A bibliometric review on application of machine learning in additive manufacturing and practical justification

Abstract

This paper delves into the cutting-edge applications of Machine Learning (ML) within modern Additive Manufacturing (AM), employing bibliometric analysis as its methodology. Formulated around three pivotal research questions, the study navigates through the current landscape of the research field. Utilizing data sourced from Web of Science, the paper conducts a comprehensive statistical and visual analysis to unveil underlying patterns within the existing literature. Each category of ML techniques is elucidated alongside its specific applications, providing researchers with a holistic overview of the research terrain and serving as a practical checklist for those seeking to address particular challenges. Culminating in a vision for the Smart Additive Manufacturing Factory (SAMF), the paper envisions seamless integration of reviewed ML techniques. Furthermore, it offers critical insights from a practical standpoint, thereby facilitating shaping future research directions in the field.