Kunskapsförmedlingen SMASh – Smart Maintenance Assessment
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
2017 – 2019
Project time: 2023 – 2026
Budget: 10 500 000kr
Realizing the future of maintenance in battery production for a sustainable and competitive battery industry.
The vision of the MATTER@SCALE project is to ensure the long-term sustainability of the Swedish battery industry, and the purpose is to put maintenance organizations in the driver’s seat toward realizing the circularity of battery production. Through excellent academic research and pioneering industry networking, the project aims to develop and scale four key enablers that allow maintenance organizations to meet the ambitious targets set for the Swedish battery industry: (1) human competencies, (2) organizational design, (3) equipment supplier relations, and (4) data analytics. The project will also establish a completely new industry network, spread the results through dissemination, education, and life-long learning, as well as leverage the insights to impact policy and regulations. The goal is that the enablers are fully ready and implementable by the time battery factories reach full production pace.
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
2017 – 2019
The project aims to digitalize established tools for production disturbance handling.
2018 – 2020
To create an inventory of AI techniques for maintenance services, apply AI techniques to three industrial cases, and evaluate their economic and environmental implications.
2017 – 2019
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.
2017 – 2020
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.
2017 – 2018
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
2019 – 2019
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.
2017 – 2020
En hållbar batterisektor i Sverige genom effektivt underhåll av framtidens batteriproduktion.
2022 – 2023
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.
2019 – 2019