AI-Enabled Axiomatic Design for MES-Level Process Parameters Optimization in Cloud-Based Manufacturing Execution Systems

In cloud manufacturing, Manufacturing Execution Systems (MES) must dynamically optimize MES-level process parameters under multi-objective and constraint-intensive conditions, including quality conformity, production-takt feasibility, energy consumption, and equipment-stability risk. Conventional rule-based approaches suffer from limited efficiency, weak handling of parameter coupling, and insufficient decision traceability. This paper presents an AI-enabled Axiomatic Design (AD) governance framework for MES-level process parameter optimization and recommendation in cloud-based MES. AI capabilities are embedded within a structured AD-based framework to decompose manufacturing requirements, estimate functional requirement-design parameter coupling, and organize constraint-first decision making. A manufacturing knowledge graph encodes equipment boundaries and process rules as computable hard constraints; a multi-agent system (MAS) separates candidate generation, evaluation, and constraint validation; and a large language model (LLM) provides an evidence-driven semantic interface. Experiments were conducted on a 10,000-sample public manufacturing-process dataset. The rule audit confirmed that the dataset-defined Historical Favorable State label follows explicit thresholds on temperature, machine speed, and vibration; the classifier output is therefore used as label-consistency evidence within the evaluation module. In the offline recommendation case, the proposed AD-governed feasible-domain ranking achieved 0/20 hard-constraint violations in the Top-20 candidates, whereas the ungated weighted energy-stability ranking and the historical-label/data-driven ranking each produced 17/20 violations under the same deterministic tie-break rule. These results support the internal consistency and engineering feasibility of the proposed governance-oriented workflow at the MES execution-parameter level, with online industrial deployment identified as future work.