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IDENTIFICATION OF MANUFACTURABILITY CONSTRAINTS THROUGH PROCESS SIMULATION AND DATA MINING

机译:通过流程仿真和数据挖掘识别可制造性约束

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With continued demands on high-quality and low-cost products, designers are increasingly required to explore the use of new materials and manufacturing processes. The design for manufacturability rules for unconventional materials and processes will be of great value to improve component manufacturability. Instead of going through years of trial-and-error practice to gain some "rule of thumb" design guidelines, this paper proposed a knowledge-based computational method for manufacturability constraint modeling (MCM) through process simulation, design of experiment, and data mining. With the input of geometric attributes for local critical features of a component, the pre-trained manufacturability constraint model will output the manufacturability prediction and the confidence of the prediction. The 2D visualization of the manufacturability prediction facilitates the interpretation by the human designers, and provides her with concurrent and intuitive manufacturability feedback and directed re-design suggestions. The preliminary result on mild steel stamping process demonstrated the feasibility of the method.
机译:随着对高质量和低成本产品的要求,设计师越来越需要探索新材料和制造过程的使用。无传统材料和流程的可制造性规则的设计将具有重要的价值,以提高组件可制造性。而不是经历多年的试用练习来获得一些“经验法则”设计指南,而是通过工艺模拟,实验设计和数据挖掘设计,提出了一种基于知识的可制造限制建模(MCM)的计算方法。通过对组件的局部关键特征的几何属性的输入,预先训练的可制造性约束模型将输出可制造性预测和预测的置信度。制造性预测的2D可视化促进了人类设计人员的解释,并为她提供了并发和直观的可制造性反馈和定向重新设计建议。温和钢冲压过程的初步结果证明了该方法的可行性。

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