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.

HiRA-Pro 是一种用于在智能制造中对来自不同信号的多模态数据进行高精度匹配的方法，通过识别和同步这些信号中显著的运动学和动力学事件来实现。它通过对齐数据，生成与物理体素对应的数据块，从而改善机器学习预测性能，提高分类准确率约 35%。