Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control meas...

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Vydané v:Operations Research Forum Ročník 5; číslo 3; s. 58
Hlavní autori: Wei, Li, Solihin, Mahmud Iwan, Saruchi, Sarah ‘Atifah, Astuti, Winda, Hong, Lim Wei, Kit, Ang Chun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.09.2024
Springer Nature B.V
Predmet:
Aerospace industry
Algorithms
Applications of Mathematics
Automation
Business and Management
Cameras
Catastrophic wear
Computer vision
Corrosion
Critical components
Data acquisition
Deep learning
Defects
Design
Image acquisition
Innovation and Infrastructure
Machine learning
Manufacturing
Math Applications in Computer Science
Mathematical and Computational Engineering
Neural networks
Operations Research/Decision Theory
Optimization
Performance degradation
Quality control
Review
State-of-the-art reviews
Surface defects
Topical Collection on Math for SDG 9 - Industry
Uniqueness
Deep learning
Computer vision
High-precision cylindrical parts
Image processing
Optical illumination
Anomaly detection
Defect detection
ISSN:2662-2556, 2662-2556
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Abstract High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation.
AbstractList High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation.
ArticleNumber 58
Author Kit, Ang Chun
Astuti, Winda
Saruchi, Sarah ‘Atifah
Solihin, Mahmud Iwan
Hong, Lim Wei
Wei, Li
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  givenname: Li
  surname: Wei
  fullname: Wei, Li
  organization: School of Computer Science and Technology (School of Software), Guangxi University of Science and Technology, Faculty of Engineering, Technology and Built Environment, UCSI University
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  givenname: Mahmud Iwan
  surname: Solihin
  fullname: Solihin, Mahmud Iwan
  email: mahmudis@ucsiuniversity.edu.my
  organization: Faculty of Engineering, Technology and Built Environment, UCSI University
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  givenname: Sarah ‘Atifah
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  fullname: Saruchi, Sarah ‘Atifah
  organization: Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah
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  givenname: Winda
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  fullname: Astuti, Winda
  organization: Computer Engineering Department, Automotive and Robotics Engineering Program, BINUS ASO School of Engineering, Bina Nusantara University
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  givenname: Ang Chun
  surname: Kit
  fullname: Kit, Ang Chun
  organization: Faculty of Engineering, Technology and Built Environment, UCSI University
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Keywords Deep learning
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High-precision cylindrical parts
Image processing
Optical illumination
Anomaly detection
Defect detection
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Snippet High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a...
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SubjectTerms Aerospace industry
Algorithms
Applications of Mathematics
Automation
Business and Management
Cameras
Catastrophic wear
Computer vision
Corrosion
Critical components
Data acquisition
Deep learning
Defects
Design
Image acquisition
Innovation and Infrastructure
Machine learning
Manufacturing
Math Applications in Computer Science
Mathematical and Computational Engineering
Neural networks
Operations Research/Decision Theory
Optimization
Performance degradation
Quality control
Review
State-of-the-art reviews
Surface defects
Topical Collection on Math for SDG 9 - Industry
Uniqueness
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Title Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review
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