Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems

Modeling manufacturing processes assists the design of new systems, allowing predictions of future behaviors, identifying improvement areas and evaluating changes to existing systems. Probabilistic Boolean networks (PBN) have been used to study biological systems, since they combine uncertainty and rule-based representation. A novel approach is proposed to model the design of an automated manufacturing assembly processes using PBNs to generate quantitative data for occurrence assessment in design failure mode and effects analysis. FMEA is a widely used tool in risk assessment (RA) to ensure design outputs consistently deliver the intended level of performance. Effectiveness of RA depends upon the robustness of the data used. Temporal logic is applied to analyze state successions in a transition system, while interactions and dynamics are captured over a set of Boolean variables using PBNs. Designs are therefore enhanced through assessment of risks, using proposed tools in the early phases of design of manufacturing systems. A two-sample T test demonstrates the proposed model provides values closer to expected values; consequently modeling observable phenomena ( $$p\,\hbox {value} > 0.05$$ p value > 0.05 ). Simulations are used to generate data required to conduct inferential statistical tests to determine the level of correspondence between model prediction and real machine data.