AI 中文总结
本研究针对热锻造中工件直接跟踪不可靠的问题，提出结合深度学习视觉与事件驱动有限状态机的设备中心式工件定位框架，在实际工厂实现高事件检测准确率与可靠定位，支持可视化与定量分析。
AI 中文摘要
连续工件定位对于热锻造中的可追溯性和工艺协调至关重要，但由于极端温度、表面退化和不规则路径，直接跟踪不可靠。本研究提出一种设备中心式框架，该框架从多个静态2D相机观测的搬运设备推断工件位置，估算平面空间3D设备坐标并识别抓取和释放活动。事件驱动有限状态机将这些活动验证为离散搬运事件，持续更新工件状态和位置。集成到3D卷积神经网络中的关键点引导注意力机制，通过聚焦于功能相关的设备区域提升活动识别性能。在实际热锻造工厂的评估显示，在33秒容忍窗口内实现了100%的事件检测准确率，平均定位误差为317.8毫米，平均系统延迟为21秒。该框架将基于视觉的感知与可解释的事件驱动推理相结合，支持工件搬运的可视化及设备操作的定量分析。
英文摘要
Continuous workpiece localization is essential for traceability and process coordination in hot forging, but direct tracking is unreliable because of extreme temperatures, surface degradation, and irregular routing. This study presents an equipment-centric framework that infers workpiece locations from handling equipment observed by multiple static 2D cameras. The framework estimates floorplan-space 3D equipment coordinates and recognizes grasp and release activities. Event-driven finite state machines validate these activities as discrete handling events and continuously update workpiece states and locations. A keypoint-guided attention mechanism integrated into a 3D convolutional neural network improves activity recognition by focusing on functionally relevant equipment regions. Evaluation in an operational hot forging factory achieved 100\% event detection accuracy within a 33-second tolerance window, a mean localization error of 317.8 mm, and a mean system latency of 21 seconds. The framework connects vision-based perception with interpretable event-driven reasoning and supports visualization of workpiece transfers and quantitative analysis of equipment operations.
Comments21 pages, 14 figures, 9 tables. Published in The International Journal of Advanced Manufacturing Technology
Journal refInt J Adv Manuf Technol 142, 635-655 (2026)
DOI：10.1007/s00170-025-17047-9
