REVOLUTION focusses on overcoming the challenges hindering the use of recycled materials, but more broadly, restricting the widespread adoption of circular economy principles in the automotive industry.
Currently, PCR (closed-loop recycling) forms ~2% of the total plastics demanded across the automotive sector, and are not used in high-value or high-performing areas. REVOLUTION is proposing a disruptive innovation that will bring open-loop recycling to the forefront of automotive injection moulding. PCR variability causes a big problem for an industry where failure rates on 1 per million are accepted. REVOLUTION will use machine learning and artificial intelligence to optimise the input of recycled materials and injection moulding process to deliver high-quality parts. The AI-Platform will use data from three production processes to predict part quality when using recycled materials. REVOLUTION will develop this platform, and develop a range of recycled formulations, including self-reinforced materials to deliver innovative components that offer light weighing, superior performance and distinctive end of life advantages for future EVs.
REVOLUTION brings together leading organisations from European strong-hold industries such as automotive, chemicals and plastics. The automotive industry is represented by Fiat Chrysler Automobiles through Tofas, CRF and Tier 1s Farplas and MAIER. LyondellBasell, Clariant and Altuglas provide a strong representation of the European excellence in plastics and chemicals. These are joined by leading research organisations and SMEs to bring a market solution that addresses the entire value chain.
REVOLUTION aims to demonstrate automotive components, using polymer solutions that feature optimised recycled materials to increase light-weighting opportunities that extend the range and efficiency of Electric Vehicles and improve end-of-life separation of components to facilitate proper dismantling and the material’s reuse, recovery, and recycling.
Specifically, REVOLUTION will:
- Develop an Artificial Intelligence (AI) Control Algorithm that supports optimised light materials featuring recycled content in the Automotive Industry.
- Design and demonstrate a mono-material B-Pillar using recycled PMMA (rPMMA) with a weight reduction of over 25% compared to a multi-material B-Pillar using virgin PMMA and virgin Acrylonitrile Butadiene Styrene (ABS).
- Design and demonstrate a Rear Bumper using recycled PP (rPP) with matching aesthetical properties to virgin PP (TD10) with a weight reduction of over 20%.
- Design and demonstrate a Rear Back-Seat Panel using Self-Reinforced Polyolefins (SRPO) with a weight reduction of over 50% compared to the steel alternative.
- Design and demonstrate a composite Rear Crash Box using reinforced thermoplastics (PP, PC or PA) with a weight reduction of over 10% compared to the steel alternative.
- Incorporate eco-design approaches from the earliest stages of vehicle development to ensure that all optimisation work supports cost-effective material separation, recycling and recovery and End-of-Life.
On the one hand, participation in the Parameter Monitoring and Data Collection WP. VTT will perform in-line measurements on shear and elongation viscosities on the relevant materials, as well as the determination of tensile strength, breaking strength, maximum elongation and impact strength for each material sample
- Development of process model. IDENER will construct a statistical model to establish the relationship between the material and process inputs and the component output variables. To this end, the application of different supervised learning methods, including traditional artificial neural networks (ANN) and long short-term memory networks (LSTM), will be assessed, according to the produced data and the nature of the problem. The main objective behind the use of both structures is to gain benefit from their respective strengths: on the one hand, the well-known regression capabilities of the traditional ANNs and, on the other, to take into account past events of the process, which potentially impact the current quality outcome, thanks to the LSTM network. To obtain such a statistical model, an experimental dataset, which contains the process inputs and the corresponding outputs, is needed to train the aforementioned model and validate its correct behaviour. VTT, FHG and Farplas will be responsible for generating this experimental dataset. IDENER, with the cooperation of FHG, will define the mathematical formulation of the system elements’ model.
- Development of Control based on the Predictive Model. To keep the quality as high as possible, a control loop will also be designed and implemented. The predictive module developed will be connected to a controller to control actions to be executed in the process.
On the other hand, participation in Environmental and Societal Assessment by leading the Societal Impact Analysis. This task will focus on the social network analysis to map key stakeholders; ripple effect analysis focused on employment and value-added in other parts of the economy, social impact analysis (innovation capacity, wider aspects such as commuting, migration, agglomeration and education)including gender dimension associated with the project.
Lastly, IDENER leads the Exploitation, Communication and Dissemination of the results of the project.
- Client European Commission
- Date 23 January, 2021
- Tags Expertise area, Funding type, Horizon 2020, ICTs, Industrial technologies, Multidisciplinary Design Optimization, Public - H2020, Raw Materials, Research area
- Programme Horizon 2020
- Call ID H2020-LC-GV-2018-2019-2020 / H2020-LC-GV-2020
- Client European Commission
- Partners FARPLAS OTOMOTIV ANONIM SIRKETI, TOFAS TURK OTOMOBIL FABRIKASI ANONIM SIRKETI, MAIER SCOOP, BASELL POLYOLEFINE GMBH, Clariant Plastics & Coatings (Deutschland) GmbH, Altuglas International SAS, HEATHLAND BV, Teknologian tutkimuskeskus VTT Oy, Norner Research AS, FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V., INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM, Iconiq Innovation Ltd, IDENER RESEARCH & DEVELOPMENT, CENTRO RICERCHE FIAT SCPA
- Project cost 4.997912,63 €
- Start date 1st January, 2021
- End date 31st December, 2023