可制造性设计
过程(计算)
转化(遗传学)
计算机科学
机械加工
模型转换
生成语法
质量(理念)
工程制图
人工智能
制造工程
工程类
机械工程
生物化学
化学
基因
哲学
一致性(知识库)
认识论
操作系统
作者
Xiaoliang Yan,Shreyes N. Melkote
标识
DOI:10.1016/j.mfglet.2022.07.098
摘要
The shape, material property, and part quality transformation capabilities of a manufacturing process are essential process capability knowledge that are traditionally acquired by process planners through experience. While efforts have been made over the years to develop automated systems that utilize known process capabilities for process selection and manufacturability assessment of part designs, such systems are hampered by the lack of a systematic approach to capture and model the shape, material property, and part quality transformation capabilities from design and manufacturing data. In this paper, the shape transformation capabilities of representative machining operations are modeled using 3D Variational Autoencoders and Generative Adversarial Networks (3D-VAE-GANs.) The proposed approach models the shape transformation capability as a latent probability distribution from which visualizations of realistic machinable features can be sampled for shape decomposition and reconstruction, thereby assisting machining process selection by a process planner and manufacturability assessment of part shapes generated by a designer.
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