scholarly journals An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

2022 ◽  
Vol 16 ◽  
pp. 141-150
Author(s):  
Cuili Mao ◽  
Wen Ma
Keyword(s):  
Machine Vision ◽  
Feature Recognition ◽  
Automatic Detection ◽  
Calibration Method ◽  
Specific Method ◽  
Quality Inspection ◽  
Surface Cracks ◽  
Feature Maps ◽  
Inspection Method ◽  
Workpiece Surface

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

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.

Ultrasonic Attenuation Based Inspection Method for Scale-up Production of A206–Al2O3 Metal Matrix Nanocomposites

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Jianguo Wu ◽  
Shiyu Zhou ◽  
Xiaochun Li
Keyword(s):  
Metal Matrix ◽  
High Performance ◽  
Ultrasonic Attenuation ◽  
Hypothesis Test ◽  
Scale Up ◽  
Al2o3 Nanoparticles ◽  
Quality Inspection ◽  
Ultrasonic Dispersion ◽  
Inspection Method ◽  
Metal Matrix Nanocomposites

A206–Al2O3 metal matrix nanocomposite (MMNC) is a promising high performance material with potential applications in various industries, such as automotive, aerospace, and defense. Al2O3 nanoparticles dispersed into molten Al using ultrasonic cavitation technique can enhance the nucleation of primary Al phase to reduce its grain size and modify the secondary intermetallic phases. To enable a scale-up production, an effective yet easy-to-implement quality inspection technique is needed to effectively evaluate the resultant microstructure of the MMNCs. At present the standard inspection technique is based on the microscopic images, which are costly and time-consuming to obtain. This paper investigates the relationship between the ultrasonic attenuation and the microstructures of pure A206 and Al2O3 reinforced MMNCs with/without ultrasonic dispersion. A hypothesis test based on an estimated attenuation variance was developed and it could accurately differentiate poor samples from good ones. This study provides useful guidelines to establish a new quality inspection technique for A206–Al2O3 nanocomposites using ultrasonic nondestructive testing method.

scholarly journals A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2732 ◽  
Author(s):  
Xinman Zhang ◽  
Jiayu Zhang ◽  
Mei Ma ◽  
Zhiqi Chen ◽  
Shuangling Yue ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Absolute Error ◽  
Detection Methods ◽  
Inspection System ◽  
Quality Inspection ◽  
Construction Site ◽  
High Definition ◽  
Steel Bar ◽  
Steel Bars

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

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