Machine learning applied to an intelligent and adaptive robotic inspection station - Variz, Luís Sousa Pinto (2019)
Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decentralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This thesis discusses the application of supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the Quality Control (QC) of Human Machine Interface (HMI) consoles, equipped with pressure buttons and Liquid Crystal Display (LCD) displays. Machine learning techniques were applied for the recognition of the operator’s face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.