TY - JOUR
T1 - Development of professional competencies for artificial intelligence in finite element analysis
AU - Chen, Dyi Cheng
AU - You, Ci Syong
AU - Su, Ming Shang
PY - 2020/1/1
Y1 - 2020/1/1
N2 - This 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.
AB - This 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.
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U2 - 10.1080/10494820.2020.1719162
DO - 10.1080/10494820.2020.1719162
M3 - Article
AN - SCOPUS:85079732280
JO - Interactive Learning Environments
JF - Interactive Learning Environments
SN - 1049-4820
ER -