Extracting causal relations from complex dynamic systems has become an appealing topic for decades, especially for machine design engineering, industrial manufacturing, and equipment maintenance, which usually suffer from a large number of tangled relationships. Although many causality detection methods have been utilized, evaluating and choosing appropriate methods, and developing proper workflow remain challenges. In this paper, a causal analysis workflow designed to detect hidden patterns involved with mechanical mechanisms is presented. In particular, various causality measures are ensembled, enabling the search for refined causal mechanisms, the impact of constitutive law, and spatial distribution of causality from the entangled raw network. Based on numerical experiments, several beneficial conclusions can be drawn: Separating typical stages is necessary for a complex process; The constitutive property has a great impact on causal inference; The discrepancy of causality among different locations of monitor points mainly depends on whether it is near the fixed boundary, near to the load, or in contact with friction; Granger Causality is suitable for discovering linear dependencies among material, load, and geometry, while constraint-based and score-based algorithms excel in identifying nonlinear causality in metal plasticity, severe discontinuity in contact, impulsive dynamic load, or damping phenomenon.
Siyang Zhou (Wed,) studied this question.