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IJAT Vol.17 No.2 pp. 156-166
doi: 10.20965/ijat.2023.p0156
(2023)

Research Paper:

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Statistical and Artificial Intelligence Analyses of Blast Treatment Condition Effects on Blast-Assisted Injection Molded Direct Joining

Shuohan Wang*,†, Fuminobu Kimura*,**, Shuaijie Zhao**, Eiji Yamaguchi***, Yuuka Ito***, Yukinori Suzuki***, and Yusuke Kajihara*,**

*Department of Precision Engineering, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

†Corresponding author

**Institute of Industrial Science, The University of Tokyo
Tokyo, Japan

***SINTOKOGIO LTD.
Toyokawa, Japan

Received:
August 3, 2022
Accepted:
October 17, 2022
Published:
March 5, 2023
Creative Commons License
Keywords:
metal–plastic direct joining, injection molded direct joining, machine learning, blasting
Abstract

Efficient hybrid joining methods are required for joining metals and plastics in the automobile and airplane industries. Injection molded direct joining (IMDJ) is a direct joining technique that uses metal pretreatment and injection molding of plastic to form joints without using any additional parts. This joining technique has attracted attention from industries for its advantages of high efficiency and low cost in mass production. Blast-assisted IMDJ, an IMDJ technique that employs blasting as the metal pretreatment, has become suitable for the industry because metal pretreatment can be performed during the formation of the metal surface structure without chemicals. To satisfy industry standards, the blast-assisted IMDJ technique needs to be optimized under blasting conditions to improve joining performance. The number of parameters and their interactions make this problem difficult to solve using conventional control variable methods. We propose applying statistical and artificial intelligence analyses to address this problem. We used multiple linear regression and back propagation neural networks to analyze the experimental data. The results elucidated the relationship between the blasting conditions and joining strength. According to the machine learning predicted results, the best joining strength in blast-assisted IMDJ reached 22.3 MPa under optimized blasting conditions. This study provides new insights into similar engineering problems.

Cite this article as:
S. Wang, F. Kimura, S. Zhao, E. Yamaguchi, Y. Ito, Y. Suzuki, and Y. Kajihara, “Statistical and Artificial Intelligence Analyses of Blast Treatment Condition Effects on Blast-Assisted Injection Molded Direct Joining,” Int. J. Automation Technol., Vol.17 No.2, pp. 156-166, 2023.
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