Simultaneous optimization of mold design and processing conditions in injection molding

Injection molding (IM) is considered the foremost process for mass-producing plastic products. One of the biggest challenges facing injection molders today is to determine the proper settings for the IM process variables. Selecting the proper settings for an IM process is crucial because the behavior of the polymeric material during shaping is highly influenced by the process variables. Consequently, the process variables govern the quality of the part produced. The difficulty of optimizing an IM process is that the performance measures (PMs), such as surface quality or cycle time, that characterize the adequacy of part, process, or machine to intended purposes, usually show conflicting behavior. Therefore, a compromise must be found between all of the PMs of interest. In the past, we have shown a method comprised of Computer Aided Engineering, Artificial Neural Networks, and Data Envelopment Analysis (DEA) that can be used to find the best compromises between several performance measures.. The analyses presented in this paper are geared to make informed decisions on the compromises of several performance measures. These analyses also allow for the identification of robust variable settings that might help to define a starting point for negotiation between multiple decision makers. Future work will include adding information about the variability of PMs on the DEA analysis and the determination of process windows with efficiency considerations. This paper discusses the application of this method to IM and how to exploit the results to determine robust process and design settings.