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.

SUMMARY: This paper presents a novel method for modeling a 3-degree of freedom open kinematic chain using quaternions algebra and neural network to solve the inverse kinematic problem. The structure of the network was composed of 3 hidden layers with 25 neurons per layer and 1 output layer. The network was trained using the Bayesian regularization backpropagation. The inverse kinematic problem was modeled as a system of six nonlinear equations and six unknowns. Finally, both models were tested using a straight path to compare the results between the Newton–Raphson method and the network training.

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.

This article introduces a new kinematic modeling method used to analyze coupled rigid multibody movements. The method was applied to the study of a 5R planar parallel mechanism’s kinematics and consists of analyzing two fixed configurations of the mechanism to systematize the rotational relationships between the two structures. Mathematical models were developed using complex numbers. The inverse kinematic problem was modeled as a system of eight nonlinear equations and eight unknowns, which was solved with Newton-Raphson’s method. Subsequently, with the inverse problem model, a numerical database related to the mechanism configurations, including singular positions, was generated to train a multilayer neural network. The Levenberg-Marquardt algorithm was used for network training. Finally, an interpolated linear path was used to understand the efficiency of the trained network.

Mechatronics developed under the influence of the Third Industrial Revolution and was a discipline that provided methods and tools for the development of industrial robots, advanced machine tools, mobile phones, and automobiles, among other sophisticated products. With the emergence of Industry 4.0 in 2011, mechatronics has become indispensable, as traditional production systems are being transformed into cyber-physical systems (CPS), some of which are composed of sophisticated technologies such as Digital Twins (DT) and sophisticated robots, among others. In 2020, the Fifth Industrial Revolution began, giving rise to so-called Human Cyber-Physical Systems (HCPS) and promoting the use of Cobots in industries. Because today’s industrial world is influenced by three active industrial revolutions and two transitions, it is possible to find machines and production systems that were designed with different principles and for different purposes, making it necessary to propose a classification that allows each system to be located according to the premises of its respective industrial revolution. This article analyzes the evolution of mechatronics and proposes a classification of machines and production systems based on the premises of each industrial revolution. The objective is to determine the influence of mechatronics on the different types of machines that exist today and analyze its implications.