Concurrent optimisation of a computer vision system’s multiple responses

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.