Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Kuric, Ivan; Klarák, Jaromír; Bulej, Vladimír; Sága, Milan; Kandera, Matej; Hajdučík, Adrián; Tucki, Karol

doi:10.3390/app12020864

Cited by 10 publications

(3 citation statements)

References 28 publications

(40 reference statements)

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“…Due to the layered nature of production, components have anisotropy that can be reduced or removed using appropriate heat treatment. AlSi10Mg is used in the aerospace and automotive industries due to its low weight [6][7][8][9][10].…”

Section: Equipment Materials and Methodsmentioning

confidence: 99%

Evaluation of Selected Technological Parameters for Selective Laser Melting of AlSi10Mg Metal Powder

Šutka¹,

Medvecká²,

Moravec³

et al. 2023

Manufacturing Technology

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Reusing the powder across consecutive route cycles is typical in a Selective Laser Melting (SLM) process since it is more sustainable and cost-effective. The theoretical part of the paper is oriented on the explanation of the SLM method principle, construction, and parameters of the device Renishaw AM250 used for manufacturing samples made of AlSi10Mg aluminium alloy. The experimental part focuses on evaluating powders that had been used in the process for a long time with periodic 'rejuvenation'. The main aim will be to monitor whether the samples will be affected and if they achieved the required quality of mechanical properties and compare results with virgin powder and manufacturer sheets for virgin powders. The experimental part further describes the procedures and analysis of the microstructure of the samples. AlSi10Mg aluminium alloy was chosen because of an assumption that it will be sensitive to increased oxygen content. A strong affinity for oxygen uptake exists in AlSi10Mg. Therefore, preventing the contamination, careful handling is required.

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Section: Equipment Materials and Methodsmentioning

confidence: 99%

Evaluation of Selected Technological Parameters for Selective Laser Melting of AlSi10Mg Metal Powder

Šutka¹,

Medvecká²,

Moravec³

et al. 2023

Manufacturing Technology

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“…Brili et al [ 38 ] used infrared imaging technology to obtain the state of the tool’s wear, and combined computer vision and deep learning methods to determine the tool’s state. Ivan Kuric et al [ 39 ] used the improved AlexNet to achieve the automatic target detection of small samples. Xian et al [ 40 ] used a new model of YOLOv3 combined with a triple loss network to improve the feature extraction capability of neural networks and achieve high-performance surface defect detection.…”

Section: Introductionmentioning

confidence: 99%

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet

Xue

Zhao

et al. 2022

Sensors

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Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 μm and 100 μm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.

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“…It is important to note that in these applications, the environment is controlled to minimize measurement errors. In some cases, special light arrangements are used to assist vision algorithms [5]. On the other hand, computer vision is also used for inspecting artifacts in a dynamic environment, such as agriculture [6].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision-Based Inspection System for Worker Training in Build and Construction Industry

Ercan

Wang

2022

Computers

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Recently computer vision has been applied in various fields of engineering successfully ranging from manufacturing to autonomous cars. A key player in this development is the achievements of the latest object detection and classification architectures. In this study, we utilized computer vision and the latest object detection techniques for an automated assessment system. It is developed to reduce the person-hours involved in worker training assessment. In our local building and construction industry, workers are required to be certificated for their technical skills in order to qualify working in this industry. For the qualification, they are required to go through a training and assessment process. During the assessment, trainees implement an assembly such as electrical wiring and wall-trunking by referring to technical drawings provided. Trainees’ work quality and correctness are then examined by a team of experts manually and visually, which is a time-consuming process. The system described in this paper aims to automate the assessment process to reduce the significant person-hours required during the assessment. We employed computer vision techniques to measure the dimensions, orientation, and position of the wall assembly produced hence speeding up the assessment process. A number of key parts and components are analyzed and their discrepancies from the technical drawing are reported as the assessment result. The performance of the developed system depends on the accurate detection of the wall assembly objects and their corner points. Corner points are used as reference points for the measurements, considering the shape of objects, in this particular application. However, conventional corner detection algorithms are founded upon pixel-based operations and they return many redundant or false corner points. In this study, we employed a hybrid approach using deep learning and conventional corner detection algorithms. Deep learning is employed to detect the whereabouts of objects as well as their reference corner points in the image. We then perform a search within these locations for potential corner points returned from the conventional corner detector algorithm. This approach resulted in highly accurate detection of reference points for measurements and evaluation of the assembly.

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Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Cited by 10 publications

References 28 publications

Evaluation of Selected Technological Parameters for Selective Laser Melting of AlSi10Mg Metal Powder

Evaluation of Selected Technological Parameters for Selective Laser Melting of AlSi10Mg Metal Powder

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet

Computer Vision-Based Inspection System for Worker Training in Build and Construction Industry

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