercial Steels

“…However, the adoption of ANN specifically for predicting deep rolling effects is not available in the open domain to the best knowledge of the authors of this review paper. Nevertheless, few studies are available on the implementation of ANNs for predicting the effects of burnishing, which is a similar process to that of deep rolling in principle and at least with that of attained surface topography [154][155][156][157].…”

“…Tang et al [155] used the ANN technique to predict the friction coefficient in the roller burnishing process for two different roller orientations. The roller curvature, burnishing speed, and force were considered as input parameters.…”