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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.

Our society is immersed in the Fourth Industrial Revolution due to the fast evolution of the new technologies that are modifying the labor market. In the near future, technologies related to Industry 4.0 will produce totally new goods and services. Therefore, the educational systems should adapt their programs to the future needs of an uncertain labor market. In particular, mathematics will play a key role in future jobs and there is a strong need to connect its teaching methodologies to the new technological scene. This work uses the STEAM approach (science, technology, engineering, arts and mathematics) along with active methodologies and educational robotics with the aim of developing a new strategy for the application of mathematics and physics in an engineering degree. In particular, a transportation challenge is posed to tackle the teaching–learning process of the Bézier curves and their applications in physics. A pilot project is developed using a LEGO EV3 robot and an active methodology, where students become the center of the learning process. The experimental results of the pilot study indicate an increase in the motivation due to the use of robots and the realistic context of the challenge.

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 objective of our research is the implementation of STEAM (Science Technology Engineering Art Mathematics) learning in the bachelor of Engineering in industrial design and product development at CEU Cardenal Herrera University through the EXPLORIA project. This article implements and develops the proposal for the first year of this bachelor, which includes 24 students aged 18–20. This article focuses on how to integrate STEAM learning within the EXPLORIA project for the improvement in the learning of the physics subject, and in particular, regarding the part of the syllabus related to mechanical engineering through different projects, challenges and milestones that allow the student to see the use in the design and development of products. The EXPLORIA project connects the competencies of the different STEAM subjects included in the bachelor, designing a learning process as a logical, sequential and incremental itinerary. Through concepts on which the fundamentals of design are based: shape, volume, color, space and structure. In particular, this article shows the adaptation made in the physical part to be able to teach the integrated mechanics part in this learning process. The complete learning was carried out through several challenges and two milestones the students had to overcome through the application of the physical knowledge learned in class. To validate the effectiveness of the proposed methodology, at the end of the paper, an ad hoc questionnaire is carried out showing the students’ assessment regarding the new teaching methodology.

Over the last years, mobile robot platforms are having a key role in education worldwide. Among others, LEGO Robots and MATLAB/Simulink are being used mainly in universities to improve the teaching experience. Most LEGO systems used in the literature are based on NXT, as the EV3 version is relatively recent. In contrast to the previous versions, the EV3 allows the development of real-time applications for teaching a wide variety of subjects as well as conducting research experiments. The goal of the research presented in this paper was to develop and validate a novel real-time educational platform based on the MATLAB/Simulink package and the LEGO EV3 brick for academic use in the fields of robotics and computer science. The proposed framework is tested here in different university teaching situations and several case studies are presented in the form of interactive projects developed by students. Without loss of generality, the platform is used for testing different robot path planning algorithms. Classical algorithms like rapidly-exploring random trees or artificial potential fields, developed by robotics researchers, are tested by bachelor students, since the code is freely available on the Internet. Furthermore, recent path planning algorithms developed by the authors are also tested in the platform with the aim of detecting the limits of its applicability. The restrictions and advantages of the proposed platform are discussed in order to enlighten future educational applications.

The COVID-19 pandemic and its social implications generate adverse psychological effects that affect success in education where educational methodologies are not ready to overcome the problem. This article presents the design and evaluation of a gamification activity, the Escape Room, applied to the subjects in Pharmacy and Nursing. The objective of using the Escape Room is to reduce the impact that COVID-19 has on students’ academic performance due to the psychological effects and the change in educational modality with which the vast majority of activities are carried out online. The Escape Room presented in this article is based on the search for a scientist who is fleeing with the COVID-19 vaccine around the world and the students have to find it by passing tests and missions related to mathematics. Due to the COVID measures imposed by the university, where double presentiality was imposed, the proposed Escape Room has the peculiarity of being designed in dual format, that is, allowing students to connect in face-to-face mode, online, or a mixture of both, depending on the maximum capacity of the classroom, the number of positive cases in the group, quarantines, etc. As a result, a great impact is shown on the initial perception of students towards mathematics and a null impact of the COVID-19 effect on the academic performance of students.