Weld defect inspection based on machine vision and weak magnetic technology

2021 ◽  
Vol 63 (9) ◽  
pp. 547-553
Author(s):  
Jing Ye ◽  
Guisuo Xia ◽  
Fang Liu ◽  
Ping Fu ◽  
Qiangqiang Cheng
Keyword(s):  
Machine Vision ◽  
Average Height ◽  
Defect Inspection ◽  
Welding Seam ◽  
Inspection Method ◽  
Weld Formation ◽  
Weld Defect ◽  
Inspection Process ◽  
Weld Area

This study proposes a weld defect inspection method based on a combination of machine vision and weak magnetic technology to inspect the quality of weld formation comprehensively. In accordance with the principle of laser triangulation, surface information about the weldment is obtained, the weld area is extracted using mutation characteristics of the weld edge and an algorithm for identifying defects with abnormal average height in the weld surface is proposed. Subsequently, a welding seam inspection process is developed and implemented, which is composed of a camera, a structured light sensor, a magnetic sensor and a motion control system. Inspection results from an austenitic stainless steel weldment show that the method combining machine vision and magnetism can identify defect locations accurately. Comprehensive analysis of the test results can effectively classify surface and internal defects, estimate the equivalent sizes of defects and evaluate the quality of weld formation in multiple dimensions.

Defect Inspection for SMD Inductors

2014 ◽  
Vol 635-637 ◽  
pp. 989-992
Author(s):  
Chun Li Chang ◽  
Wen Hong Wu ◽  
Rui Cian Weng ◽  
Chi Hung Hwang
Keyword(s):  
Machine Vision ◽  
High Frequency ◽  
Defect Inspection ◽  
Inspection Method ◽  
Electronic Products ◽  
Electrode Area ◽  
Vision Inspection ◽  
Defect Area ◽  
Machine Vision Inspection

This paper presents a machine vision inspection method for winding high frequency inductors, which affects the reliability and quality of the electronic products. This paper proposes how to quickly and correctly improve the quality of component detection, an important issue for surface mounted device (SMD) inductors manufacturers. SMD components easily damage the phenomenon of the electrode, and the brightness of the brightness of the damaged area of the electrode close to normal, not easy to be precise defect area separated from the electrode area.

INSPECTION AND QUALITY CHECKING OF CERAMIC CUP USING MACHINE VISION TECHNIQUE: DESIGN AND ANALYSIS

2017 ◽  
Vol 79 (5-2) ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Mohd Safirin Karis ◽  
Siti Azura Ahmad Tarusan ◽  
Gao-Jie Wong ◽  
Mohd Shahrieel Mohd Aras ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Machine Vision ◽  
Image Sensor ◽  
Histogram Method ◽  
Inspection Method ◽  
Inspection Process ◽  
Set Up ◽  
Vision Application ◽  
Vision Technique

The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.

Cracking of Heavy Wall Steam Separators During Fabrication: An Investigation

2012 ◽  
Author(s):  
Dilip Kumar ◽  
Luis Ganhao
Keyword(s):  
Cross Section ◽  
High Hardness ◽  
Radiographic Testing ◽  
Weld Defect ◽  
Section Viii ◽  
Carbon Arc ◽  
Recent Project ◽  
Weld Area ◽  
Non Destructive

On a recent project, four high pressure steam separator vessels were received from overseas after fabrication. There was suspicion on the quality of fabrication when non destructive examination (NDE) reports were reviewed. There were major concerns with the quality of radiographic films as they did not meet the ASMe Section VIII Div. 1 Code requirements as well as client specifications. Subsequent examination of welds using radiographic testing (RT) revealed crack-like features around nozzles in the region adjoining (but outside) the weld metal. Macro etching at the surface around nozzles showed that the weld area was extended beyond the apparent weld/base metal interface. Further examination of a cross section cut out from one vessel nozzle confirmed the initial doubts that weld repairs had been performed that were not reported. Metallography of the cross section indicated evidence of significant cracking associated with carbon contamination and very high hardness (up to 365 HV; in one particular case 609 HV) in affected areas. This was believed to be due to improper and incomplete cleaning by grinding after performing carbon arc or, flame gouging to remove a weld defect. Further detailed NDE was carried out using advanced ultrasonic testing (UT), i.e. phased array UT and time of flight diffraction (TOFD) and all defects (many new that were undetected by RT) were repaired per ASME Section VIII Div. 1 Code and client specification. This experience was a lesson for the design office and helped make a decision to be much more vigilant and to ask for greater quality surveillance on overseas fabrication of critical equipment for all future projects. The paper discusses the detailed investigation as well as findings.

scholarly journals DETECTION OF LOTUS ROOT CONTAMINANTS USING INTELLIGENT VISUAL MACHINE VISION TECHNIQUES

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Yuan Hao ◽  
Samuel Britwum Wilson ◽  
Emmanuel Asamoah ◽  
Jianrong Cia ◽  
Xukang Bao
Keyword(s):  
Machine Vision ◽  
Inspection System ◽  
Quality And Safety ◽  
Asian Continent ◽  
Detection Algorithms ◽  
Lotus Root ◽  
Water Plant ◽  
The World ◽  
Inspection Process

Lotus root, which is a water plant cherished by people in the Asian continent and some other parts of the world, is manually inspected for quality by experts to detect impurities. There is the need to update this inspection process in order to improve the quality and safety of lotus root. Machine vision systems and techniques are used for consistent, efficient, effective, and reliable inspection of images. The lotus root inspection system has been proposed to inspect the lotus roots for impurities. The detection algorithms use the size, shape, texture and color of the lotus root images as parameters to analyze the quality of lotus roots. The lotus root undergoes some processes before image acquisition and image processing. The camera and illumination used, in collaboration with the edge detection, and image segmentation techniques, efficiently and effectively exposed the impurities in the lotus root at a much faster rate. Also, it is less expensive compared to the traditional human inspections.

scholarly journals A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2940
Author(s):  
Luciano Ortenzi ◽  
Simone Figorilli ◽  
Corrado Costa ◽  
Federico Pallottino ◽  
Simona Violino ◽  
...  
Keyword(s):  
Machine Vision ◽  
Nearest Neighbors ◽  
Calibration Method ◽  
Mobile App ◽  
Harvest Time ◽  
Automatic Assessment ◽  
K Nearest Neighbors ◽  
Olive Ripeness ◽  
Olive Ripening

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

scholarly journals Inspection of Transparent Objects with Varying Light Scattering Using a Frangi Filter

2021 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
Dieter P. Gruber ◽  
Matthias Haselmann
Keyword(s):  
Light Scattering ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Production Testing ◽  
Transparent Objects ◽  
Frangi Filter ◽  
Anomaly Score ◽  
Difference Images ◽  
New Machine

This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.

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