A Micro-Computer Based Part/Tool Monitoring System for a Robotic Cell

1991 ◽  
Author(s):  
Saeid Motavalli ◽  
Behnam Bahr ◽  
Hamid M. Lankarani
Keyword(s):  
Tool Wear ◽  
Vision System ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
View Point ◽  
Robotic Cell ◽  
Specific Sequence ◽  
Tool Monitoring ◽  
Monitoring Camera ◽  
Wear Monitoring

Abstract This paper describes the elements of a unique robotic cell integrated with a vision system. The cell consists of a PUMA 560 robot, a vision system, and a conveyor belt. The robot end effector is a drill used in a drilling operation. The vision system has a two fold function: monitoring tool wear, and recognizing parts passing over the conveyor belt. Both tool monitoring and part recognition are performed using the vision system. The vision system is PC-based and uses two CCD video cameras. One camera is mounted vertically overlooking the conveyor belt, and the other camera is mounted horizontally for tool wear monitoring. Algorithms have been developed that interface the vision system with the robot. Whenever a part reaches the view point of the camera, an image of the part is captured. A recognition algorithm has been developed that recognizes the part and signals the robot to perform a specific sequence of operation on that part. The part is then moved to the drilling station where the PUMA robot perform the operation sequence according to the stored process plan. After each part is manufactured, the robot moves the drill to the view point of the tool monitoring camera, where an image of the drill head is captured. This image is then processed with the developed tool monitoring algorithm and tool wear is identified.

The Image Similarity Analysis Technique Applied to the Tool Wear Monitoring in Face Milling

2006 ◽  
Vol 315-316 ◽  
pp. 628-631
Author(s):  
Yu Teng Liang ◽  
C.J. Lo ◽  
W.C. Chen
Keyword(s):  
Tool Wear ◽  
Vision System ◽  
Image Data ◽  
Face Milling ◽  
Image Similarity ◽  
Similarity Analysis ◽  
Analysis Technique ◽  
Milling Operation ◽  
The Face ◽  
Wear Monitoring

The purpose of this paper is to monitor the tool wear based on the image data of cutting tool in the face milling operation. The surface images of the different coated inserted blade cutters are captured using a machine vision system incorporating with the mutual information and image similarity analysis technique for processing the images. The milling test is designed by using Taguchi’s method. The experimental results indicate that the coating layer factor is recognized to make the most significant contribution to the over all performance. The TiAlN-surface multilayer coated inserted blade cutter has the least wear rate amongst these coated milling cutters and has the longest tool life in this experiment.

A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture

2010 ◽  
Vol 154-155 ◽  
pp. 412-416 ◽  
Author(s):  
Zhong Ren Wang ◽  
Yu Feng Zou ◽  
Fan Zhang
Keyword(s):  
Tool Wear ◽  
Machine Vision ◽  
Vision System ◽  
Texture Feature ◽  
Tool Wear Monitoring ◽  
Machined Surface ◽  
Workpiece Surface ◽  
Tool Condition ◽  
Wear Monitoring ◽  
Feature Information

Machine vision technique is an advanced method for tool wear monitoring. In this article, a holding system has been designed and fabricated to realize the combination of machine tools and machine vision system. On-machine experiments were carried out to test the effect of this method. Experimental results indicate that tool condition monitoring can be successfully accomplished by analyzing texture feature information extracted from the machined surface.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring
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