Abstract

Design space exploration and optimization in injection molding is usually an ad-hoc process dependent upon the expertise of the user. Additionally, due to the lack of deterministic equations connecting the process inputs and outputs, the design space is not well understood and gradient based optimization methods cannot be directly applied. In this work, we demonstrate the improvement in the process output of an injection molding process using automated response surface modelling and optimization. This work utilizes collaborative simulation between OmniQuest™ Iliad™ Design Exploration & Automation Studio and Autodesk® Moldflow Insight®. A simulation model is first created in Moldflow Insight with process setting values based on user judgement. The input and output process parameters contained in this simulation are imported into Iliad through a dedicated interface. Next, a Latin Hypercube design of experiments is created in Iliad for five process settings (melt and mold temperatures, cooling time and gate location (X & Y coordinates)) and three outputs (maximum volumetric shrinkage, mass, and maximum clamping force) selected from the imported process settings. This results in 40 design points which are evaluated in Moldflow Insight, executed via Iliad. The resulting dataset is then used to fit a second order response surface model. An optimization study is then run using Iliad’s optimization component and the surrogate model to minimize the volumetric shrinkage of the finished part. Gradient based constrained optimization is run using the modified method of feasible direction method. This is enabled by the DOT optimization engine developed by OmniQuest. Results show a reduction of 32% in the maximum volumetric shrinkage compared to the initial solution, demonstrating improved process performance. Additionally, the process of exporting resulting plots is also automated using a Moldflow macro and the Python script component in Iliad, encapsulating the entire design workflow in a single platform.