dorsal/arxiv
View SchemaUncertainty Analysis of Experimental Parameters for Reducing Warpage in Injection Molding
| Authors | Yezhuo Li, Fan Zhang, Dhanashree Shinde, Qiong Zhang, Sai Pradeep, Srikanth Pilla, Gang Li |
|---|---|
| Categories | |
| ArXiv ID | 2601.05396vv1 |
| URL | https://arxiv.org/abs/2601.05396 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"date_created": "2026-02-17T05:53:04.779000Z",
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"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.",
"arxiv_id": "2601.05396",
"authors": [
"Yezhuo Li",
"Fan Zhang",
"Dhanashree Shinde",
"Qiong Zhang",
"Sai Pradeep",
"Srikanth Pilla",
"Gang Li"
],
"categories": [
"stat.ME",
"stat.AP"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Uncertainty Analysis of Experimental Parameters for Reducing Warpage in Injection Molding",
"url": "https://arxiv.org/abs/2601.05396",
"version": "v1"
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