Associate Professor, PhD
The major effect goal of the SMASh project is to enable digitalization of the Swedish manufacturing industry. Maintenance organizations are expected to have a key role in securing the robustness and efficiency required for full implementation of digital technologies in production. Therefore, the implementation of Smart Maintenance is important for manufacturing companies in order to fulfil their vision of failure-free production. The main idea is to develop a Smart Maintenance Assessment (SMA) tool for benchmarking of maintenance organizations within and across companies. Such a tool will help organizations to implement Smart Maintenance through extended collaboration within the maintenance community. There are similar tools in other areas, such as quality management. However, the few existing tools within the maintenance community have shortages, e.g. the lack of digitalization focus. A consortium including continuous and discrete manufacturers, as well as service providers and major academic partners, will collaborate on developing the tool and validate it using advanced statistical methods on large industrial data sets.
Associate Professor, PhD
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.
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.
Målet var att förstå de utmaningar som den svenska och japanska industrin står inför studiebesök.
Method to understand how to automate information handling to get more efficient handling of production deviations.
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.
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 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.
The project will develop a concept for production workers to easily build simple low-cost IoT-aided improvement solutions at the production shop floor.
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.
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.
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
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.
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 digitalize established tools for production disturbance handling.
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
To create an inventory of AI techniques for maintenance services, apply AI techniques to three industrial cases, and evaluate their economic and environmental implications.