发表机构
University of Oklahoma（俄克拉荷马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出首个金属增材制造熔池监测事件相机基准SynAM-E，含85个多源模拟事件片段，单机精度与密集帧相当，跨机任务含防相机捷径信任测试。
AI 中文摘要
熔池监测对于金属增材制造（AM）的质量认证至关重要，然而目前该领域尚无公开的事件相机基准。事件相机以微秒级时间分辨率报告每个像素的亮度变化，而非读取完整帧，从而以极低的数据率提供增材制造瞬态过程所需的时间分辨率。我们提出了SynAM-E（合成增材制造事件），这是首个用于金属增材制造熔池监测的公开多源模拟事件相机基准：包含来自8个机构的15个源的85个物理校准事件片段，并配有公开基线和固定的跨机器评估划分。在单机案例研究中，事件空间监测达到了与密集帧相当的精度（宏F1分数0.874对比0.863），而事件所丢弃的绝对强度在融合下仅增加+0.006。在NIST增材制造计量测试台（AMMT）构建中，一个近传感器事件率计数器恢复了原始帧确认的528.7 Hz强度振荡，其传感器读取量约为帧流所需读取量的380分之一。一个紧凑的93千参数尖峰模型以15倍低的建模推理能量运行，代价是宏F1分数降低0.073。每个跨源任务都包含针对相机身份捷径的内置信任测试：过程类型分类通过，而材料分类仍受相机波段混淆，这是语料库结构性的局限，发布文档已记录且信任测试已揭示。
英文摘要
Melt-pool monitoring is central to qualifying metal additive manufacturing (AM), yet no public event-camera benchmark exists for this domain. Event cameras report per-pixel brightness changes with microsecond timing instead of reading full frames, giving the temporal resolution AM transients demand at a fraction of the data rate. We present SynAM-E (Synthetic AM Events), the first public multi-source simulated event-camera benchmark for metal-AM melt-pool monitoring: 85 physics-calibrated event shards from 15 sources across 8 institutions, with public baselines and fixed cross-machine evaluation splits. On a single-machine case study, event-spatial monitoring matches dense-frame accuracy (0.874 versus 0.863 macro-F1), and the absolute intensity that events discard adds only +0.006 under fusion. On the NIST Additive Manufacturing Metrology Testbed (AMMT) build, a near-sensor event-rate counter recovers a raw-frame-confirmed 528.7 Hz intensity oscillation at ~380 times less sensor readout than the frame stream requires. A compact 93 k-parameter spiking model runs at 15 times lower modeled inference energy for a 0.073 macro-F1 cost. Every cross-source task includes a built-in trust test against camera identity shortcuts: process-type classification passes while material classification remains confounded by camera band, a corpus-structural limitation the release documents and the trust test exposes.
