Risk-informed early-stage design of lightweight chassis systems: Integrating DFMEA, PFMEA, and Bayesian network-based causal risk propagation

This study proposes a risk-informed, decision-oriented framework for early-stage lightweight semi-trailer chassis design by integrating Design Failure Mode Effects Analysis (DFMEA), Process Failure Mode Effects Analysis (PFMEA), and Bayesian Network (BN)-based probabilistic reasoning. DFMEA and PFMEA are used to structure design sensitivities and manufacturing constraints, while the BN captures causal risk propagation under data scarcity. A causality-driven BN with seven nodes, Structural Adequacy (SA), Material Transition Integrity (MTI), Joining and Assembly Integrity (JAI), Interface Protection Integrity (IPI), Specification and Configuration Control (SCC), Damage Initiation Risk (DIR), and Structural Failure Risk (SFR), is constructed based on DFMEA–PFMEA outputs, with conditional probabilities defined via a weighted ordinal aggregation approach to ensure tractability and interpretability. The model enables probabilistic inference, sensitivity analysis, and scenario-based comparison of steel, aluminium, and hybrid chassis configurations, supported by targeted FEA interpretation. Results show that SA and MTI dominate system-level failure risk, with a hierarchical influence ranking (SA > MTI > JAI > IPI > SCC). Probabilistic inference indicates that the high-risk SFR probability increases from approximately 0.20 (steel) to approximately 0.45 (aluminium), while hybrid configurations exhibit interaction-driven risk amplification. The framework transforms conventional FMEA into a causal, probabilistic decision-support tool, enabling early-stage risk prioritisation and informed design selection under uncertainty.