By taking part in this course you will learn about the role of visualization in industrial applications with a specific focus on Industry 4.0. You will learn how to evaluate and develop production systems and processes that integrate visualization technologies, such as digital twins, virtual and augmented reality and are supported by artificial intelligence.

During the course, you will explore a variety of optimization problems related to production systems. You will gain hands-on experience on modern methods for practically simulating and optimizing production systems. You will collaborate in groups towards applying the gained knowledge on your own production cases through state-of-the-art optimization software. At the end of the course, you will be equipped with the theoretical knowledge and practical experience to implement optimization techniques on real-world production systems.

The course covers digitalisation and automation technologies and their application in smart factories. Technologies covered include simulation and deployment, digital twin, connectivity as an enabler for e.g. predictive maintenance, manufacturing execution systems and robotics.

Would you like to know what Industry 4.0 is about? Then this course is for you! In the course, we look at enabling technologies of Industry 4.0 from a human and industrial perspective. The course covers many topics and you will learn the basic terminology related to Industry 4.0 as well as insight and understanding of the Fourth Industrial Revolution and how it is set to affect industry and individuals.

You will acquire knowledge about planning and implementing industrialization activities for achieving a faster time-to-market and time-to-volume with higher quality. During the course you will work on one of your company’s industrialization challenges as a “project case” and analyse ways to tackle them in an efficient way.

With the ongoing digitalization of the industry towards "Industry 4.0" i.e. smart factories with more efficient production, shorter lead times, higher quality, etc. increase the need to measure different quantities and associated processing of this measurement data. This includes the collection of signals via different types of sensors and signal processing of these signals in order to e.g. provide "smart industrial cyber-physical systems" (SICPS) with information for the purpose of monitoring, maintaining, controlling activities, etc. in order to achieve smart factories. The course will focus on sensors, analog-to-digital converters, quality of measured data and signal processing where we, among other things, highlights the choice of sensors and data collection systems and introduces some robust signal processing methods in the light of the digitization of the industry. This course is for professionals who work professionally with programming and development - regardless of industry or sector. You may be in the industry, the IT sector or a larger company in a completely different industry, you may be working in a development department within a company or as an IT consultant or. The course contains the following: Sensors for measuring vibrations, force, elongation, speed and associated signal conditioning Measurement uncertainty Analog-to-digital converter and "Effective Number Of Bits (ENOB)" Folding when sampling signals and anti-folding filters Fourier transform - Discrete Fourier transform Stochastic processes and relevant statistical concepts Power density spectrum, Power spectrum and associated systematic and random errors

The starting point for the course is Lean Production, with an understanding of principles and concepts that can lead to higher efficiency. Then we introduce computer-based application for modelling and simulation of production systems. This provides the opportunity to simulate a production system, perform experiments on it according to different conditions, and optimize the system to increase efficiency. This course is for professionals who work with production systems at different levels with a responsibility for individual production lines, departments, or has a 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: Production systems and concepts such as Industry 4.0 and Smart Industry Lean Production and workshop with the Lean game Programming exercises with event-based simulation of production systems Analysis and optimization of production systems The majority of the course will take place on Campus (Växjö), and at a distance according to the schedule below. Some of the exercises are preferably attend on Campus, but we plan for it to be possible to follow the full course at a distance due to the Covid-situation.

Do you want to deepen your knowledge about maintenance and dependability? Then this course is for you. The course focuses on the value of dependability in production systems and how maintenance contributes to the achievement of optimal dependability. A well-designed maintenance programme starts with the design of the production system, from the procurement of new equipment, to system service life.  

The manufacturing industry collects increasingly large volumes of big data, that is, data at high speed, generated from a wide range of sources in different formats and quality levels. But what is data without insight? This course will help you master the fundamental concepts of big data, cloud computing and smart decision-making for industrial analytics. Designed specifically for manufacturing sector professionals, this Master’s course provides knowledge and insights in handling and processing data, using machine learning and data analytics in the cloud environment. You will learn machine learning-based solutions for industrial applications, such as smart decision-making and predictive maintenance, using state of the art cloud platform tools.

Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The course is part of the education initiative Smarter at Örebro University. This is course requires completion of course Reinforcement Learning part 1.  Read more

Örebro University is offering an introduction course in artificial intelligence. The course will address the basic concepts within classical artificial intelligence (other than machine learning). Traditional artificial intelligence is characterized by the so-called declarative approach to problem solving. The course deals with a selection of different intelligent problem-solving methods, both in theory and practice. After completing the course, the student will be able to model and use appropriate generic solution algorithms to solve problems in an intelligent system. The course is part of the education initiative Smarter at Örebro University. Read more

This course, Digitally-enabled production, is offered in a collaboration between Mälardalens högskola (MDH), Högskolan Väst (HV) and Linnéuniversitetet (LNU). The course aims to support and facilitate our partner manufacturing companies to become competitive in digitally-enabled production. During the course, we address the potential prerequisites and capabilities required for implementing industry 4.0 in the context of an overall production system. More specifically, we increase the competence base of companies in three areas—internal logistics systems, virtual factory, and sensor and signal processing—which can holistically interconnect the key components for the successful implementation of industry 4.0. This course consists of three interconnected course modules at an advanced level and it is offered between September and November 2020: Internal Logistics in Industry 4.0, 2.5 credits, MDH, https://www.mdh.se/en/malardalen-university/education/course-syllabus?id=29594 Virtual Factory and Robot Cell Simulation, 2.5 credits, HV, https://admin.hv.se/samverka-med-oss/kompetensutveckling/teknik/kompetensutveckling-inom-produktionsteknik/virtual-factory-and-robot-cell-simulation/ Data Acquisition and Monitoring, 2.5 credits, LNU, https://kursplan.lnu.se/kursplaner/kursplan-4MT017-1.pdf More information about the course is available on the below link: https://www.mdh.se/en/malardalen-university/education/further-training/smart-production/digitally-enabled-production-7.5-hp Apply to the course in the below link:  https://www.mdh.se/en/malardalen-university/education/further-training/smart-production/digitally-enabled-production-7.5-hp/application-form-digitally-enabeled-production-7.5-hp

This course provides an understanding of the fundamental problems in software testing, as well as solid foundation in the practical methods and tools for a systematic state-of-the-art approach to testing of software.

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 learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

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 emergence of artificial intelligence has created a new opportunity to apply machine learning (ML) in industry 4.0. In this era of the Internet of Things and Big data, processing of a large amount of data would not be possible without ML. Thus, ML made industrial production smarter than ever before.  So, learn the course “Machine learning for Industry 4.0” to bring in new business models in your company and boost productivity using ML.  The course provides knowledge about basics of ML and data, describes ML algorithms and tools and also explains the concept of Industry 4.0 and digitalization in industry 4.0.   The individual processes can be better understood and optimized with the help of the knowledge from the course. Also, it could have an important contribution in analysing the industrial data set, improving results, and making  decision and/or predictions for failure, demand, sales, and production. This course is a collaboration between Högskolan Väst, Linnéuniversitetet and Mälardalens Högskola. It consist of three course modules: Introduction to Machine Learning (MDH – 2 credits, Basic level), Industry Digitalization – Industry 4.0 (HV – 2,5 credits, Advanced level), Applied Machine Learning (LNU – 3 credits, Advanced level). Students who pass all three courses will receive a diploma from Learning for Professionals.