Finished research projects: Virtual Production Development

2021-2024 For companies to stay competitive, resilient and sustainable in an ever-changing world, extreme flexibility in the production processes is required to be adaptive and agile.

2018-2020 Since inspecting the quality of die-cast and hot-pressed metal components is mostly done manually, increased automation in this step would substantially increase the competitiveness of die-casting foundries and hot-pressed metal component manufacturers. The project therefore intends to develop, test, and evaluate vision systems for automating the quality inspection of die-cast and hot-pressed metal components.

2012-2018 Even successful industrial production often has areas for improvement—and bottlenecks that cause delays. But which improvement actually has the greatest impact? In his research, Jacob Bernedixen aims to automate and optimise a model that identifies which improvement will benefit the company the most.

2016-2018 The project is within the area of Industry 4.0 closely related to the application and technical solutions proposed by Efficient Equipment Engineering E3.

2020-2022 This project wants to test the use of Augmented Reality (AR) to improve the medical simulation by increasing the ability to empathize and provide a better environment for training and learning for the entire trauma team at Skaraborg Hospital in Skövde (SkaS).

2021-2024 Predictive maintenance is one of the major thrust areas for many global manufacturing companies. Artificial intelligence, big data analytics and industrial internet of things (IoT) have already shown great potential in the area of maintenance. However, as more companies adopt these technologies, several key challenges have emerged hindering the progress towards complete digitalization of maintenance operations.

2014-2017 Designing or improving a manufacturing system involves a series of complex decisions over time to satisfy the strategic objectives of the company. To select the optimal parameters of the system entities so as to achieve the desired overall performance of the system is a very complex task that has been proven to be difficult for a decision maker in the design/improvement process.

2020-2022 The long-term vision of this research is to support knowledge-driven decision making in Swedish industry, which in turn can contribute to leverage their overall productivity and competitiveness.

2017-2020 Future manufacturing will be characterized by the complementarity between humans and automation, especially regarding the production of highly customizable products.

2015-2017 The project ”Measuring and Managing Continuous improvements in Medium-sized Manufacturing Companies” (MMC²) is a collaboration between three schools at the University, The School of Engineering Science (Virtual Systems Research Center), The School of Informatics (Informatics Research Center) and The School of Business, and four medium-sized manufacturing companies in the west of Sweden.

2018-2021 Project vision is an open modular framework for efficient and interactive simulation and analysis of realistic human motions for professional applications. Within the European economy, digital modelling activities and the simulation of human motion in particular, have emerged during the last decades in various domains ranging from automotive and truck over healthcare, construction and pedestrian simulation to the gaming industry.

2008-2011 The overall aim of this proposed multi-discipline (industrial engineering and computer science) project is to leverage the effectiveness of the Swedish industrial and service sectors, by introducing the state-of-the-art simulation technology, namely simulation-based intelligent optimisation, to their system design and daily operations.

2018-2020

2018-2020 The goal of the project is to create the required conditions for continuous innovation and developments connected to electric powertrains and efficient value chains to facilitate the production of the new generation of electric powertrains to take place in Sweden, and not through, e.g., outsourcing. The project is up and running, and the steering committee meets once a month.

2019-2022 To remain competitive on the global market, industry is continuously improving economic, social and environmental performance. The REWIND project seeks to take advantage of current industrial trends in digitalisation and sustainability. Capitalising on the expertise of the academic and industrial partners, the consortium will make use of digital technologies, assessment methods, strategic planning tools and materials research to realise opportunities towards a circular economy.

2016-2018 SIM2020 is a three-year projects aimed at building a demonstrator environment at the university. A demonstrator is a physical or virtual prototype that demonstrates the practical value of the research. The demonstrators are built for an industrial audience with the purpose to get more industrial companies interested in using the university's research. The project has a particular focus on small and medium-sized manufacturing companies.

2018-2019 The goal of this simulation study is to show what effects different closer care interventions can have on the number of emergency room visits and inpatient care days for the sickest elderly and frail elderly groups.

2011-2014 With today’s global and rapidly changing markets, companies face the challenge of constantly improving their logistics in order to stay competitive. The project Smart Optimization of Logistics (SOL) aims to support this activity by providing a way of performing smart optimizations resulting in reduced costs, improved effectiveness, and enhanced capabilities.

2016-2018 One of the most important aims of any manufacturing value chain is to align and combine many objectives, i.e. strategic, tactical and operational, in various stages, i.e. production, plant or supply chain level, so that they work towards a common goal – increasing the efficiency and profitability of the overall manufacturing value chain.

2017-2020 This project proposes the concept of multi-level and multi-objective optimization in order to both horizontally and vertically align the optimization objectives of various manufacturing levels. For example production and plant wide level, focusing on strategic and tactical manufacturing objectives.

2015-2019 The research project aims at developing the solution for a new working environment in which the collaboration between human worker and robots can take place and bring significant benefits to robot-reluctant industries.

2024 The project will focus on strengthening the implementation of the transformation of health care. The aim is to use quantitative calculations, alternative scenarios and simulations to describe how skill needs, supply and delivery of healthcare in the Västra Götaland region (VGR) may change up to 2040, both as a result of external environmental factors such as demographic pressure, medical development and the internal improvement work.

2018-2020 Production is becoming increasingly complex and the industry has a great need for development as a result of the demands on digitalisation, sustainability and competence increasing. At the same time, there is research results that contribute to faster development, but far too few with the skills to drive the implementation. The purpose of the competence development project Virtual Factory has been to minimize this gap.

2020-2023 With all the data acquired during a day of production, the possibilities for improvement are endless. All you need to do is find a way of interpreting the data. TOPAZ aims to investigate how a combination of data mining, machine learning and optimization can aid manufacturing companies. This will be done by transforming heterogeneous data derived from multiple sources, and potentially from different part of the value chain, into prescriptive actions that support long-term goals of profitability, sustainability and stability.

2017-2020 With Industry 4.0 comes a technological evolution towards cyber-physical systems and a paradigm shift from centralized to decentralized production. This paradigm shift will change production. Our goal within the TWIN project is to develop a holistic solution for integrating a simulated twin of an automation system that covers the system’s entire life cycle. Such a solution will help companies stay competitive in a global and rapidly changing market and meet the challenges accompanying the fourth industrial revolution, such as mass-customization and short product life cycles.

2018-2020 Vision systems for quality inspections in production processes have existed for a long time, but are currently used only to a limited extent and then primarily within manual assembly lines. Within the VISION project, we aim to develop a low-cost, high-performing vision system for automated quality inspections in assembly lines based on cheap hardware and state-of-the-art machine learning algorithms for real-time image processing.

2014-2017 You2 is a three-year research project that focuses on the development of IT tools for future industrial operators. The central research questions of the project concern the collection of large amounts of data, intelligent processing of the data, automatic optimizations in real-time and user interface design for new technologies such as smart glasses. The work in the project is conducted in close collaboration with industrial partners and is characterized by prototype development aiming at the higher TRL levels.