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: 2020 – 2022
SCARCE II will develop a demonstrator to show how SMEs and associated value flows can increase efficiency, competitiveness, sustainability and internal collaboration through digitalisation. The goal is to show the value of a new digital solution. SCARCE focuses on two subcontractors in the value chain linked to Scania and Volvo. The demonstrator is a cloud-based solution that connects three test beds in the industry; Stena Industry Innovation Lab, Chalmers, RISE IVF lab, Mölndal and KTH's test bed in Södertälje with the help of Siemens, AFRY, Qbim, Virtual Manufacturing and EQPack.
Today’s digital solutions for IoT platforms, automation solutions and transparent systems for displaying real-time data enable better planning and control in the value chain for SMEs. Nevertheless, it is possible to get digital solutions that can buy and if they are to be the main one for monitoring the production processor, where the potential of digitization that is not exploited. SCARCE II focuses on two subcontractors located in the value chain linked to Scania and Volvo. SCARCE I identified the following challenges: i) categorizing disruptions in production to provide what data is needed to better plan productions ii) adapting digital flows to introduce automation in material handling and iii) tracking packaging to increase efficiency and left linked to sustainability and ergonomics. In SCARCE II, a demonstrator will meet the challenges and show SMEs and their associated value streams that it is possible to increase efficiency, competitiveness and international collaboration through digitalization. The demonstrator will be a cloud-based solution that connects three test beds via pilots in the industry; Stena Industry Innovation lab, at Chalmers, RISE IVF lab in Mölndal and KTH’s testbed Södertälje are linked with the help of technology suppliers Siemens, ÅF, Qbim and Virtual Manufacturing. The pilots that are being developed at the subcontractors are P1 Digitized information in planning and material handling and P2 Emballage management. Linked to the project is a focus group with members from a haulage company and partners from the value chain such as Scania and Stena Metall, as well as participation from the Digitala Stambanan which will provide input to the development and results of digital solutions.
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
2017 – 2019
Developing circular production systems using digital technologies
2021 – 2024
Reduce the environmental impact of foundries by reducing the amount of sand waste using machine learning.
2023 – 2024
Digi-load focuses on to enhance the competitiveness in the Swedish surface treatment industry through automation and digitalization
2017 – 2020
DIDAM develops and demonstrates digitalization solutions to industrialize Additive Manufacturing
2020 – 2023
DiVISI aims at investigating how sharing of digital information can improve the forest-forest industry value chain.
2020 – 2023
A research collaboration between Luleå University of Technology and the company RGS 90 will provide new treatment methods for three common but problematic types of waste.
2015 – 2019
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.
2019 – 2019
MIDWEST will develop mechanisation solutions for Post-weld treatment methods of welded components.
2020 – 2023
DiLAM strengthens the competitiveness of the Swedish manufacturing industry by aligning the digital and physical supply chains for additive manufacturing of large parts.
2017 – 2020
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
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.
2019 – 2019
Novel methods, techniques and software for simulation of electrodeposition and galvanization processes.
2020 – 2023
The project aims to use deviation data for improving the product design and production.
2020 – 2023
The project's goal is to assist industry enabling sustainable work for operators during assembly of wire harnesses.
2022 – 2025
Målet var att förstå de utmaningar som den svenska och japanska industrin står inför studiebesök.
2017 – 2018
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.
2016 – 2019
Methods for 3D scanned digital twins for efficient development and installation of production facilities at SMEs
2018 – 2021
Roundtables with governmental organizations and industry regarding circular economy and product related legislation
2020 – 2021
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 aim of the ARR project is to develop the potential of automation in repairs and remanufacturing
2018 – 2021
Knowledge is needed that can support design and control of automation in material handling systems.
2019 – 2022
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.
2019 – 2019
In material handling processes, such as kitting and sequencing, which are used in the automotive industry to supply the assembly with a wide and growing range of component variants, the picking information system is central design aspect. Given the developments in digitization, the purpose of this concept study is to evaluate the potential of digital technology to support materials handling work in production systems.
2017 – 2017
To lay the foundation for tomorrow’s network of circular economy microfactories producing products designed by Swedish industry and produced from local recycled plastics.
2022 – 2023
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.
2017 – 2020
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.
2017 – 2018
Two major disruptive trends – electrification and digitalization are changing customer preferences, leading to the probably most substantial transformation in the automotive industry we observed in decades. Finding a balance between customer’s requirements towards “zero-emission vehicle,” “connected car,” choice of materials, clarity of functions, and interface modes under the pressure of production time and cost are not easy. The AttributDo-project aims to help engineers create, define, verify and validate new and existing design features for new product development.
2021 – 2021
The project will develop a concept for production workers to easily build simple low-cost IoT-aided improvement solutions at the production shop floor.
2018 – 2020
WELDVISI will create a new sensor-based assistance system for manual manufacturing with real-time cognitive feedback and documentation of parameters.
2022 – 2025
The overall goal of DiSAM is to create a unique test AM Hub in Sweden for metal and polymer based additive manufacturing processes.
2017 – 2021
Measure environmental performance and improve towards eco-efficient, climate-friendly and circular production
2022 – 2023
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
Method to understand how to automate information handling to get more efficient handling of production deviations.
2018 – 2020
The project aims to digitalize established tools for production disturbance handling.
2018 – 2020
SE:Kond2Life - Ecosystem for reuse of automotive components
2019 – 2022
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
2015 – 2016
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.
2017 – 2019
Tidsättning av manuell montering är centralt för verkstadsindustrins konkurrenskraft.
2021 – 2024
