Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Zhang, Yapeng | Guo, Yanling; * | Xiao, Yaning | Tang, Wenxiu | Zhang, Haoyu | Li, Jian
Affiliations: Northeast Forestry University, College of Mechanical and Electrical Engineering, Harbin, China
Correspondence: [*] Corresponding author. Yanling Guo, Northeast Forestry University, College of Mechanical and Electrical Engineering, Harbin, 150040, China. E-mail: guo.yl@hotmail.com.
Abstract: The material constriction is one of the important factors that influence the forming accuracy of selective laser sintering (SLS). Currently, in order to reduce the shrinkage and improve the quality of products, the optimal combination of machining process parameters is mainly determined by numerous experiments. This often takes valuable time and costs a lot, but the results are mediocre. With the development of intelligent optimization algorithms, they are applied in various disciplines for solving complex problems. Hence, for reducing the shrinkage of parts and overcoming the limitation in the optimization of the process parameters, this paper proposes a novel hybrid improved Hunger Games Search algorithm (HGS) with extreme learning machine (ELM) model for predicting the shrinkage of parts. Firstly, the orthogonal experiments were conducted based on the five key process parameters, the obtained parts datasets were divided into the training set and test set. Secondly, the Cube mapping and refracted opposition-based learning strategies are adopted to increase the convergence speed and solution accuracy of HGS. In addition, the regression prediction model was constructed with the improved HGS(IHGS) and ELM, and this model is trained using the training set. Finally, the test set is used to evaluate the trained model and find the optimal combination of process parameters with the lowest shrinkage of parts. The experimental results suggest that the IHGS-ELM model proposed in this study has high forecasting precision, with the R2 and RMSE are only 0.9124 and 0.2433, respectively. This model can guide the laser sintering process of polyether sulfone (PES) powder.
Keywords: Selective laser sintering, shrinkage, hunger games search, cube mapping, refracted opposition-based learning
DOI: 10.3233/JIFS-212799
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5643-5659, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl