Development of automated robotic vision inspection system for accurate quality control

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Quality control (QC) is an indispensable part of almost every manufacturing industry, especially those related to higher safety standards such as the automotive industry. Despite recent advances in automotive quality control, most of the QC processes were performed on randomly selected manufactured goods and outside of the manufacturer's product line. Spot-checking QC does not fully guarantee well-being and safety of the products, when meeting the stringent standards is essential. Off-site QC requires products measurement recording for later reference and reduces assembly line throughput. Most of the QC systems are still reliant on human operators to perform some parts of the inspection task. To address these shortcomings, this proposal aims to replace human operators with robots to perform inspection tasks automatically in the factory environment. The main goal of this project is to carry out: (i) dimensional measurement and (ii) flaw detection in bore cylinders. for the first objective, several features of the bore such as diameter, taper, and out-of-round will be inspected to verify if these parameters are within the tolerance set defined by the manufacturer. The second goal of this system will be to detect any flaws such as cracks, cavities or pinholes. Flaw detection is particularly challenging because defects may happen in any shapes or any sizes on the inside cylinder walls, and they are unnoticeable to human eye inspection. To the best to our knowledge, this is the first attempt to perform flaw detection automatically on-site. To employ robots in dynamic and uncontrolled workplaces, robots should cope with several unforeseen circumstances. Robot learning is a new paradigm which enables robots to handle variations in the working environment, such as differences in the item position on the product line or illumination variation. We will use learning from demonstration (LfD) technique and other cutting-edge technologies, such as computer vision, artificial intelligence, and pattern recognition. The outcome of this project will have considerable impacts on automotive sector as well as other critical industries.