The incursion of disruptive technologies, such as the Internet of Things, information technologies, cloud computing, digitalization and artificial intelligence, into current production processes has led to a new global industrial revolution called Industry 4.0 or Manufacturing 4.0. This new revolution proposes digitization from one end of the value chain to the other by integrating physical assets into systems and networks linked to a series of technologies to create value. Industry 4.0 has far-reaching implications for production systems and engineering education, especially in the training of mechatronic engineers. In order to face the new challenges of the transition from manufacturing 3.0 to Industry 4.0 and 5.0, it is necessary to implement innovative educational models that allow the systematic training of engineers. The competency-based education model has ideal characteristics to help mechatronic engineers, especially in the development of specific competencies. This article proposes 15 technical considerations related to generic industrial needs and disruptive technologies that serve to determine those specific competencies required by mechatronic engineers to meet the challenges of Industry 4.0 and 5.0.

Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world and is impacting work and education in general. In fact, the disruptive technologies that frame the Fourth Industrial Revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which could lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example, Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on mechanics. However, the specialized literature on Industry 4.0 says little about the importance of mechanics in the new industrial era, and more importance is placed on the evolution of Information and Communication Technologies and artificial intelligence. This article presents a frame of reference for the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between mechanics and artificial intelligence. To analyze Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined mechanics in the Third Industrial Revolution were used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes to the educational models of engineering education is described.

Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world, and is impacting work and education in general. In fact, the disruptive technologies that frame the fourth industrial revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which can lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on Mechanics. However, the specialized literature on Industry 4.0 says little about the importance of Mechanics in the new industrial era and more importance is given to the evolution of Information and Communication Technologies and Artificial Intelligence. This article presents a frame of reference about the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between Mechanics and Artificial Intelligence. For the analysis of Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined Mechanics in the third industrial revolution was used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes in the educational models of engineering education is described.

This study analyzed the effect of forming temperature on the roller hemming process of AA6011-T4 aluminum alloy sheets, using a 2^K factorial design to also evaluate the influence of roller diameter and flange height. A total of 24 experimental tests were conducted, varying the forming temperature (23 °C and 50 °C), roller diameter (22 mm and 50 mm), and flange height (7 mm and 10 mm). The hemming process was performed using a six-axis industrial robot (FANUC 2000i, Fanuc Corporation, Oshino, Japan) with roller tooling mounted o n a support fixture. The height of the flanged profile was measured using a coordinate measuring machine. ANOVA results, processed with MINITAB 18, showed that forming temperature, roller diameter, and flange height all have a statistically significant effect on the final profile height. No significant interactions were found among the factors, indicating their effects are independent. The most favorable configuration for maximizing profile height was the combination of the largest roller diameter and the highest flange height, under cold forming conditions. Additionally, a significant difference was observed between cold and warm forming processes in terms of the resulting profile height, highlighting the relevance of temperature control in the roller hemming of AA6011-T4 aluminum alloy.

This paper presents the experimental characterization and numerical optimization of the parametric design of inductors used for the induction heating process of metallic samples. Here, the variation of the geometric parameters of the inductor (the distance between the coil and the material, as well as the number of turns and separation between them), was analyzed. In addition, changes in the power input values of the heating system variables were also investigated; and finally, the heating time of the materials was taken into consideration. For the multi-objective numerical optimization process, a full factorial 2K experimental design was devised. Afterward, from preliminary experiments, a response surface was performed, from which the optimal candidates for the heating process were obtained. In the experiments, a methodology was designed for the acquisition and processing of heating data on ASTM E8 tensile test specimens. The main objective of this paper is to determine both the geometric parameters of the coil as well as the power input values of the circuit, which allow for generating uniform heating in the specimens. The objectives of the optimization process are to maximize the heating temperature minimizing the consumption of electrical current in the induction circuit. The data generated in the heating of the specimens were acquired using a Fluke® TI10 thermographic camera, while a BK Precision® regulated power supply was used for the induction circuit. The parameters obtained in the optimization process for the optimal candidates were corroborated through experimental tests. The paper shows the initial (non-optimal) and final (optimal) results obtained from the experimental tests. The heating station created with the optimal coil will be used for mechanical characterization tests on metallic materials at temperatures above room temperature.