Incorporating machine learning in shot peening and laser peening: A review and beyond

Abstract

While shot peening and laser peening are effective in improving the mechanical properties of material surfaces, their process optimization and quality assessment in advanced manufacturing still present significant challenges. Traditional optimization and evaluation methods rely on simplistic regression and hypothetical models, which tend to lead to unreliable results. In the macro-era context of intelligent manufacturing, the progressive machine learning has already had a profound impact in this field. This paper systematically reviews the machine learning methods that have been used in recent years for process optimization and quality assessment in shot peening and laser peening. These algorithms have played a crucial role in predicting surface quality characteristics, optimizing key process parameters, and achieving significant performance improvements. The primary objective of this paper is to summarize the core ideas of these works and offer a structured critique of their effectiveness. In addition, this paper critically discusses some of the emerging challenges associated with machine learning-driven quality assessment in surface peening. By analyzing these challenges and future directions in detail, researchers and engineers alike will gain important insights into the continuous optimization and quality control of the surface peening process.