NDLI: Concurrent optimisation of a computer vision system’s multiple responses

Content Provider	Springer Nature Link
Author	Chakraborty, Shankar Bordoloi, Runumi
Copyright Year	2005
Abstract	Computer vision systems can effectively be used to inspect parts/products in unmanned manufacturing cells. Proper development and deployment of a computer vision system for product inspection looks for evaluation of its capability, which is based upon a number of responses such as accuracy and repeatability of the system. However, the performance of a vision system also depends on several factors, e.g. the type of the back-illumination system, colour of the background, distance between the vision camera and the object, angle between the optical axis of the camera and object, processing speed of the developed software etc. Since several factors can be controlled in a vision system’s setting, a number of combinations of the factor levels can be obtained. In this paper, Taguchi methods are used to determine the optimal levels of the controllable factors for the vision system. However, different settings are observed to be optimal for accuracy and repeatability responses. Hence, Harrington’s desirability function, total loss function and Taguchi quality loss function methods are simultaneously applied for multi-response optimisation analysis. The experimental results show that with concurrent optimisation of the vision system’s multiple responses, its performance can be effectively improved.
Starting Page	577
Ending Page	583
Page Count	7
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	28
Issue Number	5-6
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-04-06
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Concurrent optimisation Desirability function Quality loss function Taguchi methods Total loss function Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Industrial and Production Engineering Production/Logistics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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