Uncertainty Analysis of Experimental Parameters for Reducing Warpage in Injection Molding

TL;DR

This paper introduces a data-driven, Bayesian framework for optimizing injection molding parameters to reduce warpage while quantifying uncertainty, leading to more robust process settings and better defect boundary visualization.

Contribution

It presents a novel combination of polynomial regression surrogates, Bayesian inference, and Monte Carlo boundary analysis for uncertainty-aware process optimization in injection molding.

Findings

Identifies stable process parameters for minimal warpage.

Provides confidence bands for defect boundary regions.

Demonstrates improved robustness over traditional methods.

Abstract

Injection molding is a critical manufacturing process, but controlling warpage remains a major challenge due to complex thermomechanical interactions. Simulation-based optimization is widely used to address this, yet traditional methods often overlook the uncertainty in model parameters. In this paper, we propose a data-driven framework to minimize warpage and quantify the uncertainty of optimal process settings. We employ polynomial regression models as surrogates for the injection molding simulations of a box-shaped part. By adopting a Bayesian framework, we estimate the posterior distribution of the regression coefficients. This approach allows us to generate a distribution of optimal decisions rather than a single point estimate, providing a measure of solution robustness. Furthermore, we develop a Monte Carlo-based boundary analysis method. This method constructs confidence bands for the zero-level sets of the response surfaces, helping to visualize the regions where warpage transitions between convex and concave profiles. We apply this framework to optimize four key process parameters: mold temperature, injection speed, packing pressure, and packing time. The results show that our approach finds stable process settings and clearly marks the boundaries of defects in the parameter space.