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Vehicle safety relies on the capacity of vehicle systems to decrease the likelihood of biomechanical injuries to both vehicle occupants and pedestrians. This can take the form of active or passive measures applied to the materials and their geometry in the event of an impact. Nevertheless, due to the nonlinear behaviour of materials and deformation, a distinct collapse process emerges that requires analysis; this analysis can be virtual or through experimental tests. While observed performance can be ascertained through experimental designs, executing such a design for every parameter combination can be time-consuming and costly. This study employs a Long Short-Term Memory (LSTM) to predict the energy absorbed and crushing force of thin-walled aluminium 6063 T5 tubes subject to different collapse triggers and heat treatments. The LSTM model is constructed using experimental data derived from experiments that consider trigger shape, area, trigger position, furnace duration, cooling temperature, and heat treatment soaking method. The LSTM model achieved a Root Mean Square Error (RSME) of 0.56 and 0.0025 for crushing force and energy absorption, respectively. LSTM proves to be a valuable tool for predicting results in nonlinear analysis, particularly in the context of crush behaviour. Also, a comparison of the LSTM and finite element analysis (FEA) predictive performance is presented.

Abstract The main challenge faced by many mechanical engineering educators is the implementation of real solutions during their courses. One alternative can be a realistic case study, where the students can be engaged in the development and analysis process currently applied in the industry. This kind of teaching process not only can be used to improve the quality of teaching‐learning process but also the students can have opportunities to solve real engineering problem. Therefore, this paper reports a realistic case implemented in mechanical engineering courses given in bachelor's and master's degrees. The component under evaluation has been selected considering the student's interest, this criterion was also taken to involve several students into a real project process to their learning development. The same problem was solved during an academic semester, it was solved using different approaches and student's abilities. This procedure can be replicated for students and teachers by following steps. The first step in this process was to describe the theoretical basis for modal analysis to understand how it was structured a numerical finite element model. This model was then solved and simulated using Hypermesh, Optistruct, and HyperView software to obtain numerical modal parameters. On the other, hand, the experimental modal parameters were obtained using a three‐dimensional scanning vibrometer on 60 samples. It is well noted that the implementation of engineering software commonly used at the industry would increase students' confidence. This allows students to perform real problem‐solving activities to develop outcomes as establish goals, plan task, meet deadlines. These activities are extremely important to the actual automotive industry. Therefore, this paper shows an engineering solution process to provide a learning alternative to teach the modal finite method solutions correlation with experimental solutions.

Surface material identification using hyperspectral imaging (HSI) analysis is a crucial and challenging issue in remote sensing. Researchers widely recognize that exploiting spectral-spatial data outperforms using spectral pixel-wise methods. Most of the work focuses on machine learning techniques to address issues with HSI categorization. The semi-supervised learning model, for example, was utilized in early research to address the issue of the expenses associated with training samples during HSI classification. Nevertheless, these approaches and techniques have certain drawbacks. The suggested framework must be improved in terms of hardware and computational capacity to enhance security. While integrated patch-based convolutional neural network (CNN) can help overcome these issues, due to the complicated geographical distribution of many targets, there are some downsides, such as using spectra as odorless vectors. The supervised learning techniques categorize the inputs by using a collection of appropriate examples for each class, referred to as training samples. For this purpose, samples must be obtained, however, the process is expensive and time-consuming. Deep learning techniques for evaluating spatial spectrum data have significantly advanced, yet security challenges remain in remote sensing with HSI. In this article, we introduce a novel HSI classification method, termed Deep-Detect, which leverages deep learning models with attention mechanisms to tackle these challenges. We use two weighting factors that may be obtained flexibly during the training phase to integrate both spectral and spatial properties, whose importance differs for various objects and scenarios. Additionally, we proposed a spatial and spectral fully convolutional networks (Spe-FCNs) attention mechanism that focuses attention on the input patches’ most informative regions. To verify performance, we used the northern Italy dataset from Pavia University and compared our proposed model with state-of-the-art HSI classification models. Our findings show that the proposed Deep-Detect model enhances classification accuracy while addressing key security concerns in HSIs.

Currently, various light-emitting diode (LED) lighting systems are being developed because LEDs are one of the most used lighting sources for work environments, buildings, homes, and public roads in terms of some of their applications. Similarly, they have low energy consumption, quick responses, and excellent optimal performance in their operation. However, these systems still need to precisely regulate lighting, maintain stable voltage and current in the presence of faults and disturbances, and have a wide range of operations in the event of trajectory changes or monitoring tasks regarding the desired voltage and current. This work presents the design and application of two types of robust controllers (structure-at-infinity control and geometric control) applied to an LED driver using a buck converter. The controllers aim to follow the desired trajectories, attenuate disturbances at the power supply input, and compensate for faults in the actuator (MOSFET) to keep the capacitor voltage and inductor current stable. When comparing the results obtained with the two controllers, it was observed that both present excellent performance in the presence of constant disturbances. However, in scenarios in which variable faults and path changes are implemented, the structure-at-infinity control method shows an overimpulse of output voltage and current ranging from 39 to 42 volts and from 0.3 to 0.45 A, with a margin of error of 1%, and it can generate a failure in the LED driver using a buck converter. On the other hand, when using geometric control, the results are satisfactory, achieving attenuating constant disturbances and variable faults, reaching the desired voltage (40 v to 35 v) and current (0.3 to 0.25 A) with a margin of error of 0.05%, guaranteeing a system without overvoltages or the accelerated degradation of the components due to magnetic conductivity.

To reduce the carbon footprint of manufacturing processes, it is necessary to reduce the number of stages in the development process. To this end, integrating additive manufacturing processes with three-dimensional (3D) printing makes it possible to eliminate the need to use tooling for component manufacturing. Furthermore, using 3D printing allows the generation of complex models to optimize different components, reducing the development time and realizing lightweight structures that can be applied in different industries, such as the mobility industry. Printing process parameters have been studied to improve the mechanical properties of printed items. In this regard, although the failure of most structural components occurs under dynamic load, the majority of the evaluations are quasistatic. This work highlights an improvement in fatigue strength under dynamic loads in 3D-printed components through heat treatment. The fatigue resistance was improved regarding the number of cycles and the dispersion of results. This allows 3D-printed polylactic acid components to be structurally used, and increasing their reliability allows their evolution from a prototype to a functional component.

Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.

This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99% molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94% of fit in the control signal and a 97% fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process.

It is necessary to reduce the weight of components while maintaining or improving their mechanical properties to withstand dynamic loads in lightweight structures. In this study, heat treatment and a trigger mechanism were implemented for a thin-walled tube of aluminium to increase energy absorption while reducing the peak crushing force. Different geometries and locations were proposed to trigger deformation in a controlled manner, in combination with heat treatments. Experimental designs for each energy absorption mechanism were performed, and designs were tested by quasi-static crushing. Data obtained from experiments were used to calculate energy absorption indicators that were used to compared designs with components without mechanism to analyse performance. By comparing proposed designs with tubes without modification, the best combination of design variables for each trigger mechanism were identified. It was determined that 160 mm from the upper side, 250 mm2 area and a rectangular trigger shape reduced peak crushing force by 22.03% and increased energy absorption by 37.76%. For heat treatment, the optimal combination was heating in a furnace at 175 ∘C for 1 h and cooling in water at 70 ∘C during 10 min while only soaking half of its length. This combination reduced peak crushing force by 19.02% and increased energy absorption 15.08%. When these mechanisms were combined on a single tube, peak crushing force was reduced by 21.63%, and energy absorption increased by 42.53%.

Pressure swing adsorption (PSA) technology is used in various applications. PSA is a cost-effective process with the ability to produce high-purity bio-hydrogen (99.99%) with high recovery rates. In this article, a PSA process for the production of bio-hydrogen is proposed; it uses two columns packed with type 5A zeolite, and it has a four-step configuration (adsorption, depressurization, purge, and repressurization) for bio-hydrogen production and regeneration of the beds. The aim of this work is to design and use an active fault-tolerant control (FTC) controller to raise and maintain a stable purity of 0.9999 in molar fraction (99.99%), even with the occurrence of actuator faults. To validate the robustness and performance of the proposed discrete FTC, it has been compared with a discrete PID (proportional–integral–derivative) controller in the presence of actuator faults and trajectory changes. Both controllers achieve to maintain stable purity by reducing the effect of faults; however, the discrete PID controller is not robust to multiple faults since the desired purity is lost and fails to meet international standards to be used as bio-fuel. On the other hand, the FTC scheme reduces the effects of individual and multiple faults by striving to maintain a purity of 0.9999 in molar fraction and complying with international standards to be used as bio-fuel.

Lightweight materials are in constant progress due to the new requirements of mobility. At the same time, it is mandatory to meet the internal standards of the original equipment manufacturers to guarantee product quality, and market regulations are necessary to reduce or eliminate pollution emissions. In order to reach these technical requirements, the design is optimized, and new materials and alloys are evaluated. The search for these new types of materials is long and expensive. For this search, new technologies have emerged, such as integrated computational materials engineering, which is a valuable tool to forecast through simulation alloy characteristics that meet specific requirements without fabrication. This research develops an artificial neural network to establish the chemical composition of a new aluminum alloy based on the desired manufacturing characteristics as well as fatigue strength. For this, the proposed artificial neural network was trained with the chemical composition of preexisting aluminum-based alloys and the resulting desired mechanical properties. The significant contribution of the proposed research consists not only of the neural network high-performance forecasting but also the fact that for to train and validate it, not only simulations of its responses to the different possibilities of alloys were tried but also validated through an experimental laboratory test performed by uniaxial machine. The proposed artificial neural network results show an average correlation of 99.33% between its forecasting and laboratory testing.

El seudoaneurisma de aorta ascendente, como complicación en cirugía cardíaca, tiene una incidencia menor del 0,5%, pero una mortalidad que varía del 6,7-60%. La infección del graft y de la mediastinitis son los principales factores predisponentes. Su clínica puede ser variada, o ser hallazgo casual en controles posteriores. Se presenta, a continuación, el caso clínico de un paciente por postoperatorio de cirugía de Bentall, complicado después de 14 meses con un seudoaneurisma de aorta ascendente, como así también su serie de imágenes diagnósticas. La cirugía fue llevada a cabo utilizando canulación arterial subclavia y venosa femoral, requiriendo parada circulatoria para su correcta disección. Este caso refleja una enfermedad poco frecuente, pero altamente desafiante, no solo por la complicación en sí misma, sino también por la necesidad de una reoperación y correcta estrategia quirúrgica.

Additive Manufacturing has become a vital component of the global economy, revolutionizing manufacturing processes, enhancing mechanical components, and addressing current industry challenges such as increasing production rates. This study explores the tensile strength and stiffness of 3D-printed Onyx, focusing on the effects of printed perimeter layers. Results show that increasing perimeter layers enhances tensile strength by thickening external walls and improving stress distribution. Experiments demonstrate improvements between 2 and 15 layers do not exceed 20%, and perimeter layers show no impact on toughness. Also, internal infill patterns and density play a more significant role in overall strength once sufficient perimeter layers are in place. Two layers are typically sufficient to ensure cohesion, minimize deformation, and prevent micro-crack propagation. Onyx’s nylon matrix and carbon fibers further improve durability by mitigating stress concentrations in the transition zone between the perimeter and inner layers. However, beyond a certain point, additional layers yield diminishing returns, primarily increasing material consumption without significant strength gains. These findings support future research into additional properties like shear strength and impact resistance while balancing performance, material use, and sustainability.

Passive safety systems have been evaluated for their ability to transform impact energy into deformation to reduce the probability of damage to passengers during crash events. Low-speed impacts are common during collisions and many structural components are not replaced after such collisions because of the recovery of visual components such as the bumper fascia. However, automotive foams and brackets deform permanently in case a new impact fails to dissipate energy. In this study, a thin-walled printed Onyx component was fabricated via additive manufacturing. This material was used to dissipate energy at low-cycle fatigue and recovery in the peak crushing force after the first crushing cycle. The thin-wall crash box printed with Onyx, can be designed to recover energy absorption in different regions of the crushing displacement. The first peak crushing force and the mean crushing force are recovered according to the geometry and small displacements. However, in medium and long crushing displacements, at the end of the compression its dissipation capacity is increased. Onyx printed mechanical absorber withstand fifteen load cycles, recovering the peak load 19.25%.

Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance. Moreover, they usually involve the collection and labeling of large training datasets, which is costly and time-consuming. In this study, we propose a template-based matching algorithm that utilizes a novel perceptual hashing method for binary images, enabling fast and robust pose estimation. This approach allows the automatic preselection of a subset of templates, significantly reducing inference time while maintaining similar accuracy. Our solution runs efficiently on multiple devices without GPU support, offering reduced runtime and high accuracy on cost-effective hardware. We benchmarked our proposed approach on a body-in-white automotive part and a widely used publicly available dataset. Our set of experiments on a synthetically generated dataset reveals a trade-off between accuracy and computation time superior to a previous work on the same automotive-production use case. Additionally, our algorithm efficiently utilizes all CPU cores and includes adjustable parameters for balancing computation time and accuracy, making it suitable for a wide range of applications where hardware cost and power efficiency are critical. For instance, with a rotation step of 10° in the template database, we achieve an average rotation error of 10°, matching the template quantization level, and an average translation error of 14% of the object’s size, with an average processing time of 0.3s per image on a small form-factor NVIDIA AGX Orin device. We also evaluate robustness under partial occlusions (up to 10% occlusion) and noisy inputs (signal-to-noise ratios [SNRs] up to 10 dB), with only minor losses in accuracy. Additionally, we compare our method to state-of-the-art deep learning models on a public dataset. Although our algorithm does not outperform them in absolute accuracy, it provides a more favorable trade-off between accuracy and processing time, which is especially relevant to applications using resource-constrained devices.

Desde hace más de una década, el abordaje mínimamente invasivo surgió con el objetivo de lograr mismos resultados que la cirugía convencional a través de incisiones más pequeñas, pero con una menor repercusión para el paciente. En ciertas ocasiones, el reemplazo de válvula aórtica requiere del agrandamiento anular para implantar una prótesis acorde al índice corporal y evitar un missmatch paciente-protésico, llevando a abandonar este enfoque debido a la dificultad técnica que puede representar. Se presenta una serie de casos donde tanto el reemplazo valvular como el agrandamiento anular son llevados a cabo a través de miniesternotomía. Consideramos que la cirugía cardiaca se va estableciendo en el campo de la cirugía mínimamente invasiva y deben buscarse alternativas para mantener este enfoque sin aumentar la morbimortalidad del paciente.

Biofuels produced from renewable raw materials, in this case bioethanol, provide a sustainable and renewable energy source for the future, as bioethanol positively impacts the economy, the environment, and society. Bioethanol is an alternative and immediate solution to mitigate the main greenhouse gases generated by transportation and industries that use fossil fuels. However, to produce bioethanol it is necessary to use advanced dehydration processes or technologies. Currently, azeotropic distillation, extractive distillation, and the Pressure Swing Adsorption (PSA) process using selective zeolites on water molecules are used. This PSA process has shown high selectivity, high yield, and high energy efficiency for producing anhydrous ethanol compared to other technologies. This work aims to implement automatic control laws (geometric and PID) to maintain stable the desired purity (99.5%), have higher bioethanol recovery and generate higher productivity using less energy. Both controllers performed adequately on the PSA bioethanol-producing plant, however, the geometric control presented greater robustness against disturbances, achieving to maintain stable bioethanol purity above 99% by wt, generating a recovery of 73.62%, with productivity of 59.07 kmol and using an energy efficiency of 59.21%. Using this control law, it was possible to use the entire length of the columns to adsorb a greater amount of water molecules and achieve higher production.

El origen anómalo de la arteria coronaria derecha de la arteria pulmonar (ARCAPA) es una entidad de muy baja frecuencia. Pocos casos se han publicado en la literatura sobre la misma en la edad adulta. La forma de presentación varía según el tipo de anomalía coronaria y la cantidad de circulación colateral. Nuestro caso resalta la rareza de presentación de esta enfermedad, así como su única forma de tratamiento en un paciente de 43 años con ángor que se presenta en la consulta ambulatoria.

This paper presents a plantwide control strategy for optimizing a pressure-swing azeotropic distillation process used in tetrahydrofuran dehydration. Leveraging Skogestad’s methodology, this strategy focused on two distillation columns: a low-pressure column for water recovery at 20 psia and a high-pressure column that achieved 0.99 molar fraction purity of tetrahydrofuran at 115 psia. This study identified critical control variables through plant analysis by implementing PI controllers in the regulatory control layer to stabilize flows and pressures. In the supervisory control layer, a PI controller combined with MIMO MPC effectively enhanced the product purity and reduced the energy consumption by 36%. Stable inlet and outlet flow conditions (100 lbmol/hr inlet, 29.59 lbmol/hr outlet) were maintained without compromising the equipment integrity. The operational ranges for the process included variations in the tetrahydrofuran mole fraction from 0.25 to 0.35 at the inlet, which demonstrated a robust performance across perturbations. These achievements signify significant advancements in process efficiency and sustainability, offering substantial reductions in energy usage while ensuring consistent high purity levels in tetrahydrofuran production. The developed control structure sets a new standard for efficient azeotropic distillation processes, with implications for enhancing operational performance across industrial applications.