Evolution of machine learning and deep learning in intelligent manufacturing: a bibliometric study

Technological advancements are reshaping traditional industrial processes, leading to the rapidly evolving landscape of intelligent manufacturing. In this context, machine learning and deep learning are articulated to revolutionize the complete lifecycle of products from design to production and delivery. Therefore, this study aims to provide a comprehensive overview of intelligent manufacturing practices by integrating machine learning and deep learning techniques. It employed robust bibliometric analysis over the 401 documents in the pertinent literature mined from the Scopus database. It delivers key insights on (i) pivotal journals, influential authors, and network mapping; (ii) delineation of theme-based clusters from keyword co-occurrences; and (iii) formulation of a futuristic research framework for scholars and practitioners. This domain demonstrates an increasing research trend from the articles published each year, with "IEEE Access" documenting the highest publications in this domain. The findings of this study illuminate the temporal trends and contemporary relevance within the domain of intelligent manufacturing by identifying the five clusters based on the keyword occurrence. Besides, the theoretical implications, managerial implications, and future research directions provide a roadmap for future scholars to explore and contribute to an enhanced understanding of machine learning and deep learning driven intelligent manufacturing practices.