Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

For manufacturers to stay competitive, increased levels of automation are a must for survival. Machine vision (MV) is one of the key tools in the automation toolkit as it provides for image-based inspection and analysis of parts and the machines that produce those parts. The work will be broken down into two projects, and will lead to the training of 2 PhD, 3 MSc and 5 BSc students. The overall goal is to advance the state of the art of MV-based systems with novel algorithms and system designs that will create the basis for greater acceptance of automation technology by Canadian industry.

Project A is on the subject of MV for part inspection. For decades it has been recognized that there is a need for an adaptive machine vision (AMV) system, one that can be implemented in different applications without extensive retuning. AMV part inspection systems that have been developed to date have been pseudo adaptive, in the sense that they were not tested on different applications, but instead tested on different models of the same part. The goal of this project is to develop a truly adaptive system and test it on 3 different applications: coins, gears and varistors. The initial application is a particularly challenging problem where the “part” is an Indian coin. For testing purposes, coins will be placed on a moving conveyor to mimic a manufacturing operation. The system will be required to recognize the coins and sort them, in real time. A traditional MV system has two parts: 1) feature selection with images as the input and 2) classification with features as the input. Based upon experience to date, the proposed AMV system will use a Contingency-based feature selector and a novel Fuzzy Decision Tree-based classifier. Performance will be benchmarked against AlexNet (a non-traditional Deep Neural Net).

Project B is on the subject of MV for machine fault detection. Automated assembly machines operate around-the-clock to achieve high production rates. Continuous operation results in high mechanical wear that can led to machine faults. Traditional fault detection methods check for deviations from fixed threshold limits with multiple conventional sensors. The goal of this project is to develop a MV-based detection system to detect known and unknown faults with a single camera. A high speed industrial assembly machine is available for this work. The proposed approach will be based upon the Gaussian Mixture Model (GMM) method for video analysis. The analysis sets out to identify images that deviate from the normal, in other words a “fault”. The images are then analyzed to find out where the difference has occurred. This localization stage will give basic information about the nature of the fault. Thus, the proposed GMM-based system sets out to detect and locate the fault on the machine while leaving the diagnosis to the operator.

MATLAB will be used for off-line software prototyping and OpenCV will be used for on-line testing.