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Updated: Nov 22, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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MOSS-Multi-Modal Best Subset Modeling in Smart Manufacturing.

Lening Wang1, Pang Du2, Ran Jin1

  • 1Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA.

Sensors (Basel, Switzerland)
|January 6, 2021
PubMed
Summary

Smart manufacturing relies on multi-sensing systems for quality control. This study introduces Multi-mOdal beSt Subset modeling (MOSS) to select optimal sensors, improving quality-process models and reducing costs.

Keywords:
data fusionfused deposition modelingmulti-modal sensingquality modelingsmart manufacturing

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Area of Science:

  • Manufacturing Engineering
  • Data Science
  • Industrial Engineering

Background:

  • Smart manufacturing utilizes multi-sensing systems for real-time decision-making and quality improvement.
  • Cost constraints often limit the number of sensors in industrial processes.
  • Interpreting the relationship between sensor data and quality outcomes is crucial for effective modeling.

Purpose of the Study:

  • To develop a method for selecting the most relevant sensor modalities from multi-modal sensing systems in smart manufacturing.
  • To improve the accuracy of quality-process relationship modeling by identifying key sensor inputs.
  • To provide a data-driven approach for sensor placement strategies.

Main Methods:

  • Proposed a novel model named Multi-mOdal beSt Subset modeling (MOSS).
  • Employed the concept of best subset variable selection.
  • Utilized functional norms to characterize the effects of individual sensor modalities.

Main Results:

  • MOSS effectively selects important sensor modalities, enhancing quality-process modeling accuracy.
  • The model identifies sensor modalities most correlated with quality variations.
  • Demonstrated effectiveness through simulations and a real-world additive manufacturing case study.

Conclusions:

  • The MOSS model offers an efficient approach to sensor modality selection in smart manufacturing.
  • Sensor modality significance derived from MOSS can guide sensor placement strategies.
  • The method improves the interpretability of quality-process relationships, identifying root causes of variations.