Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine

Zi-Ning Li; Xiao-Qing Tian; Dingyifei Ma; Shahid Hussain; Lian Xia; Jiang Han

doi:10.1007/s10118-025-3306-x

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Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine

RESEARCH ARTICLE | Updated：2025-07-14

- Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine
- Chinese Journal of Polymer Science Vol. 43, Issue 5, Pages: 848-862(2025)
- Affiliations：
  
  a.School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China
  b.Anhui Engineering Laboratory of Intelligent CNC Technology and Equipment, Hefei 230009, China
- Author bio：
  
  tianxiaoqing@hfut.edu.cn (X.Q.T.)
  Jianghan@hfut.edu.cn (J.H.)
- Funds：
- DOI：10.1007/s10118-025-3306-x
  CLC：
- Received：22 November 2024，
  
  Revised：2025-01-09，
  
  Accepted：22 January 2025，
  
  Online First：13 March 2025，
  
  Published：30 April 2025
- Accepted：
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Li, Z. N.; Tian, X. Q.; Ma, D. Y. F.; Hussain, S.; Xia, L.; Han, J. Optimization of extrusion-based silicone additive manufacturing process parameters based on improved kernel extreme learning machine. Chinese J. Polym. Sci. 2025, 43, 848–862

Zi-Ning Li, Xiao-Qing Tian, Dingyifei Ma, et al. Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine[J]. Chinese Journal of Polymer Science, 2025, 43(5): 848-862.
Li, Z. N.; Tian, X. Q.; Ma, D. Y. F.; Hussain, S.; Xia, L.; Han, J. Optimization of extrusion-based silicone additive manufacturing process parameters based on improved kernel extreme learning machine. Chinese J. Polym. Sci. 2025, 43, 848–862 DOI： 10.1007/s10118-025-3306-x.

Zi-Ning Li, Xiao-Qing Tian, Dingyifei Ma, et al. Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine[J]. Chinese Journal of Polymer Science, 2025, 43(5): 848-862. DOI： 10.1007/s10118-025-3306-x.

摘要

To enhance the quality of silicone printed specimens and reduce production costs

an improved KELM machine learning model combined with a genetic algorithm was developed. Experiments show this algorithm's prediction accuracy surpasses others

with printed samples' performance improving post-optimization.

Abstract

Silicone material extrusion (MEX) is widely used for processing liquids and pastes. Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation

products may exhibit geometric errors and performance defects

leading to a decline in product quality and affecting its service life. This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs. To improve the quality of silicone printing samples and reduce production costs

three machine learning models

kernel extreme learning machine (KELM)

support vector regression (SVR)

and random forest (RF)

were developed to predict these three factors. Training data were obtained through a complete factorial experiment. A new dataset is obtained using the Euclidean distance method

which assigns the elimination factor. It is trained with Bayesian optimization algorithms for parameter optimization

the new dataset is input into the improved double Gaussian extreme learning machine

and finally obtains the improved KELM model. The results showed improved prediction accuracy over SVR and RF. Furthermore

a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model. The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.

关键词

Keywords

references

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