Development of professional competencies for artificial intelligence in finite element analysis

Abstract

ABSTRACTThis study identified the competency requirement for artificial intelligence in finite element analysis. The 10 Delphi group members included 5 field engineers in mechanical fields and 5 scholars from a technology institute. Next, 10 field experts were invited to participate. Using the Delphi technique and analytic hierarchy process, questionnaires were designed to assess competency indicators of artificial intelligence in finite element analysis. The data collected from questionnaires were analyzed using a nonparametric Wilcoxon signed-rank test, the Z value of the Kolmogorov–Smirnovone (KS) test, and relative weight. To fulfill the research objectives, a questionnaire was designed to collect data for 40 general competencies in 10 domains: (1) introduction to the finite element method, (2) pretreatment, (3) coordinate system, (4) model construction skills, (5) boundary conditions and solutions, (6) post processor application, (7) machine learning, (8) neural network, (9) deep learning, and (10) artificial intelligence in finite element analysis. The results of the three rounds of the Delphi technique expert questionnaire revealed essential professional competencies for AI in FEA. These findings can be used to devise a training and development plan and provide valuable references for educators in the field of engineering and technical education.