Online prediction of composite material drilling quality based on multi-sensor fusion

The study introduces a novel online prediction method using a multi-sensor fusion approach for assessing the drilling quality of composite materials in real-time. The Multi-sensor Fusion Long Short-Term Memory (MFLSTM) model, which incorporates a Stacked Sparse Autoencoder (SSAE) within a Bayesian d...

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Bibliographic Details
Published in:Journal of intelligent manufacturing Vol. 36; no. 8; pp. 5889 - 5901
Main Authors: Liu, Wei, Cui, ·Jiacheng, Lu, Yongkang, Yin, Pengbo, Han, Lei, Jiang, Yingxin, Zhang, Yang
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2025
Springer Nature B.V
Subjects:
Algorithms
Business and Management
Composite materials
Control
Datasets
Deep learning
Drilling
Error reduction
Industrial applications
Machines
Machining
Manufacturing
Mechatronics
Methods
Multisensor fusion
Processes
Production
Quality control
Real time
Robotics
Sensors
Software
Test systems
Vibration measurement
Drilling quality prediction
Stacked sparse autoencoder (SSAE)
Multi-sensor fusion
Bayesian deep learning
Composite material
ISSN:0956-5515, 1572-8145
Online Access:Get full text
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Summary:The study introduces a novel online prediction method using a multi-sensor fusion approach for assessing the drilling quality of composite materials in real-time. The Multi-sensor Fusion Long Short-Term Memory (MFLSTM) model, which incorporates a Stacked Sparse Autoencoder (SSAE) within a Bayesian deep learning framework, was developed to manage the uncertainty inherent in composite material processing. Experimental validation, utilizing a specifically constructed dataset from multi-sensor data including force, temperature, and vibration measurements, demonstrates that our approach significantly enhances the predictability of hole quality during drilling. The MFLSTM model outperformed traditional machining process monitoring techniques by reducing prediction errors by over 25%, offering both accurate point predictions and reliable interval estimates. This method not only advances the intelligence of composite component manufacturing but also facilitates its industrial application through the development of supportive software.
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-024-02503-2