Causal modelling and quality control of complex product assembly processes driven by data and knowledge fusion
提出一种融合数据与知识的因果建模方法，通过强化学习构建质量特性因果网络，并集成根因分析与预测框架，提升复杂产品装配的质量控制效果。
Due to the complexity of assembly processes in complex products, even slight deviations can result in quality problems. Quality problems often stem from variations in quality characteristics that, when propagated and superimposed through the assembly flow, exceed acceptable thresholds. Modelling these causal effects at the quality characteristic level and establishing a causal network is an effective strategy for quality control. This paper proposes a data- and knowledge-driven approach for causal modelling and quality control in complex product assembly. First, the propagation paths of quality characteristic variations are extracted as prior knowledge and incorporated into a reinforcement learning algorithm to guide causal graph construction and improve the accuracy of causal modelling. Second, a quality control framework integrating root cause analysis and prediction is developed based on the established causal network. A Bayesian method is applied to provide probabilistic guidance for root cause analysis, while the causal network is used to identify and eliminate characteristics unrelated to the target characteristic, thereby enhancing the accuracy of quality prediction. Finally, the proposed method is validated using an aircraft assembly case study. Experimental results demonstrate its feasibility and effectiveness in enhancing quality control in complex product assembly.