Machine Learning-Based Cyber Manufacturing Services: A Review of Manufacturing Process Selection, Process Planning, and Design for Manufacturing

Cyber manufacturing services, which aim to connect geographically distributed designers and manufacturing service providers via the internet, are emerging to address the market shift from mass production to mass personalization. Recent advances in the Internet of Things (IoT) and machine learning enable new capabilities that promise improved efficiencies across the cyber manufacturing ecosystem. In this paper, we focus on machine learning methods that facilitate cyber manufacturing services in the areas of manufacturing process planning and design for manufacturing (DFM). To enable automated manufacturing process planning, we review recent advances in manufacturing capability modeling, manufacturing process selection, and feature recognition for process planning. To facilitate DFM, data-driven tools for generative design are reviewed and new methods and results presented. In the context of the literature review, we summarize work from our research group and present some new methods and results in the DFM area. Critical summaries of research challenges are provided to set the stage for recommendations on future research directions toward realizing cyber manufacturing services.