Intelligent quality inspection methods for industrial applications

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University of New Brunswick


Visual quality inspection of manufactured parts is a complex, non-linear problem whose methods are mostly based on human intuition and heuristics. However, the demand for parts with ever-increasing quality and precision has exposed the need for better, more intelligent inspection methods, which has led to the adoption of machine vision-based inspection in a wide range of manufacturing fields. While these methods have led to notable improvements in quality inspection, the performance of a machine vision-based inspection system is intrinsically linked to how effectively it is able to image the part that it must evaluate. As such, an ideal inspection system must be able to observe the part in its entirety using the fewest images possible. While work has been done in an attempt to solve this problem, most approaches rely heavily on heuristic global optimization methods which are inherently unable to guarantee the optimality of their solutions, and whose solutions are often lacking in interpretability while also being computationally expensive. Additionally, the quality of the results of these systems is often extremely sensitive to variations in part geometry and environmental conditions. This research aims to postulate the quality inspection problem using set-based approaches, which will allow the application of interval analysis tools to develop robust optimal inspection methods. The proposed methods aim to rigorously generate inspection spaces which are not only guaranteed to contain feasible solutions but whose solutions can represent the true minimum number of required sensors. Preliminary results herein showcase the flaws inherent in traditional methods while also establishing the basis for the application of set-based methods. The proposed methods are first developed and tested in simulation and are also developed to the point of being able to be implemented for the optimal inspection of real parts in real manufacturing environments. This includes experimental validation of the methods as well as testing in real industrial situations to demonstrate their ability to generate real, usable camera deployments. These results show agreement between theoretical simulation results and real images, and demonstrate the ability of the method to be generically applied across a wide array of different geometries and environments.