Machine Learning Driven Prediction, Optimization and Control in Wire Arc Additive Manufacturing: A Comprehensive Review

Industry 4.0 has led to an implementation of machine learning which has become a breakthrough in various fields by facilitating more efficient data processing, simulating the machine learning process and generalizes the knowledge from existing data to make pre-dictions which in result improvises the system accuracy. In advanced manufacturing processes, ML plays an important role in enhancing process efficiency, optimizing various input parameters, reducing experimental works along with stable quality. Wire arc additive man-ufacturing is a metal additive manufacturing technique which produces near net shape components with few challenges like parameter optimization and defects. Incorporating ML into WAAM provides a better solution in predicting output parameters and optimizing input parameters. This review consolidates the recent studies on using ML techniques in WAAM. It highlights their application in predicting response parameters, Geometric parameters, mechanical properties, microstructures and input optimization. Comparative analyses show the effectiveness of various algorithms along with their strengths and limitations. This study suggests the future research directions by analysing the recent accomplishments in the available literature, it aims to integrate ML and WAAM to highlight its potential for process modelling, overall process optimization and prediction.