single-au.php

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

Research Paper:

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
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.
Data files:
References
  1. [1] V. Spaiser, K. Scott, A. Owen, and R. Holland, “Consumption-based accounting of CO2 emissions in the sustainable development Goals Agenda,” Int. J. Sustain. Dev. World Ecol., Vol.26, No.4, pp. 282-289, 2019. https://doi.org/10.1080/13504509.2018.1559252
  2. [2] J. C. González Palencia, T. Furubayashi, and T. Nakata, “Energy use and CO2 emissions reduction potential in passenger car fleet using zero emission vehicles and lightweight materials,” Energy, Vol.48, No.1, pp. 548-565, 2012. https://doi.org/10.1016/J.ENERGY.2012.09.041
  3. [3] R. A. Witik, J. Payet, V. Michaud, C. Ludwig, and J. A. E. Månson, “Assessing the life cycle costs and environmental performance of lightweight materials in automobile applications,” Compos. Part A Appl. Sci. Manuf., Vol.42, No.11, pp. 1694-1709, 2011. https://doi.org/10.1016/J.COMPOSITESA.2011.07.024
  4. [4] S. Katayama and Y. Kawahito, “Laser direct joining of metal and plastic,” Scr. Mater., Vol.59, No.12, pp. 1247-1250, 2008. https://doi.org/10.1016/j.scriptamat.2008.08.026
  5. [5] J. Rauschenberger, A. Cenigaonaindia, J. Keseberg, D. Vogler, U. Gubler, and A. J. Rauschenberger, “Laser hybrid joining of plastic and metal components for lightweight components,” Proc. Volume 9356, High-Power Laser Materials Processing: Lasers, Beam Delivery, Diagnostics, and Applications IV, 93560B, 2015. https://doi.org/10.1117/12.2080226
  6. [6] G. Wagner, F. Balle, and D. Eifler, “Ultrasonic welding of aluminum alloys to fiber reinforced polymers,” Adv. Eng. Mater., Vol.15, No.9, pp. 792-803, 2013. https://doi.org/10.1002/adem.201300043
  7. [7] K. N. Balakrishnan, H. T. Kang, and P. K. Mallick, “Joining aluminum to nylon using frictional heat,” SAE Tech. Pap., Vol.2007, No.724, 2007. https://doi.org/10.4271/2007-01-1701
  8. [8] P. Mitschang, R. Velthuis, and M. Didi, “Induction spot welding of metal/CFRPC hybrid joints,” Wiley Online Libr., Vol.15, No.9, pp. 804-813, 2013. https://doi.org/10.1002/adem.201200273
  9. [9] K. Ramani and B. Moriarty, “Thermoplastic bonding to metals via injection molding for macro-composite manufacture,” Polym. Eng. Sci., Vol.38, No.5, pp. 870-877, 1998. https://doi.org/10.1002/pen.10253
  10. [10] Y. Kajihara, Y. Tamura, F. Kimura, G. Suzuki, N. Nakura, and E. Yamaguchi, “Joining strength dependence on molding conditions and surface textures in blast-assisted metal-polymer direct joining,” CIRP Ann., Vol.67, No.1, pp. 591-594, 2018. https://doi.org/10.1016/j.cirp.2018.04.112
  11. [11] K. Taki, S. Nakamura, T. Takayama, and A. Nemoto, “Direct joining of a laser-ablated metal surface and polymers by precise injection molding,” Microsyst. Technol., Vol.22, No.1, pp. 31-38, 2016. https://doi.org/10.1007/s00542-015-2640-2
  12. [12] S. Zhao, F. Kimura, S. Kadoya, and Y. Kajihara, “Experimental analysis on mechanical interlocking of metal–polymer direct joining,” Precis. Eng., Vol.61, pp. 120-125, 2020. https://doi.org/10.1016/J.PRECISIONENG.2019.10.009
  13. [13] K. Enami, F. Kimura, K. Yokoyama, T. Murakami, and Y. Kajihara, “Experimental and simulative investigation of the effects of laser-structured metal surface on metal-polymer direct joining,” Precis. Eng., Vol.62, pp. 273-281, 2020. https://doi.org/10.1016/j.precisioneng.2019.12.011
  14. [14] S. Kadoya, F. Kimura, and Y. Kajihara, “PBT–anodized aluminum alloy direct joining: Characteristic injection speed dependence of injected polymer replicated into nanostructures,” Polym. Test., Vol.75, pp. 127-132, 2019. https://doi.org/10.1016/j.polymertesting.2019.02.006
  15. [15] R. Y. Yeh and R. Q. Hsu, “Improving the adhesion of plastic/metal direct bonding by injection moulding using surface modifications,” Adv. Mater. Process. Technol., Vol.2, No.1, pp. 21-30, 2016. https://doi.org/10.1080/2374068X.2016.1147765
  16. [16] T. Kleffel and D. Drummer, “Investigating the suitability of roughness parameters to assess the bond strength of polymer-metal hybrid structures with mechanical adhesion,” Compos. Part B Eng., Vol.117, pp. 20-25, 2017. https://doi.org/10.1016/j.compositesb.2017.02.042
  17. [17] G. Lucchetta, F. Marinello, and P. F. Bariani, “Aluminum sheet surface roughness correlation with adhesion in polymer metal hybrid overmolding,” CIRP Annals, Vol.60, No.1, pp. 559-562, 2011. https://doi.org/10.1016/j.cirp.2011.03.073
  18. [18] C. Hopmann, J. Klein, B. I. Schönfuß, U. Reisgen, J. Schönberger, and A. Schiebahn, “Analysis and specification of the crash behaviour of plastics/metal-hybrid composites by experimental and numerical methods,” Prod. Eng., Vol.11, No.2, pp. 183-193, 2017. https://doi.org/10.1007/s11740-017-0727-6
  19. [19] R. I. Badiger, S. Narendranath, and M. S. Srinath, “Optimization of Process Parameters by Taguchi Grey Relational Analysis in Joining Inconel-625 Through Microwave Hybrid Heating,” Metallogr. Microstruct. Anal., Vol.8, No.1, pp. 92-108, 2019. https://doi.org/10.1007/S13632-018-0508-4
  20. [20] G. S. V. S. Kumar, A. Kumar, S. Rajesh, R. B. R. Chekuri, and A. G. Adigo, “Experimental investigation and optimization on friction stir welding of nylon 6A using Taguchi and ANOVA with microstructural analysis,” Adv. Mater. Sci. Eng., Vol.2021, 7483393, 2021. https://doi.org/10.1155/2021/7483393
  21. [21] E. E. Feistauer, J. F. dos Santos, and S. T. Amancio-Filho, “An investigation of the ultrasonic joining process parameters effect on the mechanical properties of metal-composite hybrid joints,” Weld. World, Vol.64, No.9, pp. 1481-1495, 2020. https://doi.org/10.1007/s40194-020-00927-x
  22. [22] D. Zhao, D. Ren, K. Zhao, S. Pan, and X. Guo, “Effect of welding parameters on tensile strength of ultrasonic spot welded joints of aluminum to steel – By experimentation and artificial neural network,” J. Manuf. Process., Vol.30, pp. 63-74, 2017. https://doi.org/10.1016/J.JMAPRO.2017.08.009
  23. [23] Z. Y. Liu, W. D. Wang, and W. Gao, “Prediction of the mechanical properties of hot-rolled CMn steels using artificial neural networks,” J. Mater. Process. Technol., Vol.57, Nos.3-4, pp. 332-336, 1996. https://doi.org/10.1016/0924-0136(95)02089-6
  24. [24] M. W. Dewan, D. J. Huggett, T. Warren Liao, M. A. Wahab, and A. M. Okeil, “Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network,” Mater. Des., Vol.92, pp. 288-299, 2016. https://doi.org/10.1016/j.matdes.2015.12.005
  25. [25] R. H. Myers, D. C. Montgomery, and C. M. Anderson-Cook, “Response surface methodology: process and product optimization using designed experiments,” John Wiley & Sons, 2016.
  26. [26] B. Bonpain and M. Stommel, “Influence of surface roughness on the shear strength of direct injection molded plastic-aluminum hybrid-parts,” Int. J. Adhes. Adhes., Vol.82, pp. 290-298, 2018. https://doi.org/10.1016/j.ijadhadh.2018.02.003
  27. [27] S. A. Filho and L. Blaga, “Joining of polymer-metal hybrid structures: principles and applications,” John Wiley & Sons, 2018.
  28. [28] K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural Networks, Vol.4, No.2, pp. 251-257, 1991. https://doi.org/10.1016/0893-6080(91)90009-T
  29. [29] J. Pradeep Kumar and A. Divyenth, “Modelling and prediction of strength of ultrasonically welded electrical contact joints using Artificial Neural Network,” Mater. Today Proc., Vol.22, pp. 1893-1901, 2020. https://doi.org/10.1016/j.matpr.2020.03.089
  30. [30] M. P. Satpathy, S. B. Mishra, and S. K. Sahoo, “Ultrasonic spot welding of aluminum-copper dissimilar metals: A study on joint strength by experimentation and machine learning techniques,” J. Manuf. Process., Vol.33, pp. 96-110, 2018. https://doi.org/10.1016/j.jmapro.2018.04.020
  31. [31] B. H. M. Sadeghi, “A BP-neural network predictor model for plastic injection molding process,” J. Mater. Process. Technol., Vol.103, No.3, pp. 411-416, 2000. https://doi.org/10.1016/S0924-0136(00)00498-2
  32. [32] D. C. Li and I. H. Wen, “A genetic algorithm-based virtual sample generation technique to improve small data set learning,” Neurocomputing, Vol.143, pp. 222-230, 2014. https://doi.org/10.1016/j.neucom.2014.06.004
  33. [33] M. Zhou, X. Xiong, D. Drummer, and B. Jiang, “Interfacial interaction and joining property of direct injection-molded polymer-metal hybrid structures: A molecular dynamics simulation study,” Appl. Surf. Sci., Vol.478, pp. 680-689, 2019. https://doi.org/10.1016/j.apsusc.2019.01.286
  34. [34] A. Roesner, S. Scheik, A. Olowinsky, A. Gillner, U. Reisgen, and M. Schleser, “Laser Assisted Joining of Plastic Metal Hybrids,” Phys. Procedia, Vol.12, PART B, pp. 370-377, 2011. https://doi.org/10.1016/j.phpro.2011.03.146
  35. [35] K. Dröder, M. Brand, and M. Kühn, “Numerical and Experimental Analyses on the Influence of Array Patterns in Hybrid Metal-FRP Materials Interlocked by Mechanical Undercuts,” Procedia CIRP, Vol.62, pp. 51-55, 2017. https://doi.org/10.1016/j.procir.2016.06.121

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2024