Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology

Hsiang-Tse Chung; Chin-Cheng Tsai; Kuo-Kuang Jen; Ying-Sun Huang; Yi-Cherng Ferng; Ching-Yuan Lo; Tso-Wei Chen; Kuo-Hao Chang; An-Chou Yeh

Results in Engineering (Jun 2024)

Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology

Hsiang-Tse Chung,
Chin-Cheng Tsai,
Kuo-Kuang Jen,
Ying-Sun Huang,
Yi-Cherng Ferng,
Ching-Yuan Lo,
Tso-Wei Chen,
Kuo-Hao Chang,
An-Chou Yeh

Affiliations

Hsiang-Tse Chung: Department of Materials Science and Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan; High Entropy Materials Center, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu 10 Road, Hsinchu, 30013, Taiwan
Chin-Cheng Tsai: Department of Materials Science and Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan; High Entropy Materials Center, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu 10 Road, Hsinchu, 30013, Taiwan
Kuo-Kuang Jen: Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, No. 481, Sec. Jia'an, Zhongzheng Rd., Longtan Dist., Taoyuan City, 325, Taiwan
Ying-Sun Huang: Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, No. 481, Sec. Jia'an, Zhongzheng Rd., Longtan Dist., Taoyuan City, 325, Taiwan
Yi-Cherng Ferng: Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, No. 481, Sec. Jia'an, Zhongzheng Rd., Longtan Dist., Taoyuan City, 325, Taiwan
Ching-Yuan Lo: Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, No. 481, Sec. Jia'an, Zhongzheng Rd., Longtan Dist., Taoyuan City, 325, Taiwan
Tso-Wei Chen: Department of Materials Science and Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan; High Entropy Materials Center, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu 10 Road, Hsinchu, 30013, Taiwan
Kuo-Hao Chang: High Entropy Materials Center, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu 10 Road, Hsinchu, 30013, Taiwan; Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan; National Science and Technology Center for Disaster Reduction, 9F., No. 200, Sec. 3, Beisin Rd., Xindian District, New Taipei City, 23143, Taiwan; Corresponding author. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.
An-Chou Yeh: Department of Materials Science and Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan; High Entropy Materials Center, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu 10 Road, Hsinchu, 30013, Taiwan; Corresponding author. Department of Materials Science and Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.

Journal volume & issue: Vol. 22
p. 102182

Abstract

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In this study, Random Forest Regression (RFR) and Response Surface Methodology (RSM) were employed to predict the optimized processing parameters to achieve the highest densification of a nickel-based superalloy Mar-M247LC fabricated by selective laser melting (SLM). The RFR model considered input processing parameters such as laser power, hatch distance, and scanning speed. A dataset of 223 samples, was used to train the RFR model. As a result, the RFR model exhibited accuracy of 99.57 %, R2 value of 0.976, Mean Square Error (MSE) of 0.402, and Mean Absolute Percentage Error (MAPE) of 0.426 % on testing set. In addition to the RFR model, this study also employed Central Composite Design (CCD) and RSM to optimize the processing parameter sets. Subsequently, this study conducted Box-Behnken Design (BBD) to experimentally validate the accuracy of this RFR model. In the end, a set of the optimal processing parameters was tested and resulted a sample densification of 99.959 %, outperformed that in the original database before building the RFR model, which was 99.734 %. In summary, the RFR models was able to predict densification with accuracy, and by coupling with RSM, the optimal processing parameter could be obtained, so better densification of the build could be achieved.

Published in Results in Engineering

ISSN: 2590-1230 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Technology
Website: https://www.journals.elsevier.com/results-in-engineering

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