Command2Vec: Feature Learning of 3D Modeling Behavior Sequence—A Case Study on “Spiral-stair”

In this study, we applied machine learning to mine the event logs generated in modeling process for behavior sequence clustering. The motivation for the study is to develop cognitively intelligent 3D tools through process mining which has been a hot area in recent years. In this study, we develop a novel classification method Command2Vec to perceive, learn and classify different design behavior during 3D-modeling aided design process. The method is applied in a case study of 112 participate students on a ‘Spiral-stair’ modeling task. By extracting the event logs generated in each participate student’s modeling process into a new data structures: ‘command graph’, we classified participants’ behavior sequences from final 99 valid event logs into certain groups using our novel Command2Vec. To verify the effectiveness of our classification, we invited five experts with extensive modeling experience to grade the classification results. The final grading shows that our algorithm performs well in certain grouping of classification with significant features.