Automated data processing for efficient development of multimodal machine learning models in tool wear detection

This study aims to develop an automated data processing (ADP) framework that automatically processes multimodal data for machine learning (ML) diagnostics of manufacturing processes. The objective is to contribute to the state-of-the-art automated ML (AutoML) domain by developing a novel all-in-one...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology Vol. 139; no. 5-6; pp. 2505 - 2524
Main Authors: Valizadeh Sotubadi, Saleh, Mahjourian, Nazanin, Nguyen, Vinh
Format: Journal Article
Language:English
Published: London Springer London 01.07.2025
Springer Nature B.V
Subjects:
Accuracy
Automation
CAE) and Design
Computer-Aided Engineering (CAD
Cutting tools
Cutting wear
Data collection
Data processing
Data science
Datasets
Engineering
Genetic algorithms
Grinding tools
Industrial and Production Engineering
Literature reviews
Machine learning
Machine tools
Machinery condition monitoring
Manufacturing
Mean square errors
Mechanical Engineering
Media Management
Original Article
Performance evaluation
Researchers
Support vector machines
Temperature sensors
Tool wear
Turning (machining)
Binary genetic algorithm
Automated machine learning
Tool wear monitoring
Multi-armed bandit
ISSN:0268-3768, 1433-3015
Online Access:Get full text
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Summary:This study aims to develop an automated data processing (ADP) framework that automatically processes multimodal data for machine learning (ML) diagnostics of manufacturing processes. The objective is to contribute to the state-of-the-art automated ML (AutoML) domain by developing a novel all-in-one ML development framework that automatically processes data, trains ML models, and conducts evaluation based on prior process-based knowledge. For this purpose, the ADP framework was designed based on a higher-level decision-making unit and a lower-level machine learning unit to select the best set of data processing methods, ML models, and training strategies, defined as policies, for the machine tool monitoring. The ADP framework was tested with data collected from orthogonal tube turning experiments for cutting tool wear classification. The data were gathered using acceleration, acoustics, and temperature sensors mounted on a Computer Numerical Controller (CNC) lathe machine. The results demonstrate that the solutions selected by the ADP framework consistently exhibited similar performance as the global optimal policy with improved computational efficiency. Consequently, the proposed ADP framework was demonstrated to automatically conduct multiple steps within the ML development process without manual intervention for tool condition monitoring to facilitate democratization of ML development for manufacturers.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-025-16028-2