Machine Vision based quality inspection for automotive parts using edge detection technique

Shankar, Rahul; Indra, J.; Oviya, R; Heeraj, A; Ragunathan, R

doi:10.1088/1757-899x/1055/1/012029

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…A conventional computer vision approach is implementing the following algorithm [ 20 , 21 ]: Image acquisition Circle and point edge detection Length and radius measurements Feature collection Matching features Generate the verdict and store it in a database …”

Section: Related Workmentioning

confidence: 99%

“…The human–machine interface was implemented using the PyQt framework (PyQt5-Qt5 version 5.15.2, developed by Riverbank Computing, open-source), which is a Python wrapper of the open-source framework Qt developed by the Qt company. Software was designed to cover all of the manufacturing necessities, e.g., logging, user management, process handling, and so on [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. In Figure 6 , a complete sequence diagram of the process can be observed.…”

Section: Solution Overviewmentioning

confidence: 99%

“…In Figure 7 , an architecture of the neural network is proposed [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Solution Overviewmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

Abagiu

Cojocaru

Manta

et al. 2023

Sensors

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This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection performed by a human operator with a computer vision application. We started by exploring different machine vision applications used in the manufacturing environment for several types of operations, and how machine learning is being used in robotic industrial applications. The solution implementation is re-using hardware that is already available at the manufacturing plant and decommissioned from another system. The re-used components are the cameras, the IO (Input/Output) Ethernet module, sensors, cables, and other accessories. The hardware will be used in the acquisition of the images, and for processing, a new system will be implemented with a human–machine interface, user controls, and communication with the main production line. Main results and conclusions highlight the efficiency of the CCD (charged-coupled device) sensors in the manufacturing environment and the robustness of the machine learning algorithms (convolutional neural networks) implemented in computer vision applications (thresholding and regions of interest).

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Section: Related Workmentioning

confidence: 99%

Section: Solution Overviewmentioning

confidence: 99%

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Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

Abagiu

Cojocaru

Manta

et al. 2023

Sensors

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“…The structural method is based on detecting edge and boundary defects in an image. This method includes the following methods: costal [73,74], skeletal [75]. Detection methods are based on region, texture and edge based spectrs and filters.…”

Section: Introductionmentioning

confidence: 99%

An approach for recognizing surface defects in products with a complex helical shape

Pivkin,

Yazev,

Grechishnikov

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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A key functional role is served by the helical surfaces of carbide end mills that can be manufactured during diamond grinding wheel. Localized changes in the form of the helical surface can be caused by abrasion, high pressure, and grinding wheel wear. Therefore, it is extremely important to measure the physical samples of products with a helical surface according to the criterion of profile accuracy, rake angle and core diameter. A specialized inspection machine in reflected light can be used to obtain images across the helical groove. Manually extracting a number of defects from photos takes time. Using defect recognition algorithms, effective and quick quality control of a ground helical surface can be established. As a result, effective surface quality control can be achieved in the machine tool industry. In this study, an innovative approach to determine a defect's shape and location as well as an algorithm for removing it are presented. Both of these approaches are integrated into the technological process used to manufacture products with helical surfaces. With the goal to recognized create suggestions for image analysis using different image levels, the suggested approach provides logically smoothing histograms and limiting contrast as an image pre-processing, based on an analysis of images with useful and faulty parts. Achieved successful extraction of areas of adhesive, diffusion, abrasion and chips from the image through post-processing. The article presents a new approach to recognizing adhesive and diffusion defects on the helical surface of a mill after grinding. When developing this approach, it was revealed that areas with alternating profile changes are most susceptible to the formation of defects under conditions of increased heating of the working area, and specialized inductors for searching for defects in localized areas according to the criterion of pixel brightness intensity were proposed.

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“…However, due to the extremely complex gear failure form, the vibration acceleration signal extracted by the sensor has non-stationary characteristics, and the energy reflecting the state information is also very weak, which brings difficulties to the gear fault diagnosis [7,8]. At the same time, with the improvement of the computer industry, machine vision inspection is widely used in non-destructive testing and other fields due to its advantages of non-contact, real-time, online, high accuracy, etc [9,10]. The failure of gears such as tooth wear, pitting and gluing occurs mainly on the gear tooth surface.…”

Section: Introductionmentioning

confidence: 99%

Bidimensional local characteristic-scale decomposition and its application in gear surface defect detection

Liu,

Cheng,

2023

Meas. Sci. Technol.

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Visual image-based inspection methods can directly reflect the type of defects on the surface of gears. However, these methods have many problems: Firstly, as a two-dimensional signal, the data volume of images is large and the processing is relatively time-consuming. Although some existing image signal processing methods (e.g., bidimensional empirical mode decomposition (BEMD)) have good decomposition results, their decomposition speed is slow. The bidimensional local characteristic-scale decomposition (BLCD) method is proposed in this paper, which adaptively decomposes an image from high to low frequencies into several bidimensional intrinsic scale components (BISC). It is demonstrated that the BLCD method maintains the advantages of the BEMD method in terms of good decomposition ability and adaptive capability while significantly reducing the processing time and improving the computational efficiency. Secondly, in the running state of the gears, the obtained images sometimes contain noise, which is not convenient for detecting surface defect types. A gear surface defect detection method based on BLCD image denoising is proposed in this paper. Firstly, it uses the BLCD denoising module for preprocessing to provide high signal-to-noise ratio images for the subsequent detection module, and then uses the detection module for defect identification and classification. Experiments prove that the BLCD denoising module has excellent performance and it is well coupled with the detection module, giving the whole method higher accuracy and stability than other classification methods.

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Machine Vision based quality inspection for automotive parts using edge detection technique

Cited by 9 publications

References 12 publications

Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

An approach for recognizing surface defects in products with a complex helical shape

Bidimensional local characteristic-scale decomposition and its application in gear surface defect detection

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