Intelligent vision-based online inspection system of screw-fastening operations in light-gauge steel frame manufacturing

2020 ◽  
Vol 109 (3-4) ◽  
pp. 645-657
Author(s):  
Pablo Martinez ◽  
Mohamed Al-Hussein ◽  
Rafiq Ahmad
1998 ◽  
Author(s):  
Chuan-Yuan Chung ◽  
Wen-Chung Cheng ◽  
Wen-Ching Chao

2010 ◽  
Vol 152-153 ◽  
pp. 368-371
Author(s):  
Chun Dong Zhu ◽  
Fei Guo ◽  
Bo Wei

Gearbox sliding bracket is an important component of Automobile Gearbox. Because it has many parts which have little difference in shape and size, it is very easy to make mistake in assembly parts of this component. Due to mass production, it is high time to develop an online inspection system for the aseembly quality. Analysis the assembly characteristic of the component, this paper develop an online inspection system which is mainly composed of a central control system(CCS) and a sensor. Under the command of the CCS, the sensor is able to automatically inspect the component’s parts of which are wrong assmbly, reverse assembly and missing assmbly. Also the sensor can send the inspection information to the CCS. Subsequently, the CCS judges the information and dispalys the judgment result in time. At the same time, through the network, it is able to send the assembly quality information to the enterprise quality management system, such as the eligible amount, the reject amount, the qualification rate. This online automatic inspection system has run in good condition since it was officially put into operation at 2007 and its accuracy comes up to 100%.


1996 ◽  
Author(s):  
Congzhou Zhang ◽  
Zhiyong An ◽  
Gouyu Zhang ◽  
Xiping Xu

1998 ◽  
Author(s):  
Guoyu Zhang ◽  
Jiawu Song ◽  
Zhiyong An ◽  
Chengzhi Li ◽  
Yujin Gao ◽  
...  

1996 ◽  
Author(s):  
Guoyu Zhang ◽  
Zhiyong An ◽  
Tiejun Zhao ◽  
Xiaoman Wang

2005 ◽  
Vol 127 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Gil Abramovich ◽  
Juyang Weng ◽  
Debasish Dutta

We present a novel online inspection method for manufacturing processes that automatically adapts to variations in part and environmental properties. This method is based on a developmental learning architecture comprising a procedure that focuses attention to apparently defective regions, a recognition method that performs automatic feature derivation based on a set of training images and hierarchical classification, and an action step that controls attention and further decision processes. The method adapts to variations incrementally by updating rather than recreating the training information. Also, the method is capable of inspecting and training simultaneously. Addressing new inspection tasks requires neither re-programming and compatibility tests, nor quantitative knowledge about the image set, from a human developer. Instead, automatic or manual training of the inspection system according to simple guidelines is applied. These attributes allow the method to improve online performance with minimal ramp-up time. Our system performed inspection of three applications with low error rate and fast recognition, confirming its suitability for general-purpose, real-time, online inspection.


2013 ◽  
Vol 46 (4) ◽  
pp. 232
Author(s):  
Jian wei Ma ◽  
Zhen yuan Jia ◽  
Fu ji Wang ◽  
Yi ming Ding

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