Escuela Superior de Enseñanzas Técnicas

This document aims to make a retrospective of our work in the Ford research group in collaboration with researchers from the CEU Cardenal Herrera University and the University of Valencia. The research group originated from the doctoral thesis by Eduardo García Magraner and his thesis was directed by Nicolás Montés in 2016. The Mini-terms were formulated for the first time in this thesis. From then on, the research group grew as the mini-terms began to consolidate both industrially and scientifically. At industrial level we were provided with a CDTI (Centre for the Development of Industrial Technology) which made it possible to massify the mini-terms at Ford factory in Valencia and at scientific level we attended different congresses. Especially relevant was ICINCO 2018 since the concept of the mini-terms could be presented to the programme chair of the congress, Oleg Gusikhin, (Global Data Insight & Analytics, Ford Motor Company, United States). His support led to the consolidation of the mini-terms through their standardization within Ford and also the consolidation of the group through the inclusion of the CEU Cardenal Herrera University in the URP (University Research Program). The success of Eduardo García’s doctoral thesis motivated the Foundation for Development and Innovation (FDI) to decide to fund doctoral theses within Ford, financing a thesis in collaboration with the University of Valencia and another one with the CEU Cardenal Herrera University. Moreover, Eduardo García’s thesis motivated the staff of the plant to take the step to carry out doctoral theses, funded by the INNODOCTO programme of the Generalitat Valenciana. Throughout this journey different awards have been won such as the Henry Ford Technology Awards in 2019, the Factories of the Future Awards in 2021, the Global Manufacturing Technical Excellence Award in 2023 and the Angel Herrera Award for the best research work in 2024. Twenty-four communications have been made to congresses, ICINCO being the congress with the highest number of communications. In particular, at ICINCO 2020, one of these articles was selected as the Best Industrial Paper Award. Thirteen articles have been published in indexed journals with an impact index and also three book chapters. This document aims at reviewing the different tools and concepts developed and introduced by the research group as well as trying to define its objective.

The world’s large factories in all sectors consume a great deal of resources, either raw materials or energy, to develop their products. Saving resources can have a positive impact on the sustainable development of the planet. Automotive manufacturers are a clear example of how to save by investing resources in improving technologies and optimizing processes. This article focuses on one of the most common processes in the automotive sector: the stamping process. For the optimization of this process, previous simulations are usually carried out in order to define the optimal parameters and which should only be applied for a correct operation. The real circumstances of the plant show there is a large discrepancy between the parameters obtained by simulation and the real process because of the difference in material properties, lubrication, press operation, etc. The solution is that the operators must adjust the parameters a posteriori and the only criterion to follow is obtaining the right quality of the part. In many cases, the parameters are well above the ideal. This article presents some algorithms used in order to perform an in situ calibration of the stamping presses to find the press parameters that, guaranteeing the quality of the part, allow to adjust the energy consumption to the minimum. At the end of this article the experimental results from this in-situ calibration process and the energy savings are shown.

In recent days there are many possibilities in develop solutions for industrial manufacturing process thanks to the emerging technology based in Industry 4.0, where one can measure and manage data from an industrial process in real time been able to know more information than ever before from the process. But still having challenges in complex process where monitoring data and give a solution is less intuitive, mostly due to a complex physical definition of the process and manufacturing car body parts in automotive is a clear example. In deep drawing process is common to have variations in the process parameters and they can carry out bad manufactured parts. The cycle time, the robust process and the complex physics in the process are the main problems to obtain feasible information from the process. In the following it is proposed a new methodology to have full knowledge of the process applying the so-called method Hybrid Twin.

It is currently going through an industrial period in which connectivity, data collection of the process and its understanding to optimize it is becoming more and more common. The automotive industry is no exception as we are on the way towards connected factories where the digitization of the stamping process is a trend followed by manufacturers. A common problem often encountered is the high cost required to develop solutions by using this technology. Obtaining parameters of the manufacturing process is a challenge on many occasions. New solutions have been proposed from an opposite point of view, i.e., we evaluate what information can be extracted from the equipment and from the data obtained we can bring forward the possible tools to be developed without the need for extra investment. This article shows the verification of an experimental process, previously developed, with which we intend to find out the status of the press during the drawing process for each cycle that is carried out during production and also the status of the equipment at all times, up to the point of detecting if there is any problem both in the die and in the mechanical components of the press and verifying it with the developed tool, showing that we can know the status of the equipment by monitoring the data in real time.