We present HiRA-Pro, a novel procedure to align, at high spatio-temporal resolutions, multimodal signals from real-world processes and systems that exhibit diverse transient, nonlinear stochastic dynamics, such as manufacturing machines. It is based on discerning and synchronizing the process signatures of salient kinematic and dynamic events in these disparate signals. HiRA-Pro addresses the challenge of aligning data with sub-millisecond phenomena, where traditional timestamp, external trigger, or clock-based alignment methods fall short. The effectiveness of HiRA-Pro is demonstrated in a smart manufacturing context, where it aligns data from 13+ channels acquired during 3D-printing and milling operations on an Optomec-LENS MTS 500 hybrid machine. The aligned data is then voxelized to generate 0.25 second aligned data chunks that correspond to physical voxels on the produced part. The superiority of HiRA-Pro is further showcased through case studies in additive manufacturing, demonstrating improved machine learning-based predictive performance due to precise multimodal data alignment. Specifically, testing classification accuracies improved by almost 35% with the application of HiRA-Pro, even with limited data, allowing for precise localization of artifacts. The paper also provides a comprehensive discussion on the proposed method, its applications, and comparative qualitative analysis with a few other alignment methods. HiRA-Pro achieves temporal-spatial resolutions of 10-1000 us and 100 um in order to generate datasets that register with physical voxels on the 3D-printed and milled part. These resolutions are at least an order of magnitude finer than the existing alignment methods that employ individual timestamps, statistical correlations, or common clocks, which achieve precision of hundreds of milliseconds.

翻译：我们提出了HiRA-Pro，一种新颖的流程，用于在高时空分辨率下对齐来自现实世界过程和系统的多模态信号，这些系统表现出多样的瞬态、非线性随机动态，例如制造机器。其基础是通过识别和同步这些不同信号中显著运动与动态事件的过程特征。HiRA-Pro解决了亚毫秒级现象数据的对齐挑战，而传统基于时间戳、外部触发或时钟的对齐方法在此类场景中难以胜任。HiRA-Pro的有效性在智能制造背景下得到验证，它在Optomec-LENS MTS 500混合式机器上进行3D打印和铣削操作时，对齐了来自13个以上通道的数据。随后，对齐的数据被体素化，生成0.25秒的对齐数据块，这些数据块对应于所生产零件上的实体体素。通过增材制造中的案例研究，进一步展示了HiRA-Pro的优越性，证明了由于精确的多模态数据对齐，基于机器学习的预测性能得到提升。具体而言，即使数据有限，应用HiRA-Pro后测试分类准确率提高了近35%，从而实现了对缺陷的精确定位。本文还对该方法及其应用进行了全面讨论，并与其他几种对齐方法进行了定性比较分析。HiRA-Pro实现了10-1000微秒的时间分辨率和100微米的空间分辨率，以生成与3D打印和铣削零件上实体体素对齐的数据集。这些分辨率至少比现有使用独立时间戳、统计相关性或公共时钟的对齐方法（其精度达到数百毫秒）高一个数量级。