SUMMARY

The ability to automatically identify manufacturing processes or their sequences capable of transforming a product design into its physical counterpart is a key requirement of cyber manufacturing services, and more generally in automated manufacturing process planning. Such function is usually achieved using Computer-Aided Process Planning (CAPP) systems with hard-coded rule-based knowledge of manufacturing capabilities, and such knowledge is usually derived from user experience. However, process capability knowledge embedded in most of the reported CAPP systems is usually incomplete and limited to one or very few manufacturing processes. In addition, the process capability knowledge may vary from system to system and is often difficult to scale as new knowledge becomes available. Modern data-driven or machine learning methods offer a potential solution to these challenges by providing the computational methods to infer core manufacturing process capability knowledge from digital design and manufacturing data. Motivated by this potential, this proposal seeks to (i) develop data-driven methods for learning the capability of different discrete manufacturing processes and their sequences from existing design and manufacturing data, and (ii) illustrate utilization of such capability knowledge in process selection and sequencing. It is expected that the proposed research will lead to computational methods necessary to acquire process capability knowledge that is self-updatable to adapt to the rapid technology advancements in manufacturing. Such adaptability is necessary to enable cyber manufacturing services.