Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station 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 cyberphysical 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 paper discusses the application of supervised Machine Learning techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the quality control of Human Machine Interface (HMI) consoles, equipped with pressure buttons and LCD displays. Machine learning techniques were applied for the recognition of the operatorxe2x80x99s 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.

Machine learning for assistance systems: patternbased approach to online step recognition Computeraided assistance systems are entering the world of work and production. Such systems utilize augmentedxe2x80x93 and virtualxe2x80x93reality for operator training and live guidance as well as mobile maintenance and support. This is particularly important in the modern production reality of everchanging products and xe2x80x98lot size onexe2x80x99 customization of production.This paper focuses on the application of machine learning approach to extend the functionality of assistance systems. Machine learning provides tools to analyse large amounts of data and extract meaningful information. The goal here is to recognize the movement of an operator which would enable automatic display of instructions relevant to them.We present the challenges facing machine learning applications in humancentered assistance systems and a framework to assess machine learning approaches feasible for this scenario. The approach is assessed on a historical data set and then deployed in a work station for live testing. The posthoc, or historical, analysis yields promising results. The adhoc, or live, analysis is a complex task and the results are affected by multiple factors, most of which are introduced by the human influence.The contribution of this paper is an approach to adapt statexe2x80x93 ofxe2x80x93thexe2x80x93art machine learning to operator movement recognition with a special focus on approaches to spatial time series data preprocessing. Presented experiment results validate the approach and show that it performs well in a realworld scenario.