TEACHING INDUSTRY 4.0

Industry 4.0 is a term that was introduced by the German government at the time of the Hannover Fair in 2011 in relation to an initiative brought forward to support German industry in addressing future challenges. It refers to the 4th industrial revolution, in which disruptive digital technologies, such as the Internet of Things (IoT), robotics, virtual reality (VR), and artificial intelligence (AI), are exercising a notable impact on industrial production.Industry 4.0 takes the emphasis on digital technology of recent decades to a whole new level with the help of interconnectivity through the Internet of Things (IoT), real-time data access, and the introduction of cyber-physical systems.This paper focuses on the design of an educational module for higher education mechatronics students. Introducing Industry 4.0 into a mechatronics curriculum will reinforce the integration of student competences in flexible and rapid manufacturing. The module includes notions of machine learning and deep machine learning, which are essential in robotics and behavioral robotics and closely interact with control theory. The results of a pilot training activity in the field are also illustrated and discussed.

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

Overview of technologies for building robots in the classroom

This paper aims to give an overview of technologies that can be used to implement robotics within an educational context. We discuss complete robotics systems as well as projects that implement only certain elements of a robotics system, such as electronics, hardware, or software. We believe that Maker Movement and DIY trends offers many new opportunities for teaching and feel that they will become much more prominent in the future. Products and projects discussed in this paper are: Mindstorms, Vex, Arduino, Dwengo, Raspberry Pi, MakeBlock, OpenBeam, BitBeam, Scratch, Blockly and ArduBlock