1. Project idea and potential: The major objective of the project is to select and apply proven and promising AI (Artificial Intelligence) techniques to maintenance services provided by three Swedish manufacturers. The project also aims to evaluate the economic and environmental implications of implementing the AI techniques. A huge market potential exists for the AI techniques to be applied to the data and to innovate maintenance services.
2. Brief background and state of the art: Today, many Swedish manufacturers who provide maintenance as part of their product/service system (PSS) have access to a huge amount of data. Despite proven AI techniques, industrial application of these techniques to the data is still in its infancy.
3. Impact: Through disseminating knowledge gained from the project to Swedish industry, their capacity and competitiveness as well as business and environmental performance by e.g. prolonged product life time will be enhanced.
4. Implementation: The major work packages are: making an inventory for AI techniques, applying the identified AI techniques to three industrial cases provided by the industry partners, evaluating the impacts of the application of the AI techniques using Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), and disseminating the results to industry.
5. Project participants, roles, and management: IEI is the project coordinator and will provide expertise about PSS, LCA and LCC. IDA will contribute through its expertise on AI. Attentec, Saab, and Toyota will apply AI techniques to create innovative services or improve existing ones. IEI is responsible for the project management, where each partner is also a member.
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.
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
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.
Develop and validate predictive maintenance algorithms based on AI and ML. The vision is failure free production
The project aims to digitalize established tools for production disturbance handling.
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
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.
The project objective is to lay a foundation to develop a digital platform that can enable generating materials passports for products to facilitate implementation of Circular Production Systems.
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.
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.