Deep Learning Based Manufacturing Capability Modeling for Process Planning Automation
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Yan, Xiaoliang
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Abstract
Realizing the vision of a generative manufacturing process planning system requires a set of efficient computational tools that enable automated 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, realistic, and scalable 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 data-driven decision making at the process, shop, and marketplace levels. Specifically, the dissertation addresses the following research questions: (1) Can we learn and represent the shape, material, and part quality transformation capabilities of discrete manufacturing processes from design and manufacturing data as a latent probability distribution? (2) Can we develop data-driven computational tools to enable decision-making in key process planning steps ranging from process/operation selection to operations sequencing? (3) Can we enable efficient automated search of manufacturers based on a computed score for the match between the desired part and the manufacturer’s capabilities? To answer these research questions, this dissertation 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. From these studies, it has been demonstrated that (1) a three dimensional variational autoencoder generative adversarial network can model machining process shape transformation capabilities as latent probability distributions, which, when combined with a Siamese neural network, achieved 100% accuracy and area under the curve (AUC) of 1 in manufacturability analysis, as well as 89% class-average accuracy in automated process selection, outperforming baseline discriminative models; (2) a semantic segmentation method using generative pre-trained neural networks achieved over 96.8% intersection over union (IoU) for machinable volume identification in complex machined parts produced in a lathe; (3) a three-dimensional convolutional recurrent neural network model can be used to learn precedence relations in machining operations sequencing, outperforming a baseline binary classifier with 97.6% validation accuracy, and demonstrating practical applicability in validating operations sequences for realistic machined parts; (4) the proposed deep unsupervised part retrieval model improved part retrieval precision by incorporating manufacturing capability information, achieving a combined process and function class precision at 1 of 93.0%, which, when combined with federated learning, demonstrated an accuracy of 89% in identifying suppliers with non-overlapping manufacturing capabilities and a 87.8% accuracy when suppliers’ capabilities overlap. The findings and contributions of this research serve as key technology enablers for future generative manufacturing process planning systems.
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2024-10-08
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Dissertation (PhD)