Associate Professor, PhD
Production disturbances cause substantial costs and efficiency losses in automated production systems (studies estimate 106 billion SEK every year in Swedish manufacturing industry). Therefore, the D3H project aims to digitalize established disturbance handling tools (DHT) by making them data-driven and, thus, more effective. Desired effects include: reduced disturbance frequency, increased Overall Equipment Effectiveness (OEE), and better opportunities for cost-effective automation. The digitalized DHT will be implemented at six case companies and the effects on different disturbance patterns will be measured. The project will also develop improvement services based on the DHT for internal use at manufacturing companies or to be provided on a consultancy basis. The project results will be disseminated outside the consortium by means of technology workshops and development of digitalized learning modules, inspired by Massive Open Online Courses (MOOCs). These modules are designed for use in university courses, professional education, etc., and meant to be spread by learning platforms, such as Civilingenjör 4.0. Finally, to secure the right competences, the consortium includes major Swedish research institutes and universities together with manufacturing companies, Small and Medium Sized Enterprises (SMEs), and a software innovator company. A multi-disciplinary team combining manufacturing maintenance experience and computer science expertize.
Associate Professor, PhD
To create an inventory of AI techniques for maintenance services, apply AI techniques to three industrial cases, and evaluate their economic and environmental implications.
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.
DiLAM strengthens the competitiveness of the Swedish manufacturing industry by aligning the digital and physical supply chains for additive manufacturing of large parts.
The project aims to reduce the lead time for sheet metal die tryout by optimizing the value stream and develop methods for numerical compensation of die and press deflections.
Method to understand how to automate information handling to get more efficient handling of production deviations.
The overall goal of DiSAM is to create a unique test AM Hub in Sweden for metal and polymer based additive manufacturing processes.
The project aims a digitising the temperatures during the casting of rolls and suggest actions to the casting manager to reduce the variability of the process
SCARCE will investigate the needs, possibilities and obstacles in value chains up- and down-stream from a focal SME company. SCARCE will explore what data to measure and visualize, and how this data can enable more automated execution, as well as, more dynamic and proactive planning of production capacity and material flows across the companies in the value chain. In addition, we will study organizational capabilities, especially the future human role, for implementing and managing in a digital and data-driven value chain.
The paintshop is often a bottleneck in production and the processes are fine-tuned based on testing on numerous prototypes. To meet the future demands there is a great need to improve the product preparation process. The aim is to develop methods, techniques and software, and supporting measurement methodology, for simulation of paint curing in IR and convective ovens. The goal is to assist the industry to further develop and optimize their surface treatment to be more energy and cost efficient; to have a shorter lead time in product development; and to give a higher product quality.
Maintenance in existing plants is becoming increasingly important, where predictive maintenance has become an emerging technology. The use of decision support tools contributes to environmentally and economically sustainable production. Within this project, different types of digital twins have been designed and evaluated. Specifically, new predictive model types have been tested in two different industrial case studies; a heat exchanger at SSAB and a profiled header at Svenska Fönster AB.
Recent research from Chalmers have shown that by slightly tuning robot motions, the energy use can be reduced by 10 –30%, with preserved cycle time.
Målet var att förstå de utmaningar som den svenska och japanska industrin står inför studiebesök.
Improve the efficiency of sawmills, including improved monitoring and maintenance of the production line. This by sharing data via digital twin between the actors in the maintenance chain.
The SAPPA project is about innovative cloud-based predictive and preventive maintenance systems, improving availability of products and production systems.
This project aims to contribute to the development of future ERP-systems. The project will explore how to offer work, redefine work roles and challenge companies to make use of advanced systems support and the technology within and around these. Overall, the project aims to contribute to the development of both the next generation of ERP-systems and a complementary change in the way firms see upon work organization, so that technology can support and meet the needs of the humans within organisations rather than enforcing structures upon them.
The objective is to bring together expertise from AI and LCE to Product/Service Systems for Swedish manufacturing firms in a multidisciplinary research effort to utilise latest techniques efficiently for Swedish production industry. The goal is to make a plan of developing demonstrators in production and maintenance using artificial intelligence techniques, digital technologies and lifecycle engineering methods
The project will develop a concept for production workers to easily build simple low-cost IoT-aided improvement solutions at the production shop floor.
The project aims at radically improving the working environment and the employee security within the heavy manufacturing industries by using and adapting the latest technology for low and ultraprecise positioning and decision support systems. The target is to increase security and safety by adapting the decision-support and positioning system for the heavy manufacturing industries.