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Related Experiment Videos

Multimodal Sensor Fusion and Temporal Deep Learning for Computer Numerical Control Toolpath and Condition

Stephen S Eacuello1, Romesh S Prasad1, Manbir S Sodhi1

  • 1Department of Industrial Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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This study introduces a new AI model for classifying Computer Numerical Control (CNC) machine operations, achieving high accuracy even with reduced sensor data. The findings guide cost-effective sensor selection for CNC process monitoring.

Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Sensor Systems

Background:

  • Existing sensor-based studies rarely address the challenge of classifying specific Computer Numerical Control (CNC) machine operations.
  • Operation-type classification requires distinguishing toolpath strategies and cutting conditions from heterogeneous, noisy sensor data with varying modality discriminative value.

Purpose of the Study:

  • To conduct a systematic ablation study for a nine-class CNC toolpath and condition classification task.
  • To introduce and evaluate a novel Multimodal Denoising Temporal Attention Encoder with Long Short-Term Memory (MM-DTAE-LSTM) for handling multimodal fusion under sensor noise.
  • To determine necessary sensors and assess performance degradation with sensor removal for cost-effective monitoring system design.

Main Methods:

Keywords:
computer numerical control machiningcross-validationdeep learningdenoising autoencoderfeature ablationmultimodal sensor fusionprocess monitoringsensor modality selectiontemporal modeling

Related Experiment Videos

  • Collected 120 operation files from a desktop CNC mill instrumented with six distributed sensor units (inertial, acoustic, environmental, electrical).
  • Developed the MM-DTAE-LSTM model incorporating learned modality weighting, cross-modal attention, and a self-supervised denoising objective, followed by recurrent temporal modeling.
  • Performed systematic cross-validated ablation studies across five sensor-ablation levels and ten temporal resolutions, using file-level cross-validation.

Main Results:

  • MM-DTAE-LSTM maintained 92.5% classification accuracy with nearly half the sensor channels removed (56 of 110 features).
  • Simpler baseline models degraded by up to 10.7 percentage points under the same sensor reduction.
  • Analysis revealed pressure channels captured atmospheric variation, not machining dynamics, highlighting the importance of suppressing uninformative modalities.

Conclusions:

  • The MM-DTAE-LSTM model demonstrates robust performance in CNC operation classification, even with significant sensor reduction.
  • Findings provide concrete recommendations for sensor selection and deployment, enabling cost-effective CNC process monitoring with under USD 500 in hardware.
  • Further validation is needed for generalization to industrial machines, diverse materials, and production environments.