Deep Learning based Manufacturing Capability Modeling for Process Planning Automation

Realizing the vision of a generative manufacturing process planning system requires a set of efficient computational tools that enable automation of decision making at different stages of the design-to-product pipeline. While efforts have been made over the years to develop automated systems for feature recognition, shape retrieval, process selection, and manufacturability assessment of part designs, such methods are hampered by the lack of a systematic approach to model and integrate the knowledge required to enable decision-making at different granularity levels of a manufacturing system (e.g., process level, shop level, and online marketplace level). Specifically, methods to date have relied on simplistic and sometimes ad-hoc classification and/or encoding of manufacturing process capability knowledge, which are incomplete or too simplistic, difficult to scale, and heavily reliant on human expertise. Recent advances in machine learning and especially deep learning have shed light on potential pathways for data-driven inference of manufacturing process capabilities from existing design and manufacturing data of successfully produced parts. This dissertation presents a coherent set of research efforts to develop deep learning-based computational tools to enable a generative manufacturing system at the process, shop, and marketplace levels. Specifically, this research presents: (1) a deep embedding modeling approach for inferring the shape, material properties, and part quality transformation capabilities of machining processes/operations from historical design and manufacturing data as a latent probability distribution to enable automated process/operation selection and manufacturability assessment at the process level; (2) a deep-learning based part geometry segmentation approach to segment and label machinable volumes with candidate machining operations; (3) a deep sequence learning approach to automatically learn precedence relations among machining operations to enable automated operations sequencing at the shop level; and, (4) at the online marketplace level, a deep unsupervised learning-based manufacturing capability model to enable efficient process-aware part retrieval, which when combined with federated learning enables an implicit and secure manufacturer search without the need for a centralized parts database. The contributions of this research serve as key technology enablers for future generative manufacturing systems.

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