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.

scholarly journals Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4687 ◽  
Author(s):  
Yuan Yu ◽  
Jinsheng Ren ◽  
Qi Zhang ◽  
Weimin Yang ◽  
Zhiwei Jiao
Keyword(s):  
Image Segmentation ◽  
Machine Vision ◽  
High Precision ◽  
Clustering Algorithm ◽  
Dynamic Balance ◽  
Experimental Results ◽  
Quality Inspection ◽  
Point Evaluations ◽  
Smart Factories

The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the quality inspection of finished tires. In order to meet the high-precision requirements of the evaluation of tire marking point completeness in the smart factories, the K-means clustering algorithm is introduced to segment the image of marking points in this paper. The pixels within the contour of the marking point are weighted to calculate the marking point completeness on the basis of the image segmentation. The completeness is rated and evaluated by completeness calculation. The experimental results show that the accuracy of the marking point completeness ratings is 95%, and the accuracy of the marking point evaluations is 99%. The proposed method has an important guiding significance of practice to evaluate the tire marking point completeness during the tire quality inspection based on machine vision.

Colored rice quality inspection system using machine vision

2019 ◽  
Vol 88 ◽  
pp. 87-95 ◽  
Author(s):  
Shumian Chen ◽  
Juntao Xiong ◽  
Wentao Guo ◽  
Rongbin Bu ◽  
Zhenhui Zheng ◽  
...  
Keyword(s):  
Machine Vision ◽  
Inspection System ◽  
Quality Inspection ◽  
Rice Quality ◽  
Colored Rice

scholarly journals A New Method for Internal Force Detection of Steel Bars Covered by Concrete Based on the Metal Magnetic Memory Effect

Metals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 661 ◽  
Author(s):  
Caoyuan Pang ◽  
Jianting Zhou ◽  
Qingyuan Zhao ◽  
Ruiqiang Zhao ◽  
Zhuo Chen ◽  
...  
Keyword(s):  
Memory Effect ◽  
Internal Force ◽  
New Method ◽  
Detection Methods ◽  
Axial Position ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Force Detection ◽  
Steel Bar ◽  
Steel Bars

In this paper, the specimens of steel bars covered by concrete (SBCC) are taken as research objects, and a new method for steel bar internal force detection based on the metal magnetic memory effect is proposed. The variation law of the self-magnetic flux leakage (SMFL) signals on the surfaces of SBCC specimens with loading tension and the variation of the SMFL signals along the axial positions of specimens under different tensile forces are studied. The results show that when the loading tension is about 90% of the yield tension, the tangential component of the SMFL signal has a maximum extreme point. The distribution of the SMFL signals along the axial position shows a smooth curve, where the values at both ends are small while the intermediate values are large. This paper also proposes the use of the “area ratio deviation parameter” to quantitatively calculate the internal forces of the steel bars. This parameter shows a significant linear relationship with the loading tension during the strengthening stage of the specimens. This method can supplement the existing steel bar stress detection methods and has prospective research value.

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

scholarly journals Implementation and evaluation of an echo state network for a quality inspection system for laser welding

Procedia CIRP ◽  
2021 ◽  
Vol 99 ◽  
pp. 496-501
Author(s):  
Ivan Vishev ◽  
Claus-Philipp Feuring ◽  
Oliver Bringmann
Keyword(s):  
Laser Welding ◽  
Inspection System ◽  
Echo State Network ◽  
Quality Inspection
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