Using Bayesian optimization for warpage compensation in injection molding

Abstract

In injection molding, shrinkage and warpage lead to a deformation of the produced parts with respect to the cavity shape. One method to mitigate this effect is to adapt the cavity shape to the expected deformation. This deformation can be determined using appropriate simulation models, which then also serve as a basis for determining the optimal cavity shape. Shape optimization usually requires a sequence of forward simulations, which can be computationally expensive. To reduce this computational cost, we use Bayesian optimization which uses Gaussian process regression as a reduced order model. Additionally, Gaussian process regression has the benefit that it allows to account for uncertainty in the model parameters and thus provides a means to investigate their influence on the optimization result. We present a Gaussian process regression trained with samples from a finite‐element solid‐body model. It predicts the deformation of the product after solidification and, together with Bayesian optimization, allows for efficient cavity optimization.

Comparison of Numerical Methods for Geometric Warpage Compensation

“…In the frame of robust optimization, BO is strongly interlinked with Gaussian process regression. How BO can be used in cavity design is described in detail in [26]. Here, we give a brief recap.…”

“…Both the reverse geometry method and the normal vector method by Kastelic et al [25] require very few iterations to compute the cavity shape. Method (4): Another approach is to formulate the problem as a shape optimization task [26]. This can be performed in many different ways, e.g., the shape parameterization can be parametric [27] or nonparametric [28][29][30].…”