While modern 5-axis computer numerical control (CNC) systems offer enhanced design flexibility and reduced production time, the dimensional accuracy of the workpiece is significantly compromised by geometric errors, thermal deformations, cutting forces, tool wear, and fixture-related factors. In-situ sensing, in conjunction with machine learning (ML), has recently been implemented on edge devices to synchronously acquire and agilely analyze high-frequency and multifaceted data for the prediction of workpiece quality. However, limited edge computational resources and lack of interpretability in ML models obscure the understanding of key quality-influencing signals. This research introduces , a novel graph-based hyperdimensional computing framework that not only assesses workpiece quality in 5-axis CNC on edge, but also characterizes key signals vital for evaluating the quality from in-situ multichannel data. Specifically, a hierarchical graph structure is designed to represent the relationship between channels (e.g., spindle rotation, three linear axes movements, and the rotary A and C axes), parameters (e.g., torque, current, power, and tool speed), and the workpiece dimensional accuracy. Additionally, memory refinement, separability, and parameter significance are proposed to assess the interpretability of the framework. Experimental results on a hybrid 5-axis LASERTEC 65 DED CNC machine indicate that not only achieves a 90.7% F1-Score in characterizing a 25.4 mm counterbore feature deviation but also surpasses other ML models with an F1-Score margin of up to 73.0%. The interpretability of the framework reveals that load and torque have 12 times greater impact than power and velocity feed forward for the characterization of geometrical dimensions. offers the potential to facilitate causal discovery and provide insights into the relationships between process parameters and part quality in manufacturing.

中文翻译：

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用于加速加工过程质量监控的分层表示和可解释学习

虽然现代 5 轴计算机数控 (CNC) 系统提高了设计灵活性并缩短了生产时间，但工件的尺寸精度会受到几何误差、热变形、切削力、刀具磨损和夹具相关因素的严重影响。现场传感与机器学习（ML）相结合，最近已在边缘设备上实现，以同步获取并敏捷分析高频和多方面的数据，以预测工件质量。然而，有限的边缘计算资源和机器学习模型缺乏可解释性模糊了对影响质量的关键信号的理解。这项研究引入了一种基于图形的新型超维计算框架，该框架不仅可以评估边缘 5 轴 CNC 中的工件质量，还可以表征对于评估现场多通道数据质量至关重要的关键信号。具体来说，设计了分层图形结构来表示通道（例如，主轴旋转、三个线性轴运动以及旋转A轴和C轴）、参数（例如，扭矩、电流、功率和刀具速度）和参数之间的关系。工件尺寸精度。此外，还提出了内存细化、可分离性和参数重要性来评估框架的可解释性。混合 5 轴 LASERTEC 65 DED 数控机床上的实验结果表明，该机床不仅在表征 25.4 mm 沉孔特征偏差方面实现了 90.7% 的 F1 分数，而且还超越了其他 ML 模型，F1 分数裕度高达 73.0%。该框架的可解释性表明，对于几何尺寸表征，负载和扭矩的影响比功率和速度前馈的影响大 12 倍。提供了促进因果发现的潜力，并提供了对制造过程参数和零件质量之间关系的见解。