Robot system 1 deals with industrial robots and their use in industry. The course addresses the structure and properties of robots, as well as principles for use in industry based on different principles.
Based on the requirements placed on a robot system, these can be configured based on different technical points of departure, how they are to be used and methods for creating efficiency. Common equipment is taken up as adaptations to different work processes and applications. Here grippers and sensors come in as well as process equipment of various kinds.
The course covers factors for investment, as well as laboratory work on how security and programming can be handled.
This course is for professionals who work with production systems, automation and robotics at various levels as responsible for individual production lines, departments, or role as production manager or production development. The course will mainly focus on the manufacturing industry in application examples, but principles that will be addressed will be applicable to a number of industries, including consulting companies working towards the manufacturing industry.
The course contains the following:
Industrial robots as a concept; structure and properties; robots in relation to concepts such as Industry 4.0 and Smart Industry
Principles of use; configuration of robot system
Laboratory work with safety and programming of industrial robots
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth.
The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.
The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling
The aim of this course is to give students insight about certification and about what it means to certify/self-assess safety- critical systems with focus on software system and to create a safety case, including a multi-concern perspective when needed and reuse opportunities, when appropriate.
In this course, you will gain insight in components and technologies included in the Industrial Internet of Things (IIoT).
Designed specifically for manufacturing sector professionals, the course provides knowledge about the infrastructure, the technologies and requirements needed to generate, transport and manage data in the Industrial Internet of Things (IIoT) system which is one of the main building blocks towards digitalization and smart factories.
It includes project work, laboratory exercises and assignments where the student gets knowledge of different applications of IIoT in the manufacturing industry.