Machine Learning Analysis of Bonding Strength and Forging Conditions in Forge-Bonding with Circumferential Sliding

8 pages•Published: August 6, 2026

Ryo Matsumoto, Kakeru Hashimoto and Hiroshi Utsunomiya

Abstract

To investigate the influence of the circumferential sliding at the upper–lower workpiece contact interface on the bonding characteristics in forge-bonding with copper and aluminum workpieces, the experimental relationship between the forge-bonding conditions and the bonding characteristics was analyzed by means of machine learning analysis. The bonding probability was predicted with an accuracy of approximately 90%, while the bonding strength was predicted with an accuracy comparable to the experimental scattering. Furthermore, the circumferential sliding at the upper–lower workpiece contact interface was secondary greatest influence, following forging stroke, on both bonding probability and bonding strength.

Keyphrases: bonding, forging, machine learning

In: Numpon Mahayotsanun (editor). Proceedings of The 11th International Conference on Tribology in Manufacturing Processes & Advanced Surface Engineering, vol 4, pages 57-64.

Links:	https://easychair.org/publications/paper/DhJC
	https://doi.org/10.29007/6t2v

BibTeX entry

@inproceedings{ICTMP2026:Machine_Learning_Analysis_Bonding,
  author    = {Ryo Matsumoto and Kakeru Hashimoto and Hiroshi Utsunomiya},
  title     = {Machine Learning Analysis of Bonding Strength and Forging Conditions in Forge-Bonding with Circumferential Sliding},
  booktitle = {Proceedings of The 11th International Conference on Tribology in Manufacturing Processes & Advanced Surface Engineering},
  editor    = {Numpon Mahayotsanun},
  series    = {EPiC Series in Technology},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2322},
  url       = {/publications/paper/DhJC},
  doi       = {10.29007/6t2v},
  pages     = {57-64},
  year      = {2026}}

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