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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Vydané v:	Journal of intelligent manufacturing Ročník 36; číslo 8; s. 5889 - 5901
Hlavní autori:	Liu, Wei, Cui, ·Jiacheng, Lu, Yongkang, Yin, Pengbo, Han, Lei, Jiang, Yingxin, Zhang, Yang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2025 Springer Nature B.V
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Cui, ·Jiacheng Zhang, Yang Lu, Yongkang Liu, Wei Han, Lei Yin, Pengbo Jiang, Yingxin
Author_xml	– sequence: 1 givenname: Wei surname: Liu fullname: Liu, Wei organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology – sequence: 2 givenname: ·Jiacheng surname: Cui fullname: Cui, ·Jiacheng organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology – sequence: 3 givenname: Yongkang orcidid: 0000-0002-2906-5591 surname: Lu fullname: Lu, Yongkang email: lyk2024@dlut.edu.cn organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology – sequence: 4 givenname: Pengbo surname: Yin fullname: Yin, Pengbo organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology – sequence: 5 givenname: Lei surname: Han fullname: Han, Lei organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology – sequence: 6 givenname: Yingxin surname: Jiang fullname: Jiang, Yingxin organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology – sequence: 7 givenname: Yang surname: Zhang fullname: Zhang, Yang organization: School of Mechanical Engineering, Dalian University of Technology, State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology
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SubjectTerms	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
Title	Online prediction of composite material drilling quality based on multi-sensor fusion
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