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.

This article defines a new concept for real-time factory management—manufacturing maps. Manufacturing maps are generated from two fundamental elements, mini-terms and Petri nets. Mini-terms are sub-times of a technical cycle, the time it takes for any component to perform its task. A mini-term, by definition, is a sub-cycle time and it would only make sense to use the term in connection with production improvement. Previous studies have shown that when the sub-cycle time worsens, this indicates that something unusual is happening, enabling anticipation of line failures. As a result, a mini-term has dual functionality, since, on the one hand, it is a production parameter and, on the other, it is a sensor used for predictive maintenance. This, combined with how easy and cheap it is to extract relevant data from manufacturing lines, has resulted in the mini-term becoming a new paradigm for predictive maintenance, and, indirectly, for production analysis. Applying this parameter using big data for machines and components can enable the complete modeling of a factory using Petri nets. This article presents manufacturing maps as a hierarchical construction of Petri nets in which the lowest level network is a temporary Petri net based on mini-terms, and in which the highest level is a global view of the entire plant. The user of a manufacturing map can select intermediate levels, such as a specific production line, and perform analysis or simulation using real-time data from the mini-term database. As an example, this paper examines the modeling of the 8XY line, a multi-model welding line at the Ford factory in Almussafes (Valencia), where the lower layers are modeled until the mini-term layer is reached. The results, and a discussion of the possible applications of manufacturing maps in industry, are provided at the end of this article.

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.

This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works.

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.

The present study analyses in depth the algorithms of change point detection in time series for the prediction of failures through the monitoring of mini-terms in real time. The mini-term is a new concept in the area of failure prediction that is based on the measurement of the time it takes for a component to perform its task. The simplicity of the technique has made it feasible to build industrial Big Data for the prediction of failures based on this concept. There are currently more than 11,000 sensorized mini-terms at Ford factory in Almussafes (Valencia). For the present study, 10 representative real cases of the different change points that have been detected up to the present were selected and, these cases were analysed by using the change point algorithms, which are representative of the great majority of algorithms described in the literature in their different versions. As a result, their accuracy was measured when detecting the change point and its computational cost. A discussion of the results is shown at the end of the paper.

This paper presents evidence on how the variability of machine parts can affect the throughput of an assembly line. For this purpose, a novel model based on mini-terms and micro-terms has been introduced as a machine subdivision. A mini-term is a cycle time subdivision that can be selected by the user for several reasons: the replacement of a machine part or simply to analyse the machine more adequately. A micro-term is a miniterm subdivision and it can be as small as the user wishes. Therefore, the cycle time of a machine is the sum of mini-terms or the sum of the micro-terms. This paper focuses its attention on a welding line in a Ford Factory located in Almussafes (Valencia) where a welding unit was isolated and tested for some particular pathologies. This unit is divided in three mini-terms: the robot motion, the welding motion and the welding task. The cycle time of each mini-term is measured by changing the deteriorated components for others in the time. The deterioration of a proportional valve, a cylinder, an electrical transformer, the robot speed and the loss of pressure are tested within a range that cannot be detected by alarms and maintenance workers, that is, the range of normal production. The real welding line is modelled and a novel simulation algorithm is created based on mini-terms. The experimental measurements are introduced in the simulation model and the effect of the pathologies in the production rate is computed. As a result, the pathologies with greater variability have a deeper impact in the production rate mainly due to the bowl phenomenon effect. On the contrary, the pathologies with low variability have a low effect in the production rate. In fact, this paper demonstrates that the maximum rate capacity can be achieved if the machine variability is near zero.