Application of deep learning in workpiece defect detection

Ye, Lanqing; Xia, Xiaojun; Chai, Bin; Wang, Shuai; Yang, Baofeng

doi:10.1016/j.procs.2021.02.058

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Existing procedures employed for detecting workpiece defects depend on manual practices involving human resources, resulting in a high False Positive/ True Negative rate. In order to unravel the overhead deliberation issue, an AI-driven defect detection system is employed here to augment the correct detection rate of workpiece flaws, minimize waste work, improve quality, and reduce the cost of workpiece production [8].…”

Section: Introductionmentioning

confidence: 99%

“…Considerable research work on the implementation of AI-driven defect detection systems in smart manufacturing factories has been carried out in the context of bulk production [5][6][7][8][9]. However, there is a dearth of research work on aerospace manufacturing factories, as manufacturing factories do not engage in bulk production.…”

Section: Introductionmentioning

confidence: 99%

“…In existing aerospace manufacturing factories, defect detection with respect to various components involves labor-intensive scrutiny, causing the achieved results to be inconsistent and subjective. Furthermore, due to manufacturing process-related issues and duplication of technology, workpieces in the workshop may develop defects, affecting the ornamental value and usefulness of the workpiece [8]. The cornerstone of a lean management philosophy is to reduce waste in the value chain in order to reduce total lead time, including all operations [10].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Shafi

Mazahir

Fatima

et al. 2023

Drones

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Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Shafi

Mazahir

Fatima

et al. 2023

Drones

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“…There is an urgent need to study the automatic digital detection technology of full surface defects. In recent years, machine vision has developed rapidly with the characteristics of high efficiency, visualization, traceability, non-contact, and high resolution [8][9][10]. Therefore, it is proposed to use machine vision to carry out the research on automatic digital detection technology of full surface defects.…”

mentioning

confidence: 99%

Research on Digital Testing Technology for Full Surface Defects of Cross scale Heteromorphic Metal Devices

Liu

Ma²,

Bai³

et al. 2023

J. Phys.: Conf. Ser.

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There are many types of cross scale special-shaped metal components in various fields, and the surface quality (defects) of some key components will affect the device performance and even cause the long-term stability and reliability of the system to decline. The existing manual inspection methods are difficult to accurately quantify and establish a digital traceability database, so it is urgent to study the automatic digital inspection technology of full surface defects. However, most of the devices are curved surfaces with different shapes, large size spans, various types of surface defects and diffuse reflection imaging, and the background of machine vision images is complex, which greatly increases the difficulty of defect detection. Based on the principle of machine vision imaging, this paper designs a long depth of field and low distortion imaging system for curved surfaces. Combined with a flexible scanning motion mechanism with multiple degrees of freedom, this paper studies the technology of complex background defect extraction to realize the full surface defect detection of cross scale special-shaped metal devices.

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A lightweight parallel attention residual network for tile defect recognition

Lv,

Zhang,

et al. 2024

Sci Rep

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Application of deep learning in workpiece defect detection

Cited by 7 publications

References 3 publications

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Research on Digital Testing Technology for Full Surface Defects of Cross scale Heteromorphic Metal Devices

A lightweight parallel attention residual network for tile defect recognition

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