Kunskapsförmedlingen 4S – Strategies and Standards for Smart Swedish Industry (part2)
Projektet 4S - Strategies and Standards for Smart Swedish Industry - har som övergripande mål att stärka Sveriges position som ett ledande land inom Smart industri.
2019 – 2020
Projektet 4S - Strategies and Standards for Smart Swedish Industry - har som övergripande mål att stärka Sveriges position som ett ledande land inom Smart industri.
2019 – 2020
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
A digital twin model for improved predictive maintenance decision support, supporting service business models
2019 – 2022
Development of a thermo-chemical test bed and implemenation of results for chosen processes and applications
2019 – 2022
A/HOPE/AI demonstrate novel Mobile Human-Machine Interaction and Services for smart production.
2018 – 2019
To aim is to develop a low-cost vision system for automated quality inspections in assembly lines
2018 – 2020
The aim of the ARR project is to develop the potential of automation in repairs and remanufacturing
2018 – 2020
Method to understand how to automate information handling to get more efficient handling of production deviations.
2018 – 2020
2016 – 2019
By estimating the material conditions and system errors, adaptive fixture and process can minimize variations.
2018 – 2019
The aim is to use compound casting to reduce weight by at least 30 % on a truck wheel hub without deteriorating the performance.
2018 – 2021
This project will in a doctoral project develop models of how the cooling rate affects the fatigue strength so that production can quickly be adapted to new components and steel alloys in order to reduce lead times.
2016 – 2020
The purpose of the COPE-project is to increase the ability of Swedish industries to maintain and strengthen the role as a core plant, with undiminished competitive capabilities.
2016 – 2019
The project aims to increase productivity, robustness, and resource efficiency in production systems using data-driven maintenance planning.
2016 – 2019
The project aims to digitalize established tools for production disturbance handling.
2018 – 2020
2017 – 2020
Digi-load focuses on to enhance the competitiveness in the Swedish surface treatment industry through automation and digitalization
2017 – 2020
2017 – 2020
Demonstrations of a digital infrastructure that supports manufacturing by creating a digital twin of the production system based on existing IT applications for development.
2017 – 2020
makeITrail is concerned with developing new methods and technologies to further integrate the value chain of railway manufacturing in Sweden. It aims to strengthen the supplier-producer-consumer relationship and connect local suppliers internationally. The project focuses on several strength areas in Produktion2030, like flexible production, virtual production, integrated product development, and resource efficient production. These areas link to the agenda of sustainable development 2030; in specific, “industry, innovation, and infrastructure” goal, and “responsible production and consumption”. The consortium consists of: Bombardier Transportation, HiTest, DELTA Development Technology AB, Mälardalen University, and the Research Institutions of Sweden.
2019 – 2019
Methods for 3D scanned digital twins for efficient development and installation of production facilities at SMEs
2018 – 2021
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
Increased digitalization brings new possibilities for Swedish manufacturing companies. This project focuses on what data are needed to feed assembly variation simulation and how this data can be captured and stored efficiently and effectively. The project contributes to increased geometrical quality. The technical value chain from part inspection, to extraction of relevant data, storage of data, usage of data in variation simulation (as a digital twin) and to visualization of simulation results as decision support will be covered. This has the potential to replace prototypes/test series and saves cost, time and reduces the environmental impact.
2019 – 2019
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
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
The transition from conventional to electric power lines creates many challenges, but also opens up new possibilities for component packing and design of load bearing structures as well as new manufacturing methods. The project aims to exploit opportunities with sandwich construction to reduce weight and hence the energy consumption for propulsion; reduce heat losses and hence energy consumption for climate change and to reduce manufacturing costs using new manufacturing concepts, rational assembly and new construction technology.
2017 – 2020
e-FACTORY will enable companies to utilize digital tools as a means to obtain a number of different production values, e.g. increased capacity, improved quality, improved traceability, etc
2018 – 2020
The REWIND project combines the principles of lean production and eco-efficiency in three industry pilots at Stena Recycling, Volvo AB and IKEA GreenTech. The objective is to retain the value embedded in materials with circular strategies (reuse, remanufacture, recycle, repurpose, etc.). Lessons learnt from the pilots will be used to develop educational tools for engineers and industry leaders to accelerate the uptake of best practices for circularity and sustainable production.
2019 – 2022
Study a new QP forming process steel grade that shows significantly improved properties
2018 – 2021
FORD conceptualizes a technical framework enabling reutilization and fast integration of production equipment
2018 – 2019
The project focuses on the combined effects of creep and corrosion on fatigue life, thus contributing to reliable material data and material models for development of exhaust manifolds.
2018 – 2020
To replace the manual inspection, minimize the consequences of defects and to improve the quality of delivered products with an efficient and automated Quality Control system.
2016 – 2019
The goal is to demonstrate the additive manufacturing of micrometer/millimeter wave components.
2019 – 2022
The aim is to develop new models for visualizing and predicting delivery schedule variations in supply chains.
2018 – 2021
Hybrid joints, combination of gluing and mechanical joining are highly demanded where several materials are to be used and assembled. HJT therefore focuses on the smart factory's ability to create flexible production with simulation and programming in a digital twin that combines the latest technology for bonding, assembly and mechanical joining, rheology based simulation and automated collision-free planning. The projects goal is to focus on the whole hybrid joining process and to establish a testbed for hybrid joining as a resource for Swedish Industry.
2017 – 2020
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
Develop alloys of grey iron or compacted graphite iron for cylinder heads with doubled life.
2018 – 2021
Implementation of thin film pre-treatments to replace ZnPh for a more sustainable and cost-efficient production.
2019 – 2022
IDAG aim to identify gaps and propose actions for the digital infrastructure necessary to industrialize additive manufacturing technologies. Actors from a new type of value chain of manufacturing companies – from powder to product – collaborate with digital solution providers and researchers to understand the needs and articulate the necessary actions through analysis of industrial cases. The target is to deliver a description in the form of a roadmap for how these actions can be developed and provided in order to ensure flexible and scalable digital platforms for additive manufacturing value chains.
2019 – 2019
Innofacture är en Företagsforskarskola som bedrivs i samverkan mellan Mälardalens högskola och tio av landets största tillverkningsföretag. Projektet fokuserar både på innovation och produktion för att utveckla produktionsprocesser i företagen.
2012 – 2021
The goal is to improved resource utilisation of advanced machinery through an integrated metrology and analytics approach.
2019 – 2022
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
Robust laser welding of aluminium by using innovative laser welding tools and prediction models to avoid cracking and distortions
2017 – 2020
Demonstrate solutions for visibility in production logistics, for dynamic abilities and resource efficiency.
2019 – 2022
Investigate the preconditions for services on digitalized geometrical metrology. E.g. Mentoring coordination
2018 – 2019
The project will develop design solutions where existing/modified joining techniques are used for joining of new lightweight materials to more traditional materials
2016 – 2019
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
The project will enable joining of new advanced materials by developing a new methodology for weldbonding:
2019 – 2021
To provide understanding of the direct and indirect cost and CO2 effects of packaging principles for industrial packaging in the automotive industry.
2018 – 2021
The aim of the present project is to develop a prediction tool for laminated veneer products (LVPs) to make it possible for the industry to improve product performance by reducing rejects and customer complaints and reducing time from idea to market by means of a tool to simulate LVP performance.
2019 – 2021
The aim with plasma as pretreatment before painting is to fully/partly replace the pretreatment of today with a quality assured, environmental and working friendly process.
2017 – 2019
Develop and validate predictive maintenance algorithms based on AI and ML. The vision is failure free production
2019 – 2022
The main idea of the project is to investigate and demonstrate how the digitalized and proven systems and technologies of the Process Industry, like CPAS, can be used in DM to improve competitiveness and drive growth
2017 – 2019
To improve accuracy and usability for simulation of compression moulding through two PhD-students focusing on long and short fibre composites.
2017 – 2019
The goal is to mirror the production and make custom information available for industry personnel.
2018 – 2019
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
The project's main goal is to develop a design and manufacturing methodology, for resource efficient additive manufacturing of components in the automotive industry.
2017 – 2020
50% increased productivity targeting robust production along the whole value chain for transmission parts
2018 – 2021
The goal is to increase the knowledge in robust design and manufacturing of components with high quality and low environmental impact.
2018 – 2020
Design process from concept to printable stl-file for AM including surface based networks in the structure.
2019 – 2022
The project aims to increase the knowledge about the mechanisms controlling the polishability of coated surfaces to facilitate the development and implementation phase of new and more environmental friendly coating systems.
2016 – 2019
New non-destructive test for sand moulds for iron and steel casting based on new measurement method and data analysis.
2019 – 2021
SeeCut focuses on cost-efficient production systems for secure collection, analysis, visualization, storage and sharing of production data. The project addresses the integration of new signals and sensors. For advanced products, like jet engine components, collecting data during manufacturing and usage is essential for quality assurance and remanufacturing. The project goals are to identify key technical requirements related to production data for use in digitalized value chains, define means to ease the run-time integration of new signals and data streams from manufacturing machinery and sensors, and define how to securely and cost-efficiently share data through the value chains in circular production.
2019 – 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
The purpose of this project is to adopt and to demonstrate a system for automatic inspection of all manufactured components in-line in a production line.
2017 – 2020
Every manufacturing company measure and control production performance with a system of KPIs. The aim of the SMART-PM project is to investigate and demonstrate new ways of collecting data, transforming data to information and introducing new decision tools based on valid information and economic models of the production systems.
2018 – 2020
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
2017 – 2019
Målet är att demonstrera byte från perfluorerade kemikalier till miljövänlig silikonkemi i kombination med nya textila strukturer, för att nå smutsavvisande fordonstextil.
2017 – 2020
2017 – 2021
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
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
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
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.
2017 – 2020
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
The goal is to develop decision support to handle increased flexibility in next generation platforms in the automotive industry.
2018 – 2022
2017 – 2019
The project will develop a demonstrator of a digital human modelling tool considering human diversity, to be used for design, optimisation, visualisation, and verification of vehicle assembly workstations.
2019 – 2022
