CLARiC: Contrastive Learning and Root-Cause Inference With Causality for Explainable Smart Manufacturing

This paper introduces CLARiC (Contrastive Learning and Root-cause Inference with Causality), a next-generation AI framework that unifies anomaly detection, causal inference, and explainable AI (XAI) techniques to advance the state of smart manufacturing. Unlike existing methods limited to correlation-based insights or opaque “black-box” models, CLARiC stands out in four key ways. First, it employs multimodal contrastive learning—fusing sensor streams, text logs, and images—to robustly distinguish normal from anomalous data, even under severe class imbalance or shifting operational conditions. Second, the approach extends beyond correlation by integrating causal inference (e.g., propensity score matching, difference-in-differences), thereby identifying high-impact factors that genuinely drive process deviations. Third, through counterfactual simulations in the learned embedding space and controlled real-world interventions, CLARiC verifies whether modifying specific parameters actually enhances manufacturing outcomes—reducing defects, boosting yield, and mitigating equipment drifts. Finally, the framework’s built-in XAI components deliver transparent, fine-grained explanations, enabling domain experts to trace anomalies back to root causes with clear causal pathways rather than post-hoc guesswork. Rigorous theoretical analyses confirm robust representation learning and consistent causal predictions across heterogeneous environments. Empirically, CLARiC lowered real-line abnormal rates by 34% in a 30-day WBG semiconductor trial and outperformed the best state-of-the-art baseline by $\approx ~3$ percentage points in accuracy and F1-score, underscoring its effectiveness from wide-band-gap semiconductors to automotive assembly lines. By bridging anomaly detection, interpretability, and validated causal interventions, CLARiC offers a decisive step forward in industrial AI for mission-critical manufacturing.