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.

翻译：注塑成型是一种关键的制造工艺，但由于复杂的热力相互作用，控制翘曲变形仍是一个主要挑战。基于仿真的优化方法被广泛用于解决此问题，然而传统方法往往忽略了模型参数的不确定性。本文提出一种数据驱动框架，旨在最小化翘曲变形并量化最优工艺设置的不确定性。我们采用多项式回归模型作为盒形件注塑成型仿真的代理模型。通过采用贝叶斯框架，我们估计了回归系数的后验分布。该方法使我们能够生成最优决策的分布而非单一的点估计，从而提供解决方案稳健性的度量。此外，我们开发了一种基于蒙特卡洛的边界分析方法。该方法为响应面的零水平集构建置信带，有助于可视化翘曲变形在凸面与凹面轮廓之间转变的参数区域。我们将此框架应用于优化四个关键工艺参数：模具温度、注射速度、保压压力和保压时间。结果表明，我们的方法能够找到稳定的工艺设置，并在参数空间中清晰标定缺陷边界。