Process signature-driven high spatio-temporal resolution alignment of multimodal data
Abhishek Hanchate, Himanshu Balhara, Vishal S. Chindepalli, Satish, T.S. Bukkapatnam

TL;DR
HiRA-Pro is a new high-resolution alignment method for multimodal signals in manufacturing, significantly improving synchronization precision and predictive accuracy over traditional techniques.
Contribution
The paper introduces HiRA-Pro, a novel process signature-based alignment method that achieves sub-millisecond and sub-100 micron resolution for multimodal data in complex systems.
Findings
Achieves 10-1000 us and 100 um resolution in data alignment.
Improves machine learning classification accuracy by nearly 35%.
Demonstrates effectiveness in additive manufacturing contexts.
Abstract
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…
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Taxonomy
TopicsAdvanced Computational Techniques and Applications · Distributed and Parallel Computing Systems
MethodsMatching The Statements
