Generative Modeling of the Shape Transformation Capability of Machining Processes
Generative Modeling of the Shape Transformation Capability of Machining Processes
复制标题
加工过程形状变换能力的生成建模
DOI：
10.1016/j.mfglet.2022.07.098
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Melkote, Shreyes
中科院分区：
文献类型：
--
作者：
Yan, Xiaoliang;Melkote, Shreyes
制造过程的形状、材料特性和零件质量转换能力是传统上由过程计划人员通过经验获得的基本过程能力知识。虽然多年来人们一直在努力开发自动化系统，利用已知的工艺能力来进行工艺选择和零件设计的可制造性评估，但由于缺乏从设计和制造数据中捕获和建模形状、材料特性和零件质量转换能力的系统方法，这些系统受到了阻碍。本文采用三维变分自编码器和生成对抗网络（3D- vae - gans）对典型加工工序的形状变换能力进行了建模。该方法将形状转换能力建模为一个潜在的概率分布，从中可以采样真实可加工特征的可视化以进行形状分解和重构，从而帮助工艺规划者选择加工工艺，并帮助设计人员对零件形状进行可制造性评估。©2022制造工程师协会。Elsevier Ltd.出版。版权所有。
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.© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.