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September 10, 2025Open Access

Use of Machine Learning Algorithms to Predict Almen (Shot Peening) Intensity Values of Various Steel Materials

MİMurat İnceIsparta University of Applied Sciences HÖHatice Varol ÖzkavakIsparta University of Applied Sciences

Key Points

The use of a deep neural network model achieved an R2 value of 0.9665 for predicting Almen intensity.
Shot peening parameters were optimized to enhance hardness and surface properties of various steel materials.
Machine learning methods demonstrated significant improvements in predicting compressive residual stress in shot peening processes.
The application of artificial intelligence could decrease the need for extensive experimental trials in process optimization.

Abstract

Wear, fatigue, and corrosion are just a few of the issues that mechanical components in engineering experience, leading to surface deterioration. Enhancing the surface characteristics of engineering components is therefore essential. The surface properties of engineering objects can be improved by applying different surface treatments. One of these processes is shot peening (SP). Process parameters are crucial for SP. This necessitates the optimization of SP process parameters. In this study, we applied SP and vibratory shot peening (VSP) processes to different steel materials (AISI 8620, AISI 5140, AISI 4140, and AISI 1020) using different process parameters, aiming to determine the effects of these parameters on hardness, residual stress, and surface roughness. The highest compressive residual stress (CRS) and hardness values for shot-peened samples were obtained at the 24–26 A intensity for all steels. For all steel-group VSP samples, the highest CRS and hardness values were obtained at the 60 s −4 mm parameter. This paper aims to predict Almen intensity values using CRS, surface roughness, and hardness values from various steels. The supplied experimental data was utilized to estimate the SP Almen intensity value using a number of machine learning (ML) methods, eliminating the need for costly and time-consuming experimentation. With an RMSE of 0.0731, R2 of 0.9665, and MAE of 0.0613, the deep neural network (DNN) surpassed the other models in terms of prediction accuracy. The results indicate that artificial intelligence technology could be utilized to accurately evaluate Almen intensity.

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