Currently, vehicles come equipped with a growing number of electronic devices that enable various driving assis-tance systems. From an academic perspective, it is imperative that engineering students develop the skills and knowledge necessary to handle standardized devices in the automotive industry. To promote interaction with this technology and disseminate theo-retical concepts, innovative ideas, and critical thinking, educative workshops playa crucial role in engaging undergraduate students in the research field. This article presents a case study of an active learning method (project-based learning) in engineering education at Tecnologico de Monterrey, which involves developing an automotive control project using an industrial-level controller, i.e., a Vehicle Control Unit (VCU). This activity helps students to learn signal processing tasks, control design problems, and analyze the capabilities of VCU with simpler environments like Arduino. Typically, students conduct these activities following a research methodology. For this assessment, two groups of engineering students have been defined. One group has previously undergone a control engineering course, while the other group has not. The study evaluated the results obtained from the statisti-cal analysis of the scores of the two groups with respect to their involvement in industrial equipment projects and their perceptions of the project complexity. The first group consisted of undergraduates who had not taken a control course, and the second group included those who have already taken it. Therefore, the first group showed a better understanding of the subject than the second group. However, both groups expressed a preference for conducting engineering projects with real vehicles instead of scaled -down models.

Reducing the dimensional variability of the body-in-white (BIW) in automotive manufacturing is perhaps the most difficult quality control problem due to complex interdependencies amongst the multiple assembly stations that a BIW must pass through in a bodyshop. As increasing quantities of dimensional data are generated in factories, manufacturers face the challenge and opportunity to derive value from the data by enabling advanced quality control methods that can realize greater dimensional stability. As the BIW moves through the bodyshop, dimensional deviations propagate and amplify to downstream stations affecting final vehicle fit-and-finish and visible quality aesthetics potentially influencing a customers’ purchase decision. Current BIW quality approaches rely on univariate statistical process control (SPC) charts. With the large amounts of complex data produced, such charts often fail to detect quality patterns that may exist in hyper-dimensional spaces. As a stop-gap measure, manufacturers attempt to remediate quality issues by assigning operators in final vehicle assembly to visually identify and manually fix apparent deviations. This paper illustrates the application of artificial intelligence (AI) to develop a real-time monitoring system that seeks to predict and detect early dimensional quality issues and eliminate the need for costly downstream corrective actions. Moreover, beyond early detection and prediction, the proposed system also facilitates diagnosis of root causes and understanding the true nature of quality issues.

Many real-world data sets contain missing values, therefore, learning with incomplete data sets is a common challenge faced by data scientists. Handling them in an intelligent way is important to develop robust data models, since there is no perfect approach to compensate for the missing values. Deleting the rows with empty cells is a commonly used approach, this naive method may lead to estimates with larger standard errors due to reduced sample size. On the other hand, imputing the missing records is a better approach, but it should be used with great caution, as it relies on often unrealistic specific assumptions which can potentially bias results. In this paper, a new greedy-like algorithm is proposed to maximize the number of records. The algorithm can be used to generate various maximized sub-sets by varying the number of columns (features) that can be used for learning. It salvages more records than the naive method, and it avoids the bias induced by imputation. The learning algorithms would be able to learn from real sub-sets without the bias induced by artificial data. Finally, the proposed algorithm is applied to a case study, the COVID-19 Open Research data set (CORD-19) that was prepared and posted by The White House and a coalition of leading research groups as a call to action to the world's artificial intelligence experts to answer high priority scientific questions. This data set contains missing records, therefore, resulting maximized sub-sets from this analysis can be further investigated by the research community.

This study aims to develop a biofeedback tool to assess the efficiency of different learning and teaching techniques. Here, neuroengineering tools are used to detect and compare the level of learning obtained through two different modalities: a traditional text and an interactive video. Electroencephalographic signals were recorded in two groups during learning tasks, and performance was evaluated with an exam. Results showed better performance on the video group, as well as power changes in theta and beta bands, mainly in frontal and occipital cortices. A real time monitoring protocol of these features could be implemented to develop a learning evaluation tool based on biometric signals.

This document presents the methodology to implement a project of innovation in the classroom for the course of analysis and synthesis of mechanisms using 3D prototypes that can assist in understanding theoretic concepts that require spatial abilities. This project was conducted with faculty and students that generated different types of models that were improved through iterative exercises of design and experimentation. The application of this project was based on the experiential learning methodology and principles from the Montessori philosophy.

The main challenges currently faced by Higher Education Institutions (HEI) to achieve their integration with social agencies, industry, and government are discussed. Faced with the demand for solutions to increasingly complex problems and the modernization of teaching approaches that address these problems, the TransDisciplinary Learning Community (TDLC) model is proposed as a dynamic path to help educational systems, specifically HEI, to face these challenges.

Higher education institutions have a significant role in the development of the economy of any society. Its primary goal should lie in the generation, transfer, and application of knowledge. The institutions that take their role in this complex task seriously will achieve closer ties of dynamic collaboration and will be able to compete with other economies. This research addresses the mission, function, and challenges of curriculum and studies plan design and development in higher education institutions, to present a model proposal to foster articulated curricula between academic institutions and society.

Tecnológico de Monterrey is working hard to promote research in its undergraduate students. Research Path (RP) program was designed to motivate those students with skills and a willingness to do research and innovation as part of their curriculum for any academic program at Tecnológico de Monterrey. Many learning outcomes and experiences have been achieved including international research papers, technical developments, industrial projects and several innovations. The RP program has been adjusted and improved based on our experience and how similar strategies are implemented in the top ranked universities (based on the QS World University Ranking and the Academic Ranking of World Universities). Promoting the students engagement to fill the space beyond the student's own learning and up to the frontier of knowledge on a subject is one of our key strategies. The main challenge is to maximize the motivational benefits of a research problem so that students are cognitive engaged; special emphasis to this strategy is analyzed/discussed since it has been crucial in the success of the RP. It is advisable that universities wishing to develop a research program for undergraduate students scan what other organizations are doing, their experiences and incorporate the best practices in order to facilitate the implementation and ensure its success.

The paper describes a new curriculum for Engineering Education 4.0 based on the analysis of different references and essential relevant documents that explain the necessity to well-formed a new generation of professionals. Considering the technological change that world is going through, our current social environments and generational differences in a society focus on the extraneous and declares itself as the savior of the world. The university rises as a relevant and essential agent to ensure knowledge and development of competencies in this fourth industrial revolution, which has been called Industry 4.0 (I4.0). A series of digital technologies converge in applications transform the industrial processes so that they become more connected, reliable, predictable, resilient, and a high degree of certainty. All of this is due to the articulation of several capabilities of information technologies. Several efforts have been detonated in the world trying to better understand this evolution and in particular for Engineering Education towards the I4.0.

Based on a Global Chassis Control system with three-layers architecture (decision, control, and physical layers) a Fuzzy Logic (FL) approach is exploited. The FL based decision layer identifies the current driving condition of the vehicle and decides the control strategy to take care of this driving condition. A confusion matrix validates the classification results. The control strategy is implemented through the subsystems (suspension, steering, and braking) at the FL based control layer. The strategy was evaluated under two different tests: slalom and double line change by comparing the performance with an UnControlled system. Early results show the proposed strategy has less roll, yaw movement and side slip angle than an UnControlled system during a double line change maneuver; also, for the slalom test the proposal improves the dynamic vehicle performance allowing the driver to maintain the vehicle under control.

A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the performance of the proposed ANN model in comparison with the classical parametric model of Bingham. Based on the Root Mean Square Error index, an average error of 7.2 % is obtained by the ANN model, by taking into account 5 experiments with 10 replicas each one; while the Bingham model has 13.8 % of error.

An approach to find out the training inputs for identification of a Magneto-Rheological (MR) damper is proposed. Reduction of overuse of the damper, number of experiments and configurations of training inputs are main features of this approach. Experimental validation with a commercial MR damper was carried out.Main results show inputs configuration with modulated frequency at fixed amplitude displacement, and random amplitude step with fixed period generate key information. A feed-forward neural network was selected as model emulator. Modelling results showed an error-to-signal ratio lower than milli-thousands.

The precise variation of the magnetorheological (MR) damping force in a semi-active suspension is a key issue in order to assure the desired performances over a suspension control system. The open loop control of this force is a very common strategy. Other schemes propose adaptive controller alternatives while the automotive hardware is a constrained computation resource. This paper proposes the implementation of the damping force control system based on the MR damper using an internal model control approach. The controller and the internal model are proposed as artificial neural networks (ANN) trained and validated with realistic automotive datasets. The results shows good servo control and fast regulation to abrupt disturbances without on-line ANN tuning computations.

The aeronautic and automotive industries demand high quality products. Different aluminium alloys are used to produce them exploiting high speed machining (HSM) systems. HSM needs intelligent features such as monitoring and decision control. A framework that consists of four modules: data acquisition system, cutting tool monitoring, surface roughness prediction and intelligent planning module is presented. The planning module exploits genetic algorithms and Markov decision processes. This module supports and guides the operator in the operation of the CNC. Early results validate the benefits of this system in peripheral milling process, with a decrease between 18 % and 60 % in the operation costs.

The successful performance of machining centers involves selection, control and monitoring of a large number of parameters. Cutting tool condition is one of the most important variables due to the strong influence on dimensional accuracy and surface finish of the product. However, the cutting tool condition is difficult to measure online. A new proposal for online monitoring of the cutting tool condition based on Hidden Markov Models is presented. The generated vibration signals between the cutting tool and the workpiece are used for classification of the tool condition in different states (new, half-new, half-worn, and worn cutting tool). Feature vectors were obtained from these vibration signals by applying a triangular filter bank and computing the Mel Frequency Cepstrum Coefficients. The proposal includes a training step using the Baum-Welch algorithm and a diagnosis step exploiting the Viterbi algorithm. A Monte Carlo simulation validates a successful performance using experimental data in a face milling process. A comparison with classical approaches such as Artificial Neural Network is discussed.

This paper shows how state-of-the-art state estimation techniques can be used to provide efficient solutions to the difficult problem of real-time diagnosis in mobile robots. The power of the adopted estimation techniques resides in our ability to combine particle filters with classical algorithms, such as Kalman filters. We demonstrate these techniques in two scenarios: a mobile waiter robot and planetary rovers designed by NASA for Mars exploration.

This paper discusses the application of particle filtering algorithms to fault diagnosis in complex industrial processes. We consider two ubiq- uitous processes: an industrial dryer and a level tank. For these appli- cations, we compared three particle filtering variants: standard parti- cle filtering, Rao-Blackwellised particle filtering and a version of Rao- Blackwellised particle filtering that does one-step look-ahead to select good sampling regions. We show that the overhead of the extra process- ing per particle of the more sophisticated methods is more than compen- sated by the decrease in error and variance.