Digital Twins and AI in Smart Motion Control Applications

Abstract

Recently, smart system integration was identified as a key competence for optimizing machines and robots. However, when one wants to ’tune’ the entire production process a step further is necessary. We should evaluate performance indicators (e.g. energy and material consumption) over the whole machine life cycle in order to align the production with circular economy principles. To reach that target MBSE (model-based system engineering) should be covered by advanced digital twin approaches which allow continuous monitoring of machine performance, predict the failures and maintenance. Moreover, artificial intelligence and machine learning must be used to process big data sets gathered from the production lines. This paper identifies a common set of technologies and building blocks suitable to solve above mentioned problems for a large variety of industrial domains (semiconductor production, health-care robotics, CNC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> machining, high-speed packaging and others). It presents the first results of the large-scale IMOCO4.E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> project and shows the pathways for application of the technology on specific machines (so-called pilots). The authors believe the ideas presented could be inspiring also in other domains.