scholarly journals Detecting Teeth Defects on Automotive Gears Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8480
Author(s):  
Abdelrahman Allam ◽  
Medhat Moussa ◽  
Cole Tarry ◽  
Matthew Veres
Keyword(s):  
Domain Knowledge ◽  
Vision System ◽  
Inspection System ◽  
Automotive Systems ◽  
Vehicle Transmissions ◽  
Inspection Process ◽  
Time Requirements ◽  
Vital Component ◽  
Human Operators ◽  
Complex Mechanical Systems

Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear’s integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator’s inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%.

Intelligent Vision Inspection System for IC Engine Head: An ANN Approach

2012 ◽  
Vol 479-481 ◽  
pp. 2242-2245 ◽  
Author(s):  
Rajesh Kanna ◽  
Manikandan Saravana
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Back Propagation ◽  
Engine Block ◽  
Inspection System ◽  
Back Propagation Algorithm ◽  
Ic Engine ◽  
Vision Inspection ◽  
System Module ◽  
Artificial Neural Network Ann

A machine vision system based on Artificial Neural Network (ANN) for inspection of IC Engine block was developed to identify the misalignment and improper diminishing of holes in the IC Engine block. The developed machine vision and ANN module is compared with the commercial MATLAB® software and found results were satisfactory. This work is broadly divided into four stages, namely Intelligent inspection module, Machine Vision module, ANN module and Expert system module. A system with a camera was used to capture the various segments of head of the IC Engine block. The captured bitmap format image of IC Engine block has to be filtered to remove the noises present while capturing and the size is also altered using SPIHT method to an acceptable size and will be given as input to ANN. Generalized ANN with Back-propagation algorithm was used to inspect the IC Engine block. ANN has to be trained to provide the inspected report.

Automatic Inspection System of Small Plastic Gears Based on Machine Vision

2012 ◽  
Vol 522 ◽  
pp. 628-633 ◽  
Author(s):  
Jian Zhe Chen ◽  
Gui Tang Wang ◽  
Jian Qiang Chen ◽  
Xin Liang Yin
Keyword(s):  
Machine Vision ◽  
Template Matching ◽  
Vision System ◽  
Inspection System ◽  
Injection Molding Process ◽  
Automatic Inspection ◽  
Machine Vision System ◽  
Molding Process ◽  
Small Plastic ◽  
Plastic Gears

Small plastic gear is generally made by injection molding.But the injection molding process and mold have problems with missing tooth, shrink, more material, less material and inaccurate roundness and so on. Furthermore, using manual inspection will appear phenomenon of low efficiency, false detection and leak detection. To solve these problems, this paper introduces an automatic inspection system of small plastic gears based on machine vision. The system consists of feeding and sorting machine control system and machine vision inspection system of the gear defects. Mechanical control use digital servo control technology to achieve automatic nesting, feeding, positioning of gear workpiece, and depend on the inspection result of machine vision system to sort. After acquired gear image through a camera, Machine vision system uses median filtering, binarization, edge detection algorithms to process image. Then the system adopts template matching algorithm to obtain the inspection result and send the result to the sorting controller, which achieve automatic smart inspection of gear. The automatic inspection system has accurate, efficient, intelligent and other advantages.

High-Speed Machine Vision System Based on Human Sensibility Analysis for Automotive Cluster Dial Inspection

1995 ◽  
Vol 7 (3) ◽  
pp. 234-237
Author(s):  
Yoshio Yokoyama ◽  
◽  
Eiji Ichihashi
Keyword(s):  
High Speed ◽  
Vision System ◽  
Quality Criteria ◽  
Inspection System ◽  
Automated Visual Inspection ◽  
Appearance Quality ◽  
Manufacturing Line ◽  
Test Operation ◽  
Human Sensibility ◽  
Sensibility Analysis

This paper addresses with the problems in automated visual inspection of automotive instrument cluster dials for which appearance quality criteria are based on human sensibility. By analyzing the experimental human visual sensibility for defects, two types of defect identification method are proposed for pinhole and for particle and scratch. To execute these identification methods in real-time, a pipelined image processor composed of FPGA boards has been newly developed and introduced into the dial manufacturing line as an automatic inspection system. The result of test operation was satisfactory.

Machine Vision System for Inspecting Surgical Blades

2010 ◽  
Vol 139-141 ◽  
pp. 2067-2071
Author(s):  
Shu Yi Wang ◽  
Jie Tan ◽  
Xun Chao Yin ◽  
Hui Fang Wang
Keyword(s):  
Machine Vision ◽  
Template Matching ◽  
Precise Measurement ◽  
Vision System ◽  
Inspection System ◽  
Measuring Range ◽  
One Dimensional ◽  
Maximum Range ◽  
Matching Technique ◽  
Telecentric Lens

In this paper, a surgical blade inspecting system is designed and researched, which can automatic test and determine eligibility according to tolerances. High precision and non-contact detection of surgical blades based on machine vision is realized by using camera with telecentric lens to obtain the image of surgical blades, by applying template matching technique to automatic match surgical blades. One-dimensional edge of sub-pixel accuracy is extracted by combining the edge filtering of the Deriche filter and parabolic fitting for the maximum range from edge. It can be used for precise measurement of different part of surgical blades with different sizes. Extensive experiments showed that measuring range is from 2mm ~ 50mm, measuring accuracy is 10μm and average detection time is 0.6s. Thus the inspection system is fast, accurate and robust to fulfill the industrial demands.

Neural Networks Inspection System for Glass Bottles Production

1998 ◽  
Vol 12 (04) ◽  
pp. 505-516 ◽  
Author(s):  
D. Hamad ◽  
M. Betrouni ◽  
P. Biela ◽  
J.-G. Postaire
Keyword(s):  
Neural Networks ◽  
Vision System ◽  
High Rate ◽  
Second Step ◽  
Inspection System ◽  
Lighting System ◽  
Small Cracks ◽  
Glass Bottles ◽  
Rate Of Success ◽  
Very High

This paper describes a vision system that detects cracks in glass bottles production. The first step consists in collecting prototypes of bottles with and without defects. A sequence of 16 images is captured by a matrix camera while each bottle rotates in front of a specific lighting system. The second step is concerned with morphometric and photometric features extraction. The subsequent decision step is performed by different neural networks, such as MLP, RBF, PNN and LVQ. Finally, performances of these networks have been compared. All the images of bottles without defects have been recognized but a few images with small cracks, which are very important defects, have not been identified. However, since each bottle is represented by a sequence of 16 images, cracks will appear in at least three or four images, so that a defective bottle can be detected at least one time through the sequence. Therefore the decision system recognizes good and defective bottles with a very high rate of success.

scholarly journals Measurements of surface flatness distortions using machine vision inspection system

Mechanik ◽  
2017 ◽  
Vol 90 (12) ◽  
pp. 1155-1156
Author(s):  
Anna Zawada-Tomkiewicz ◽  
Dariusz Tomkiewicz ◽  
Lesław Wilk
Keyword(s):  
Thermal Treatment ◽  
Machine Vision ◽  
Vision System ◽  
Float Glass ◽  
Inspection System ◽  
Vision Inspection ◽  
Surface Flatness ◽  
Machine Vision Inspection

The use of a vision system for evaluating the flatness distortion of float glass under thermal treatment in a horizontal process is presented. The possibility of evaluation of such parameters as overall bow, roller wave and edge lift was analyzed for a pane of glass taken from production.

scholarly journals A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1895
Author(s):  
Donggyun Im ◽  
Sangkyu Lee ◽  
Homin Lee ◽  
Byungguan Yoon ◽  
Fayoung So ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Data Acquisition ◽  
Type I Error ◽  
Vision System ◽  
Structural Characteristics ◽  
Inspection System ◽  
Type I ◽  
Data Annotation ◽  
Background Removal

Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator’s structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion.

scholarly journals An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

2019 ◽  
Vol 9 (11) ◽  
pp. 2185 ◽  
Author(s):  
Jiange Liu ◽  
Tao Feng ◽  
Xia Fang ◽  
Sisi Huang ◽  
Jie Wang
Keyword(s):  
Neural Networks ◽  
Defect Detection ◽  
Vision System ◽  
Control Panel ◽  
Quality Inspection ◽  
Defect Inspection ◽  
Vision Inspection ◽  
Front End ◽  
Inspection Process ◽  
Front End Module

Automatic vision inspection technology shows a high potential for quality inspection, and has drawn great interest in micro-armature manufacturing. Given that the inspection process is highly influenced by the lack of real standardization and efficiency performed with the human eye, thus, it is necessary to develop an automatic defect detection process. In this work, an elaborated vision system for the defect inspection of micro-armatures used in smartphones was developed. It consists of two parts, the front-end module and the deep convolution neural networks (DCNNs) module, which are responsible for different areas. The front-end module runs first and the DCNNs module will not run if the output of the front-end module is negative. To verify the application of this system, an apparatus consisting of an objective table, control panel, and a camera connected to a Personal Computer (PC) was used to simulate an industrial position of production. The results indicate that the developed vision system is capable of defect detection of micro-armatures.

scholarly journals An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 644 ◽  
Author(s):  
Qinbang Zhou ◽  
Renwen Chen ◽  
Bin Huang ◽  
Chuan Liu ◽  
Jie Yu ◽  
...  
Keyword(s):  
Automobile Industry ◽  
Surface Defects ◽  
Hessian Matrix ◽  
Human Vision ◽  
Vehicle Body ◽  
Support Vector ◽  
Inspection System ◽  
Light Sources ◽  
Automatic Inspection ◽  
Inspection Process

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.

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