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
- Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine
- 高分子科学（英文） 2025年43卷第5期页码：848-862
- 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：
  
  This study was financially supported by the National Key R&D Program of China (No. 2022YFA1005204l).
- DOI：10.1007/s10118-025-3306-x
  中图分类号：
- 收稿：2024-11-22，
  
  修回：2025-01-09，
  
  录用：2025-01-22，
  
  网络首发：2025-03-13，
  
  纸质出版：2025-04-30
- 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

Liravi, F.; Toyserkani, E. A hybrid additive manufacturing method for the fabrication of silicone bio-structures: 3D printing optimization and surface characterization. Mater. Des. 2018 , 138 , 46−61..

Plott, J.; Tian, X.; Shih, A. J. Voids and tensile properties in extrusion-based additive manufacturing of moisture-cured silicone elastomer. Addit. Manuf. 2018 , 22 , 606−617..

Herzberger, J.; Sirrine, J. M.; Williams, C. B.; Long, T. E. Polymer design for 3d printing elastomers: recent advances in structure, properties, and printing. Prog. Polym. Sci. 2019 , 97 , 101144..

Lopes, P. A.; Fernandes, D. F.; Silva, A. F.; Marques, D. G.; Almeida, A. T. de; Majidi, C.; Tavakoli, M. Bi-Phasic Ag–In–Ga-embedded elastomer inks for digitally printed, ultra-stretchable, multi-layer electronics. ACS Appl. Mater. Interfaces 2021 , 13 , 14552−14561..

Hui, Y.; Yao, Y.; Qian, Q.; Luo, J.; Chen, H.; Qiao, Z.; Yu, Y.; Tao, L.; Zhou, N. Three-dimensional printing of soft hydrogel electronics. Nat. Electron. 2022 , 5 , 893−903..

Lorenz, G.; Kandelbauer, A. In Silicones . Handbook of thermoset plastics. William Andrew Publishing, New York, 2022 , pp. 659-677.

Altıparmak, S. C.; Daminabo, S. I. C. Suitability analysis for extrusion-based additive manufacturing process. Addit. Manuf. Front. 2024 , 3 , 200106..

Wehner, M.; Truby, R. L.; Fitzgerald, D. J.; Mosadegh, B.; Whitesides, G. M.; Lewis, J. A.; Wood, R. J. An integrated design and fabrication strategy for entirely soft, autonomous robots. Nature. 2016 , 536 , 451−455..

Clet, J. A. G.; Liou, N.-S.; Weng, C.-H.; Lin, Y.-S. A parametric study for tensile properties of silicone rubber specimen using the bowden-type silicone printer. Materials 2022 , 15 , 1729..

Fay, C. D.; Wu, L. Cost-effective 3D printing of silicone structures using an advanced intra-layer curing approach. Technologies 2023 , 11 , 179..

Luis, E.; Pan, H. M.; Sing, S. L.; Bastola, A. K.; Goh, G. D.; Goh, G. L.; Tan, H. K. J.; Bajpai, R.; Song, J.; Yeong, W. Y. Silico ne 3D printing: process optimization, product biocompatibility, and reliability of silicone meniscus implants. 3D Print. Addit. Manuf. 2019 , 6 , 319−332..

Lam, Y. C.; Zhai, L. Y.; Tai, K.; Fok, S. C. An evolutionary approach for cooling system optimization in plastic injection moulding. Int. J. Prod. Res. 2004 , 42 , 2047−2061..

Sood, A. K.; Ohdar, R. K.; Mahapatra, S. S. Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater. Des. 2009 , 30 , 4243−4252..

Chung Wang, C.; Lin, T. W.; Hu, S. S. Optimizing the rapid prototyping process by integrating the Taguchi method with the gray relational analysis. Rapid Prototyping J. 2007 , 13 , 304−315..

Abdollahi, S.; Davis, A.; Miller, J. H.; Feinberg, A. W. Expert-guided optimization for 3D printing of soft and liquid materials. PloS One 2018 , 13 , e0194890..

Hsu, C. M. Improving the electroforming process in optical recordable media manufacturing via an integrated pr ocedure. Eng. Optim. 2004 , 36 , 659−675..

Chen, W. C.; Fu, G. L.; Tai, P. H.; Deng, W. J. Process parameter optimization for MIMO plastic injection molding via soft computing. Expert Syst. Appl. 2009 , 36 , 1114−1122..

Mok, S. L.; Kwong, C. K.; Lau, W. S. An intelligent hybrid system for initial process parameter setting of injection moulding. Int. J. Prod. Res. 2000 , 38 , 4565−4576..

Shahzad, A.; Lazoglu, I. Direct ink writing (DIW) of structural and functional ceramics: recent achievements and future challenges. Composites, Part B. 2021 , 225 , 109249..

Ribeiro, J.; Lopes, H.; Queijo, L.; Figueiredo, D. Optimization of cutting parameters to minimize the surface roughness in the end milling process using the Taguchi method. Period. Polytech-Mech. Eng . 2017 , 61 , 30−35..

Maloney, A.; Major, I.; Gately, N.; Devine, D. M. Effects of 3D printing parameters on the flexural properties of semi-crystalline PEKK. Mater. Today Commun. 2025 , 42 , 111152..

Xia, C.; Pan, Z.; Polden, J.; Li, H.; Xu, Y.; Chen, S. Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. J. Intell. Manuf. 2022 , 33 , 1467−1482..

Champa-Bujaico, E.; García-Díaz, P.; Díez-Pascual, A. M. Machine Learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art. Int. J. Mol. Sci. 2022 , 23 , 10712..

Montáns, F. J.; Chinesta, F.; Gómez-Bombarelli, R.; Kutz, J. N. Data-driven modeling and learning in science and engineering. Comptes Rendus. Mécanique . 2019 , 347 , 845−855..

Dong, J.; Li, Q.; Deng, L. Fast multi-objective optimization of multi-parameter antenna structures based on improved MOEA/D with surrogate-assisted model. AEU – Int. J. Electron. C. 2017 , 72 , 192−199..

Shit, S. C.; Shah, P. A review on silicone rubber. Natl. Acad. Sci. Lett. 2013 , 36 , 355−365..

DOWSIL™ 737 Neutral Cure Sealant. Available online: https://www.dow.com/content/dam/dcc/documents/en-us/.

Gibson, I.; Rosen, D.; Stucker, B.; Khorasani, M. in Additive Manufacturing Technologies , Springer International Publishing: Cham , 2021, p. 98.

Vaezi, M.; Seitz, H.; Yang, S. A review on 3D micro-additive manufacturing technologies. Int. J. Adv. Manuf. Technol. 2013 , 67 , 1721−1754..

Mortara, L.; Hughes, J.; Ramsundar, P. S.; Livesey, F.; Probert, D. R. Proposed classification scheme for direct writing technologies. Rapid Prototyping J. 2009 , 15 , 299−309..

Murphy, S. V.; Atala, A. 3D bioprinting of tissues and organs. Nat. Biotechnol . 2014 , 32 , 773–785..

Bogert, W. V. D.; Lorenz, J., Yi, X.; Fazeli, N.; Shih, A. J. Lumped-parameter modeling and control for r obotic high-viscosity fluid dispensing in additive manufacturing. arXiv preprint . 2022, arXiv:2210.10747.

Abbott, A. C.; Tandon, G. P.; Bradford, R. L.; Koerner, H.; Baur, J. W. Process-structure-property effects on ABS bond strength in fused filament fabrication. Addit. Manuf. 2018 , 19 , 29−38..

Messimer, S. L.; Rocha Pereira, T.; Patterson, A. E.; Lubna, M.; Drozda, F. O. Full-density fused deposition modeling dimensional error as a function of raster angle and build orientation: large dataset for eleven materials. J. Manuf. Mater. Process. 2019 , 3 , 6..

Liu, Z.; Wang, K.; He, H.; Xie, J.; Huang, W. Method for evaluating laser welding quality of battery module based on coupling model. J. Power Sources 2022 , 546 , 231959..

Sun, S.; Wei, Y.; Tsui, K.-L.; Wang, S. Forecasting tourist arrivals with machine learning and internet search index. 2019 , 70 , 1–10..

Huang, G.-B.; Wang, D. H.; Lan, Y. Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2011 , 2 , 107−122..

Otaki, D.; Nonaka, H.; Yamada, N. Thermal design optimization of electronic circuit board layout with transient heating chips by using Bayesian optimization and thermal network model. Int. J. Heat Mass Transfer . 2022 , 184 , 122263..

Pelikan, M., in Bayesian optimization algorithm: From single level to hierarchy , University of Illinois at Urbana-Champaign, Urbana, 2002 , p. 51.

Brochu, E.; Cora, V.M.; Freitas, N. de. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint . 2010, arXiv:1012.2599..

Liu, X.; Tian, Z.; Chen, C. Total organic carbon content prediction in lacustrine shale using extreme gradient boosting machine learning based on bayesian optimization. Geofluids 2021 , 2021 , 1−18..

Greenhill, S.; Rana, S.; Gupta, S.; Vellanki, P.; Venkatesh, S. Bayesian optimization for adaptive experimental desi gn: a review. IEEE Access 2020 , 8 , 13937−13948..

Wu, J.; Chen, X. Y.; Zhang, H.; Xiong, L. D.; Lei, H.; Deng, S. H. Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electro. Sci. Technol . 2019 , 17 , 26−40..

Bengio, Y.,in Neural Networks: Tricks of the Trade , Springer Berlin Heidelberg, Berlin Heidelberg New York, 2012 , pp. 437–478..

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