Bringing Machine Intelligence to Welding Visual Inspection: Development of Low-Cost Portable Embedded Device for Welding Quality Control

Gong, Yifu; Lin, Zhibin; Wang, Jinhui; Gong, Na

doi:10.2352/issn.2470-1173.2018.09.iriacv-279

Cited by 10 publications

(6 citation statements)

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“…In another application by Ardhy and Hariadi [5], Raspberry Pi, Python, and OpenCV integration has been utilized to inspect Printed Circuit Board (PCB) defects and authors suggested Adaptive Gaussian Threshold as the best defect identifier. The study for welding visual inspection by Gong et al [6] also suggests a low-cost system where they inspect the quality of welding on circuits via a Support Vector Machine Algorithm. Korodi et al [7] deploy another low-cost visual system to control quality Electronic Control Unit (ECU) control in the automotive industry.…”

Section: Machine Vision Supported Quality Controlmentioning

confidence: 99%

Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

KARACAN¹,

ERDOĞAN²,

İĞDİL³

et al. 2021

Natural and Applied Sciences Journal

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Emerging in the past few decades, Industry 4.0 has wide effects over production lines with an increasing number of novel applications. These applications implement more than one of the tools of Industry 4.0. These tools include but are not limited to internet of things (IoT), big data, cloud computing, artificial intelligence, augmented reality, virtual reality, machine to machine communication (M2M), smart robot applications, etc. The aim of these efforts is mainly to acquire smarter manufacturing systems. With spreading Industry 4.0 methodologies, the role of sensors became more important to respond to new demands. One of the most important sensor types in this sense is the camera which now has wide variants in different forms. Applying machine vision algorithms via cameras grants optimization of many critical processes. In this perspective, the quality of both product and process should be handled as a key performance indicator that may be continuously enhanced for excellence. Machine vision algorithms may be adapted to check and manage quality in designated control points on the production lines. This study focuses on the control of the quality of rotary switches that are widely used in household appliances like ovens and washing machines. Rotary switches are critical components of an appliance since they direct the flow of electricity within the product. A failure in the functionality of this component directly causes the failure of the main product. Hence, the quality rate of rotary switches should be calculated in defective parts per million (dppm) units. An intense quality control procedure is required to achieve low dppm rates during production. As a real-life application, a camera system is integrated into the rotary switch production line on a selected point. Classification algorithms are developed on a cost-effective platform to perform visual quality checks of the rotary switches and qualify as "Ok" or "Defective". The selected point ensures a high percent check of quality criteria while enabling repair of the defective parts with minor interventions. This control aims to identify a defective rotary switch as soon as possible since most of the defects are irreversible IDUNAS

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Section: Machine Vision Supported Quality Controlmentioning

confidence: 99%

Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

KARACAN¹,

ERDOĞAN²,

İĞDİL³

et al. 2021

Natural and Applied Sciences Journal

View full text Add to dashboard Cite

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“…Within the inspection process in an industry, the detection of defects has a very important role because it approves or rejects parts produced in factories or delivered by suppliers. It also helps to reduce material wastage because it can include the rework and repair of parts [1], even though within machine learning there are several options for solving defect detection problems, such as support vector machines (SVMs) in the metal industry [2], cellular neural networks (CNNs) in the metal industry [3], or using different image processing algorithms in the metal industry [4]. Based on the information collected in [5], CNNs stand out in a number of existing studies, result in the extraction of information from images, and outperform other traditional machine learning architectures; therefore, CNNs were chosen as our starting point.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Cumbajin,

Rodrigues,

Costa

et al. 2023

J. Imaging

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Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection.

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“…Welding is one of the major connection methods used for metallic structures [ 1 , 2 ], including connecting oil/gas metallic pipelines [ 3 , 4 , 5 ]. Due to the nature of the complex welding process on shop and/or construction sites, different types of welding defects are often reported, including incomplete penetration, lack of fusion, cracking, and undercut.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Empowered Structural Health Monitoring and Damage Diagnostics for Structures with Weldment via Decoding Ultrasonic Guided Wave

Zhang

Pan

Wang

et al. 2022

Sensors

Self Cite

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Welding is widely used in the connection of metallic structures, including welded joints in oil/gas metallic pipelines and other structures. The welding process is vulnerable to the inclusion of different types of welding defects, such as lack of penetration and undercut. These defects often initialize early-age cracking and induced corrosion. Moreover, welding-induced defects often accompany other types of mechanical damage, thereby leading to more challenges in damage detection. As such, identification of weldment defects and interaction with other mechanical damages at their early stage is crucial to ensure structural integrity and avoid potential premature failure. The current strategies of damage identification are achieved using ultrasonic guided wave approaches that rely on a change in physical parameters of propagating waves to discriminate as to whether there exist damaged states or not. However, the inherently complex nature of weldment, the complication of damages interactions, and large-scale/long span structural components integrated with structure uncertainties pose great challenges in data interpretation and making an informed decision. Artificial intelligence and machine learning have recently become emerging methods for data fusion, with great potential for structural signal processing through decoding ultrasonic guided waves. Therefore, this study aimed to employ the deep learning method, convolutional neural network (CNN), for better characterization of damage features in terms of welding defect type, severity, locations, and interaction with other damage types. The architecture of the CNN was set up to provide an effective classifier for data representation and data fusion. A total of 16 damage states were designed for training and calibrating the accuracy of the proposed method. The results revealed that the deep learning method enables effectively and automatically extracting features of ultrasonic guided waves and yielding high precise prediction for damage detection of structures with welding defects in complex situations. In addition, the effectiveness and robustness of the proposed methods for structure uncertainties using different embedding materials, and data under noise interference, was also validated and findings demonstrated that the proposed deep learning methods still exhibited a high accuracy at high noise levels.

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Bringing Machine Intelligence to Welding Visual Inspection: Development of Low-Cost Portable Embedded Device for Welding Quality Control

Cited by 10 publications

References 0 publications

Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Deep Learning Empowered Structural Health Monitoring and Damage Diagnostics for Structures with Weldment via Decoding Ultrasonic Guided Wave

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