Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.05396 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914242610855936 |
|---|---|
| author | Li, Yezhuo Zhang, Fan Shinde, Dhanashree Zhang, Qiong Pradeep, Sai Pilla, Srikanth Li, Gang |
| author_facet | Li, Yezhuo Zhang, Fan Shinde, Dhanashree Zhang, Qiong Pradeep, Sai Pilla, Srikanth Li, Gang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05396 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty Analysis of Experimental Parameters for Reducing Warpage in Injection Molding Li, Yezhuo Zhang, Fan Shinde, Dhanashree Zhang, Qiong Pradeep, Sai Pilla, Srikanth Li, Gang Methodology Applications 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. |
| title | Uncertainty Analysis of Experimental Parameters for Reducing Warpage in Injection Molding |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2601.05396 |