结合循环本体的激光粉末床熔融的神经符号闭环控制
Neuro-Symbolic Closed-Loop Control of Laser Powder Bed Fusion with an In-Loop Ontology
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中文总结 AI 辅助
该研究提出结合循环本体的神经符号闭环架构，用于激光粉末床熔融，可消除焊瘤、保持焊瘤为零且适配新合金，确立了架构可行性，下一步需实验校准深宽比。
中文摘要 AI 辅助
针对激光粉末床熔融（Laser Powder Bed Fusion），本文提出了一种几何条件下的神经符号闭环架构，其中符合标准的本体在控制循环内运行，将符号推理与统计学习相结合，以设置约束感知预测控制器的目标。该本体将工艺目标和约束与控制器可观测的信号关联起来，描述逻辑推理器将其转换为对每次扫描强制执行的参考值和边界。所展示的案例是悬垂焊瘤（overhang dross），这是熔池深度的一个质量限制，它决定了质量但在构建过程中无法测量，通过几何和功率相关的深宽比映射到可观测宽度的边界，该比值及其校准后的不确定性由高斯过程（Gaussian process）提供。推理器对每个即将到来的特征进行分类并选择主动约束——在悬垂处添加未熔合下限、在校准范围外添加单调保护、在工艺窗口被声明处添加能量密度上限，同时仅基于几何上下文的变化运行，否则在每次扫描路径上仅保留一个小型二次规划。在校准至NIST AM-Bench基准（针对IN625）的Eagar-Tsai代理模型中，该架构消除了无几何感知控制器产生的焊瘤，在双评分下将焊瘤保持在零，仅存在少量残留未熔合，在故意的设备失配下表现平稳，并且通过编辑本体数据而非代码来重新适配新合金和约束。结果确立了架构可行性，下一步的主要工作是对该比值进行实验校准。
英文摘要
A geometry-conditioned, neuro-symbolic closed-loop architecture is proposed for laser powder bed fusion, in which a standards-aligned ontology operates inside the control loop and couples symbolic reasoning with statistical learning to set the targets of a constraint-aware predictive controller. The ontology links the process objectives and constraints to the signals a controller can observe, and a description-logic reasoner converts them into the references and bounds enforced on each scan. The demonstrated case is overhang dross, a quality limit on the melt pool depth, which governs quality yet cannot be measured during the build, is mapped through a geometry- and power-dependent depth-to-width ratio onto a bound on the observable width, with the ratio and its calibrated uncertainty supplied by a Gaussian process. The reasoner classifies each upcoming feature and selects the active constraints-adding a lack-of-fusion floor at overhangs, a monotone guard beyond the calibrated range, and an energy-density cap where a process window is declared while running only on changes of geometric context and otherwise leaving a single small quadratic program on the per-scan path. In an Eagar-Tsai surrogate calibrated to the NIST AM-Bench benchmark for IN625, the architecture eliminates the dross produced by a geometry-blind controller, holds dross at zero with only a small residual lack-of-fusion under dual scoring, degrades gracefully under deliberate plant mismatch, and retargets to new alloys and constraints by editing ontology data rather than code. The results establish architectural feasibility, experimental calibration of the ratio is the principal next step.
