Abstract
Critical design decisions are often made during the detailed design stage assuming known material and process behavior. However, in net shape manufacturing processes such as stamping, injection molding, and metals casting, the final part properties depend upon the specific tool geometry, material properties, and process dynamics encountered during production. As such, the end-use performance can not be accurately known in the detailed design stage. Moreover, slight random variations during manufacture can inadvertently result in inferior or unacceptable product performance and reduced production yields. These characteristics make it difficult for the designer to select the tooling, material, and processing details which will deliver the desired functional properties, let alone achieve a robust design which is tolerant to process variation.
This paper describes a methodology for assessing the design/manufacturing robustness of candidate designs at the detailed design stage. In the design evaluation, the fundamental sources of variation are explicitly modeled and the effects conveyed through the manufacturing process to predict the distribution of end-use part properties. This is accomplished by utilizing optimization of manufacturing process variables within Monte Carlo simulation of stochastic process variation, which effectively parallels the industry practice of tuning and optimizing the process once the tool reaches the production floor. The resulting estimates can be used to evaluate the robustness of the candidate design relative to the product requirements and provide guidance for design and process modifications before tool steel is cut, as demonstrated by the application of the methodology for dimensional control of injection molded parts.
Pattern Recognition for Manufacturing Process Variation using Ensembled Artificial Neural Network
