Process signature-driven high spatio-temporal resolution alignment of multimodal data
CoRR(2024)
摘要
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
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.
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