Multi-objective optimization of injection molding processes for truck wheel cover cross beams based on Bayesian regularized neural networks

ZHANG Han; WANG Mingwei; CAI Shiming; WANG Zongqiang; YU Junwei; YE Xinghui

doi:10.3969/j.issn.1001-3539.2025.10.013

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Multi-objective optimization of injection molding processes for truck wheel cover cross beams based on Bayesian regularized neural networks

Process Technology | 更新时间：2025-11-14

- Multi-objective optimization of injection molding processes for truck wheel cover cross beams based on Bayesian regularized neural networks
- “In the field of optimizing injection molding processes for large and complex plastic parts, experts have effectively solved multi-objective optimization problems using Bayesian regularized neural networks and non dominated sorting genetic algorithms, providing new solutions for improving the quality of plastic parts.”
- Engineering Plastics Application Vol. 53, Issue 10, Pages: 95-103(2025)
- 作者机构：
  
  1.大连工业大学机械工程与自动化学院，辽宁大连 116034
  2.浙江凯华模具有限公司，浙江台州 318020
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1001-3539.2025.10.013
  CLC： TQ320.66
- Received：10 September 2025，
  
  Revised：2025-09-01，
  
  Accepted：10 September 2025，
  
  Published：10 October 2025
- 稿件说明：
移动端阅览
张晗，王明伟，蔡世铭，等.基于贝叶斯正则化神经网络的卡车轮罩横梁注塑工艺多目标优化[J].工程塑料应用，2025，53(10)：95-103.

ZHANG Han，WANG Mingwei，CAI Shiming，et al. Multi-objective optimization of injection molding processes for truck wheel cover cross beams based on Bayesian regularized neural networks[J]. Engineering Plastics Application，2025，53(10)：95-103.
张晗，王明伟，蔡世铭，等.基于贝叶斯正则化神经网络的卡车轮罩横梁注塑工艺多目标优化[J].工程塑料应用，2025，53(10)：95-103. DOI： 10.3969/j.issn.1001-3539.2025.10.013.

ZHANG Han，WANG Mingwei，CAI Shiming，et al. Multi-objective optimization of injection molding processes for truck wheel cover cross beams based on Bayesian regularized neural networks[J]. Engineering Plastics Application，2025，53(10)：95-103. DOI： 10.3969/j.issn.1001-3539.2025.10.013.

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Multi-objective optimization of injection molding processes for truck wheel cover cross beams based on Bayesian regularized neural networks

DOI：10.3969/j.issn.1001-3539.2025.10.013