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PROFACTOR

PROFACTOR GMBH
Country: Austria
41 Projects, page 1 of 9
  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 831830
    Overall Budget: 825,378 EURFunder Contribution: 722,109 EUR
    Partners: PROFACTOR, ACS

    The SonicScan project aims at developing NDT methods based on ultrasonic testing that are suitable for primary structural parts. The main challenge is the compact shape of the parts and their high thickness. To address this problem the project will build upon the sampling phased array technology that allows the tomographic inspection of parts and combine it with a robotic handling system to move the sensor across the part. Particular emphasis will be put on the model-based, automatic planning of the robot's inspection path to ensure that all elements of the part are inspected. This will be based on methods developed by the project partners for image-based surface inspection robots and they will be adapted to volumetric inspection methods. Data analysis for automatic defect detection, segmentation and classification will be developed, including machine learning methods such as random forests. The main result of the project will be an integrated inspection robot that will be demonstrated on landing gear components. The technologies developed in the project will have impact on the manufacturing of structural composite parts, in particular on their quality control in series production, and on the efficient deployment of ultrasonic inspection robots, by increasing the usability of such robots through automatic adaptation to new part designs. The consortium consists of two partners, covering the required expertise in ultrasonic testing of composite parts and in the automation of robotic inspection tasks. The partners will extend their business in the field of inspection robots by about 2M€ per year based on the developed technologies.

  • Open Access mandate for Publications
    Funder: EC Project Code: 686355
    Overall Budget: 425,950 EURFunder Contribution: 370,638 EUR
    Partners: FISCHER ADVANCED COMPOSITE COMPONENTS AG*FACC AG, PROFACTOR

    The extended use of CFRP parts in aircraft construction (e.g. Airbus A350) requires improved process technologies for the drilling of holes in carbon fibre composites. For the assembly of hull components than 10.000 holes need to be drilled. This project aims at the development of a machine-vision-based inspection tool for the inspection of the inner surface of bores and at the definition of the related quality criteria. The concept is based on a combination of an endoscopic inspection system with photometric stereo. This enables the robust distinction of different characteristic properties in the bores and has a good chance for later automation of the inspection process. Quality criteria will be defined using expert knowledge from previous projects, standards and literature. This knowledge will be extended with experimental data extracted from test parts by using machine learning methods. An evaluation of the inspection tool in an industrial environment will conclude the project. The main result will be a prototype of the inspection tool and a set of quality criteria and rules that are proposed for the quality assessment of the inner surface bores. The developments will be done in such a way that the automation of the inspection process, e.g. by combining it with coordinate measurement systems is possible. This will have impact on the efficiency of drilling methods, specifically of drilling of mixed material stacks such as carbon fibre and titanium. The automatic documentation of surface quality will enable an increase in the efficiency of drilling processes.

  • Open Access mandate for Publications
    Funder: EC Project Code: 735367
    Overall Budget: 3,286,070 EURFunder Contribution: 3,286,070 EUR
    Partners: KIT, FRONIUS INTERNATIONAL GMBH, ELRINGKLINGER AG, OMB SALERI SPA, PROFACTOR

    The INLINE project aims at the solution of key challenges to enable the implementation of a scalable manufacturing process for fuel cell systems. Current manufacturing processes rely on manual work that has substantial limits in terms of cycle times, costs and scalability. Developments will start with the re-design and optimization of two key components: the media supply unit and the tank valve regulator. Both are components that are currently difficult to manufacture and are perceived as bottlenecks in the production process. Based on these new designs, an integrated production line will be planned using simulation tools. These tools will enable the evaluation of different layouts, part flow strategies and for different production scenarios. In terms of manufacturing tools, the end of line test will be improved to reduce cycle times by a factor of 3 and assistance systems for assembly stations will be developed that will enable scalability by reducing the need for training of workers. The overall target is to reduce the cycle time for production of a whole fuel cell system from 15 hours to less than 2.5 hours. Data gathering and analysis methods will be developed to enable the tracking of parts through the production line and - through a correlation of process and quality data - the continuous improvement of the production process. Demonstration of the end of line test and the assistance system will be done in hardware. The whole production line will be evaluated using a simulation tool that has been verified on the current production process. A set of engineering samples of the re-designed tank valve regulator and the media supply unit will be produced and used for tests of the integrated fuel cells and for assessment of the whole production process.A potential of 250 new jobs in manufacturing of fuel cells and for production of the key components will be generated by the project.

  • Open Access mandate for Publications
    Funder: EC Project Code: 645725
    Overall Budget: 346,500 EURFunder Contribution: 346,500 EUR
    Partners: METAL ESTALKI SL, Cranfield University, NANO4ENERGY, HZDR, PROFACTOR, AR

    Increasing the share of renewables in the European energy mix has a key function for the security of energy supply and the reduction of greenhouse gas emissions from fossil fuels. This proposal is entitled “Framework of Innovation for Engineering of New Durable Solar Surfaces”, (acronym FRIENDS2) and aims at achieving a European network for the transfer of knowledge to establish a shared culture of research and innovation which allows turning creative ideas in the field of surface engineering into innovative solutions for concentrating solar power (CSP) applications. FRIENDS2 will be led by one large European industry (Abengoa) who is a world leader in the development of CSP plants. The other FRIENDS2 participants are two well-recognized academic organizations (the University of Cranfield and the Helmholtz-Zentrum Dresden - Rossendorf e.V.), and one SME (Metal Estalki). The purpose of FRIENDS2 is to strengthen the inter-sectoral capabilities in research and development of coating designs in order to improve the performance of CSP key components (reflectors, receivers and containers for heat storage) for high temperature applications. The methodology of this joint research proposal contains aspects of very high novelty. It includes computer modelling, multi-technique coating deposition, use of advanced characterization techniques, and the possibility of scaling-up new coating developments. Special attention is paid to the intersectoral transfer of knowledge and to the establishment of a long-lasting international network with global impact. It is worth noting that a substantial fraction of secondments (51%) will be carried out from the industrial to the academic sector. With the proposed approach, there will be an effective transfer of knowledge among the partners which will pave the road from fundamental research to applied innovation of surface engineering solutions for further CSP development.

  • Open Access mandate for Publications
    Funder: EC Project Code: 270138
    Partners: KCL, FOUNDATION FOR RESEARCH AND TECHNOLOGYHELLAS, PROFACTOR, CVUT, NOVOCAPTIS COGNITIVE SYSTEMS & ROBOTICS, IIT