Gaining control over the manufacturing processes is necessary to produce high quality safety components that fulfil as well, the required properties. Process data analysis and Predictive Control Models (PCM) are some useful tools that allow to relate specific real time manufacturing parameters and the corresponding results, ensuring that process variables keep within the optimal operational limits, in order to achieve specific characteristics in the final components.
By applying these technologies to the iron casting process, DigiMAT Project Consortium, led by VEIGALAN and participated by AAPICO, CONTINENTAL TEVES and AZTERLAN Technology Centre, is working on an intelligent solution for producing materials with enhanced properties for the automotive industry. The technology developed up to date by the working team has allowed to optimize the metallic alloy used to produce braking systems with the aim of manufacturing more resistant and lighter components for passenger vehicles.
In the words of Jon Garay, AZTERLAN researcher specialized in foundry technologies, “iron casting is a complex transformation process where a large number of variables are involved. Due to the excellent mechanical properties, the quality-cost ratio and the possibility to obtain complex geometries and near net shape parts, cast iron plays and will keep on playing a determining role in cars. Nevertheless, the current weight-lightening trends and requirements are pushing actual materials to new limits. To achieve this core objective we need to be able to settle more robust and efficient processes”.
In a first phase, the working team has developed an artificial intelligence system that in real time connects manufacturing process data with the results obtained in the testing benches of AAPICO and the advanced characterisation studies performed at AZTERLAN. Thanks to the models developed, acting over specific parameters identified as relevant, “such as, the iron metallurgical quality, the chemical composition and other critical parameters of the process” the team has improved the properties of the material by improving its weight and its resistance.
As explained by the researcher of VEIGALAN Asier González, “we have focused our efforts towards structuring a digital architecture composed by a network of sensors, destructive and non-destructive testing controls, PCM and supervised deep learning algorithms to manufacture metallic components with optimised characteristics”. The technology that is being created within the DigiMAT project can be also applicable to other foundry materials and it has a direct impact on the design possibilities of complex components, promoting weight reduction strategies. Also, thanks to being able to assure that parts meet specific requirements during the manufacturing process, this new development makes it possible to eliminate specific post-production controls and non added value operations.
The project consortium considers that this innovation is potentially applicable to any other metallic material, opening opportunities to a new generation of optimized components, as well as to building more sustainable and efficient processes.
Currently, the project team is working on the implementation of the new models in AAPICO to afterwards, start manufacturing the first prototypes based on them.
DigiMAT project is funded by the EIC Fast Track to Innovation Horizon 2020 program of under the licence No 830903.