Machine learning based hierarchy of causative variables for tool failure in friction stir welding

Y. Du, T. Mukherjee, P. Mitra, T. DebRoy

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Since friction stir welding tools fail in service under various mechanisms, it is difficult to mitigate tool failure based on mechanistic understanding alone. Here we use multiple machine learning algorithms and a mechanistic model to identify the causative variables responsible for tool failure. We analyze one hundred and fourteen sets of experimental data for three commonly used alloys to evaluate the hierarchy of causative variables for tool failure. Three decision tree based algorithms are used to rank the hierarchy of the relative influence of six important friction stir welding variables on tool failure. The maximum shear stress is found to be the most important causative variable for tool failure. This is consistent with the effect of shear stress on the load experienced by the tool. The second most important factor is the flow stress which affects the plasticized material flow around the tool pin. All other variables are found to be significantly less important. Three algorithms also generate identical results and predict tool failure with the highest accuracy of 98%. A combination of mechanistic model, machine learning and experimental data can prevent tool failure accurately.

Original languageEnglish (US)
Pages (from-to)67-77
Number of pages11
JournalActa Materialia
Volume192
DOIs
StatePublished - Jun 15 2020

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Ceramics and Composites
  • Polymers and Plastics
  • Metals and Alloys

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