The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Abstract

AISI 1040 steel offers a wide range of industrial applications due to its mechanical characteristics and applicability. The present work investigates the wear performance of AISI 1040 steel under dry sliding conditions and its optimization using combined machine learning (ML) and metaheuristic algorithm. Sliding wear test were carried out on a pin-on-disc tribometer by varying the load (10–100 N), sliding speed (0.5–1.5 m/s) and sliding distance (400–1000 m). The test parameters were varied at three levels. Experiments were carried out following combinations in Taguchi's L27 orthogonal array (OA). Artificial neural network (ANN) was used to model the process parameters with wear rate. The trained network was optimized using genetic algorithm (GA) to predict optimal wear rate. This methodology has been termed as ANN-GA method. The results were compared with conventional Taguchi based and regression based GA optimization. A significant reduction in the wear rate could be realized due to optimization using ANN-GA method. This work also examined the corrosion behaviour of AISI 1040 steel exposed to 3.5% NaCl, 3.5% NaOH, 0.5 M H2SO4 and 3.5% NaCl + 0.5 M H2SO4 to simulate various corrosive environments encountered in industrial applications. A nobler corrosion potential was obtained in 3.5% NaOH. Investigations of corroded samples showed pitting corrosion in 3.5% NaCl, 0.5 M H2SO4 as well as combined chloride and sulphate attack. On the other hand, negligible corrosion was observed in 3.5% NaOH.

Development of Ni-B electroless coating from stabilizer free bath and characterization of high temperature tribological behaviour, scratch and corrosion resistance

“…The AISI 1040 steel containing 0.35-0.45 wt% C, 0.15-0.4 wt% Si, 0.6-0.9 wt% Mn, 0.05 wt% P and 0.04 wt% S [32,33] were used as base material. The AISI 1040 steel specimens were chosen because of their wide industrial usage [32,33]. Specimen preparation is an important aspect prior to electroless deposition.…”

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

“…Existing forecasting models, such as simple artificial neural networks (ANNs), have shown some success in this area [22][23][24][25], but there is a pressing need to explore new and potentially more accurate computational modeling approaches for predicting wear rates in these Al/SiC metal matrix composites (MMCs). The previous literature has focused on the implementation of using conventional machine learning models for predicting wear rate [26][27][28][29][30].…”

Prediction of friction coefficient and torque in self-lubricating polymer radial bearings produced by additive manufacturing: A machine learning approach

“…They utilized experimental data from various coatings under different operating conditions to train several ML algorithms, including artificial neural networks (ANN) and support vector machines (SVM), in order to predict the coefficient of friction. In their study, Agrawal and Mukhopadhyay 58 utilized ML techniques and metaheuristic algorithms to explore the corrosion resistance and optimize the wear performance of AISI 1040 steel under dry operating conditions.…”