Industrial Technologies INNOVATION
Cloud-based Data Platform for integrating Data Driven Models for further adjustment of process parameters and defect pattern detection.
Market Maturity: Tech Ready
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Market Creation Potential
This innovation was assessed by the JRC’s Market Creation Potential indicator framework as addressing the needs of existing markets and existing customers. Learn more
Women-led innovation
A woman had a leadership role in developing this innovation in at least one of the Key Innovator organisations listed below.
Go to Market needs
Needs that, if addressed, can increase the chances this innovation gets to (or closer to) the market incude:
  • Prepare for Market entry
  • Scale-up market opportunities
Location of Key Innovators developing this innovation
Key Innovators
Small or Medium Enterprise
Small or Medium Enterprise
Higher Education Institute / Research Centre
UN Sustainable Development Goals(SDG)
This innovation contributes to the following SDG(s)
Ensure sustainable consumption and production patterns

The UN explains: "Sustainable consumption and production is about promoting resource and energy efficiency, sustainable infrastructure, and providing access to basic services, green and decent jobs and a better quality of life for all. Its implementation helps to achieve overall development plans, reduce future economic, environmental and social costs, strengthen economic competitiveness and reduce poverty.

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

The UN explains: "Investments in infrastructure – transport, irrigation, energy and information and communication technology – are crucial to achieving sustainable development and empowering communities in many countries. It has long been recognized that growth in productivity and incomes, and improvements in health and education outcomes require investment in infrastructure."

The EU-funded Research Project
This innovation was developed under the Horizon 2020 project STREAM-0D with an end date of 31/03/2020
Description of Project STREAM-0D
Facts: i) Zero-defect manufacturing and flexibility of production processes are some of the main challenges for European manufacturing; ii) One of the engineering tools with higher potential is the linking of simulation tools with measurement devices for real-time control of applications. The huge potential of this synergistic loop remains untapped for manufacturing processes and could be used for reducing product variability, increase line flexibility and achieve zero defect production. These objectives could be reached by integrating in the production line multi-physics simulation models, able to predict the product quality indicators in response to the values of critical input parameters (components dimensions, material properties, etc), which are unavoidably subject to variability: different batches, different suppliers,... The models will be fed with actual data from online measurements and, based on the model prediction, the critical steps of the line will be controlled to adjust the product to the exact design specifications or to quickly change specifications for producing customised batches. However, doing this in real time is not possible due to the computational cost of models. Reduced Order Modelling is a new generation of techniques which allow us to obtain parametric solutions of complex models that can be particularized in real time for any value of the parameters. The models run so fast that they can be executed on tablets or smart phones. ROM will be used to transform complex models into the real-time capable models that can be integrated in the production line. Moreover, the online deployment of ROMs and data gathering systems will generate big data which will be exploited through data analysis techniques for further improving the process. The project will show proof-of-concept demonstrations in three real process chains of the automotive sector, covering different types of production methods, products, materials and manufacturing processes.

Innnovation Radar's analysis of this innovation is based on data collected on 19/04/2019.