让AI在制造中做安全决策，能追踪每一步依据并自动验证物理合理性。
Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing
- 分四模块协同：意图路由、量化分析、知识图谱检索、批判性验证
- 相比单模型，多步任务成功率提升87.5个百分点，偏差降至约±0.001英寸
- 适合高风险制造场景，支持可追溯的人机协同决策
高精度CNC加工自由曲面航空件需基于检测、仿真与工艺知识进行有界补偿。通用大语言模型虽能生成文本，但无法可靠执行受风险约束的多步数值流程，也无法提供高价值决策的可审计溯源。本文提出多智能体知识分析（MAKA）架构，通过分离意图路由、仅工具量化分析、知识图谱检索与基于批评的验证，确保推荐前满足物理合理性、安全边界和溯源完整性。该架构在钛合金转子叶片加工测试平台上实现，融合16片叶片的虚拟路径误差场、切削力与变形仿真及扫描3D检测偏差图。分析将偏差分解为：证据关联的路径分量、反映系统性演变的磨损代理、残余系统性合规项及不稳定性敏感的变异性代理。三层次工具调度基准测试显示，相较无结构单模型交互模式（相同工具访问），MAKA使多步任务成功执行率最高提升87.5个百分点。数字孪生试算表明，MAKA可协调可追溯的补偿方案，将预测表面偏差从约10⁻²英寸降低至模拟环境内大部分区域的±10⁻³英寸，为高风险人机决策提供预部署验证信号。
原文摘要 · Abstract (English)
High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledge. Off-the-shelf large language model (LLM) assistants can generate text, but they do not reliably execute risk-constrained multi-step numerical workflows or provide auditable provenance for high-stakes decisions. We present multi-agent knowledge analysis (MAKA), a human-in-the-loop decision-support architecture that separates intent routing, tools-only quantitative analysis, knowledge graph retrieval, and critic-based verification that enforces physical plausibility, safety bounds, and provenance completeness before recommendations are surfaced for human approval. MAKA is instantiated on a Ti-6Al-4V rotor blade machining testbed by fusing virtual-machining path-tracking error fields, cutting-force and deflection simulations, and scan-based 3D inspection deviation maps from 16 blades. The analysis decomposes deviation into an evidence-linked pathing component, a drift-based wear proxy capturing systematic evolution across parts, a residual systematic compliance term, and a variability proxy for instability-aware escalation. In a three-level tool-orchestration benchmark (single-step through $\geq$3-step stateful sequences), MAKA improves successful tool execution by up to 87.5 percentage points relative to an unstructured single-model interaction pattern with identical tool access. Digital twin what-if studies show MAKA can coordinate traceable compensation candidates that reduce predicted surface deviation from order $10^{-2}$in to approximately $\pm 10^{-3}$in over most of the blade within the simulation environment, providing a pre-deployment verification signal for risk-aware human decision-making.
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