Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries

Zhao, Xinfeng; Zhao, Yangjing; Hu, Shunchang; Wang, Hongyan; Zhang, Yuyan; Ming, Wuyi

doi:10.3390/s23218780

Cited by 7 publications

(7 citation statements)

References 213 publications

(288 reference statements)

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“…Understanding the heat transfer mechanism is crucial for infrared detection. By deeply understanding the heat transfer model, the advantages and disadvantages of infrared imaging in various defect detection and classification applications can be analyzed more thoroughly [119]. At the same time, revealing the heat transfer mechanism helps to further improve the accuracy and applicability of infrared imaging.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen,

Wu,

et al. 2023

IEEE Access

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A printed circuit board (PCB) functions as a substrate essential for interconnecting and securing electronic components. Its widespread integration is evident in modern electronic devices, spanning computers, cell phones, televisions, digital cameras, and diverse apparatus. Ensuring product quality mandates meticulous defect inspection, a task exacerbated by the heightened precision of contemporary circuit boards, intensifying the challenge of defect detection. Conventional algorithms, hampered by inefficiency and limited accuracy, fall short of usage benchmarks. In contrast, PCB defect detection algorithms rooted in deep learning hold promise for achieving heightened accuracy and efficiency, bolstered by their adeptness at discerning novel defect types. This review presents a comprehensive analysis of machine vision-based PCB defect detection algorithms, traversing the realms of machine learning and deep learning. It commences by contextualizing and elucidating the significance of such algorithms, followed by an extensive exploration of their evolution within the machine vision framework, encompassing classification, comparison, and analysis of algorithmic principles, strengths, and weaknesses. Moreover, the introduction of widely used PCB defect detection datasets and assessment indices enhances the evaluation of algorithmic performance. Currently, the detection accuracy can exceed 95% at an Intersection over Union (IoU) of 0.5. Lastly, potential future research directions are identified to address the existing issues in the current algorithm. These directions include utilizing Transformers as a foundational framework for creating new algorithms and employing techniques like Generative Adversarial Networks (GANs) and reinforcement learning to enhance PCB defect detection performance.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen,

Wu,

et al. 2023

IEEE Access

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“…The system can control and produce a joint of the requested get diameter with a small error. A comparison between the CCC and CPC strateg been carried out in [48] with the CCC showing a better performance overall and able to deliver larger nuggets using the same amount of energy. Apart from nugget size regulation, in [71], the authors presented a novel control egy able to prevent expulsion.…”

Section: Process Control-closed-loop Methodsmentioning

confidence: 99%

“…Its utilization is limited to applications where access to the joint by the robot end-effector is possible and when short times between production steps are not required. In the same way, thermal vision monitoring has been also utilized for implementing active lock-in thermography [47,48]. The utilization of this system concerns battery welding applications only.…”

Section: Practices and Norms In Manufacturingmentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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Resistance spot welding is a process with high variability regarding the quality of the produced joints. This means that key performance indicators (KPIs) such as geometrical and mechanical features as well as failure modes can deviate from the initial design even if the same process parameters are used. The industry has developed quality assurance programs and quality control methods for tracking these KPIs; however, most of them are based on offline or/and destructive practices. On the other hand, state-of-the-art approaches have made online quality assessment feasible and proved its necessity if a 100% quality rate is required. However, limited attention has been given to “closing the loop” and providing feedback for preventing and correcting process anomalies that cause quality variations in real time. In this study, the main gaps between the state of practice and the state of the art are discussed in the context of quality assurance for resistance spot welding. Finally, the role and importance of digital twins by taking into consideration the entire welding ecosystem in quality assurance are discussed in order to form the prospects for the road ahead.

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“…Traditional machine learning operates effectively with smaller datasets [18,129]. DL requires relatively large amounts of data, especially in deep neural networks [130]. Compared with traditional machine learning, DL is able to automatically learn features from raw data, which can result in richer information and higher-level feature representations, thus improving model performance.…”

Section: Vs Traditional ML and Physical Modelsmentioning

confidence: 99%

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Zhao,

Wang,

Bai

et al. 2024

Water

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Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.

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Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries

Cited by 7 publications

References 213 publications

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

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