The Journal of Engineering and Exact Sciences

Topology Optimization and Experimental Validation of a Baja Competition Brake Pedal Design

Artur Fernando de Vito Junior — 2025-03-06

The automotive industry continually seeks innovative methods to reduce vehicle weight, enhance efficiency, and maintain structural integrity. This study presents a detailed framework that combines topology optimization (TO) and experimental validation, applied specifically to the design of a brake pedal for a Baja competition vehicle. The research involves a multi-step process, starting with finite element modeling, followed by TO using ANSYS Workbench to optimize the pedal's material distribution for weight reduction. Post-processing in Siemens NX is then performed to incorporate manufacturing constraints, ensuring the design is suitable for water jet cutting. The optimized brake pedal, constructed from 7075-T6 aluminum, weighs 42.6 g and demonstrates stress levels well below the yield strength of the material. Dimensions were determined based on the available space within the vehicle to ensure that the brake pedal would not interfere with any vehicle structure and would not impede the driver during operation. These considerations ensured that the pedal’s design maximized comfort and safety for the driver. Experimental validation is conducted through force and strain measurements, resulting in a 3.5% discrepancy between simulated and experimental stress values. Over 50 hours of track testing, including extensive on-road competition use, confirms the robustness and practical viability of the optimized design. This work underscores the potential of TO as a powerful tool for lightweight component design, demonstrating its integration with real-world testing and the ability to enhance the manufacturability of critical automotive components. The framework presented here not only validates the design process for brake pedals but also offers a versatile approach applicable to other vehicle components, contributing to the broader goal of optimizing automotive performance through lightweight designs.

Assessing the efficiency of environmentally sustainable cutting fluids in industrial machining applications

Antônio Santos Araújo Junior — 2025-02-28

The drive towards sustainable manufacturing processes has led to a renewed focus on environmentally friendly alternatives in the industrial sector. Initially, the technological evolution focused on efficiency while overlooking the harmful consequences for operators and the environment. However, increasing concerns about environmental pollution and the need to comply with regulations prompted revisions in cutting fluid compositions to eliminate toxic agents with detrimental environmental impacts. This study meticulously examines the potential of various vegetable oils, including babassu, canola, cotton, corn, soya, and sunflower, as cutting fluids, comparing their performance to the well-established commercial oil LB2000. The assessment encompasses critical parameters, notably wettability, cooling efficiency, and lubricity. Our findings indicate that these edible oils exhibit behaviors closely mirroring those of the industry-standard LB2000, affirming their compatibility with machining operations. The adoption of edible oils as cutting fluids presents a compelling avenue, underpinned by their biodegradability and the prevailing global inclination towards reducing reliance on petroleum-based cutting fluids. In terms of practicality, these tested vegetable oils demonstrate remarkable suitability as cutting fluids, offering enhanced lubricity, particularly in the cutting region. Supplementary tests, spanning wetting properties, cooling capabilities, and lubricity, reaffirmed these vegetable oils' efficacy as cutting fluids, squarely within the boundaries of acceptability for industrial machining. In summary, this investigation highlights the viability of vegetable oils as sustainable and high-performance substitutes for conventional cutting fluids in industrial applications.

Modeling of Fluid Content in Vibratory Screening Residual Solids Using Artificial Neural Networks

Rodrigo Sislian — 2025-03-06

The proper management of drilling waste, particularly the fluid content in residual solids from shale shakers, remains a critical challenge in oil and gas operations. Traditional methods relying on laboratory analysis introduce significant delays, hindering real-time process optimization. This study proposes an artificial neural network (ANN)-based virtual sensor to predict fluid content in vibratory screening residual solids in real time. Experimental data were collected from an industrial shale shaker system under varied operational parameters, including motor speed, feed flow rate, and screen inclination. A multilayer perceptron model was developed using TensorFlow, featuring input normalization, dropout regularization, and optimized training with stochastic gradient descent. The ANN architecture achieved a mean absolute error of 0.03 and a loss of 0.002, demonstrating robust convergence without overfitting. Statistical validation via t-tests confirmed no significant difference between predicted and experimental values (p-values of 0.67 for test data and 0.85 for the full dataset). The model’s accuracy under stable operating conditions enables continuous monitoring without additional hardware, addressing the industry’s reliance on delayed laboratory measurements. Key implications include real-time operational adjustments, reduced waste management costs, and a scalable solution for existing systems. This work bridges a critical gap in Artificial Intelligence (AI) applications for solids control, offering a practical framework for enhancing separation efficiency and sustainability in drilling operations.

Creative Organizational and Decision Processes from Artificial Generative Intelligence and Innovation Management

Lucas Gonçalves Dias — 2025-02-24

The objective of this work was to develop an academic and critical review based on scientific articles: Generative Artificial Intelligence in Innovation Management: a preview of future research developments, published in 2024, in English, in the scientific journal Journal of Business Research. The article addresses central concepts, such as: Generative Artificial Intelligence, Organizational Innovation and Innovation Management. The original work is based on the premise that the use of Artificial Intelligence will allow greater opportunities for students and collaborators, not just academically and professionally. Therefore, this content is interesting for undergraduate and post-graduation students, due to the fact that Artificial Generative Intelligence has the capacity to promote changes and revolutionize the way people and companies develop products, services and new ways of thinking. The gain in training in this topic is the possibility of becoming a more complete professional prepared to act in an increasingly digital and automated market, with the ability to identify and address its demands and opportunities.

Improved Performance of Box Truck Life using Computational Analysis

Douglas Nogueira de Lima — 2025-03-06

This case study presents an investigation of the structural behavior of a box truck body mounted on the chassis of a Volkswagen Constellation 24.330, utilizing Finite Element Method (FEM). The study specifically evaluates the performance of the structure under static and fatigue conditions, comparing scenarios with and without reinforcement applied to the rear column. Aimed at improving the structural performance and durability of commercial vehicles, the research focuses on identifying stress distribution, analyzing torsional behavior, and addressing common structural failures such as cracks and deformations. The results demonstrate that the implementation of rear column reinforcement enhances the structural performance of the box body. Stress concentrations were reduced by over 30% under uniform load conditions, while maximum displacement under torsion decreased by 64%. Additionally, the torsional stiffness of the structure increased by 162%, leading to improved durability and greater resistance to dynamic loads. The fatigue analysis showed infinite life for most parts of the reinforced structure; however, critical areas, such as the intermediate columns and lower I-beam regions, were identified as stress concentration zones requiring closer attention to manufacturing quality. This study highlights the importance of computational simulations in optimizing vehicle design by enabling accurate predictions of structural behavior. By reducing stress concentrations and improving durability, the proposed solution addresses the challenges faced by commercial vehicles and enhances their safety and reliability in the market.

Production of Potassium Fertilizer from Verdete: A Review

Raquel Stavale Schimicoscki — 2025-02-28

This review examines the geological and physicochemical properties of Verdete, a potassium-rich rock formation from the Serra da Saudade region (Minas Gerais state, Brazil), and evaluates its viability as a sustainable source of potassium for fertilizer production. The review discusses the composition of Verdete, characterized by mineral associations including glauconite, illite, and quartz, and highlights its potential nutrient profile, which consists of essential elements such as K, Mg, and Ca. The review focuses on various beneficiation methods, emphasizing thermal treatments, acid and alkaline leaching, biological and hydrometallurgical processes that have shown promise in optimizing potassium recovery. By synthesizing recent advancements in the field, this study aims to provide insights into the future of potassium extraction techniques, the environmental implications of mining practices, and the role of Verdete in sustainable agriculture. The findings suggest that further research and technological innovation are essential to maximize the economic and ecological benefits of Verdete as a potassium source.

Quantification of Heavy Metals (Pb2+, Zn2+, Cd2+) by Square Wave Voltammetry through Anodic Re-dissolution in Landfill Leachate

Larissa Souza Fernandes — 2025-03-06

The rapid increase in waste production, driven by the Industrial Revolution, has led to significant environmental challenges, particularly the contamination of soil and water from landfill leachate. This study aims to evaluate the removal of heavy metals (zinc, cadmium, and lead) from landfill leachate through chemical precipitation using the analytical technique of square wave voltammetry by anodic re-dissolution. The study involved sequential stages, starting with adjustments to the electroanalytical method and calibration curve development, followed by precipitation assays with a synthetic metal solution to optimize variables for heavy metal removal. Precipitation experiments were conducted using zinc, cadmium, and lead ions with calcium hydroxide and sodium carbonate, and voltammetric analyses were performed using square wave anodic stripping voltammetry to assess metal concentrations. The study examined the electrochemical behavior of Bi³? using square wave voltammetry, revealing linear relationships between peak current and frequency, indicating reversibility in the reaction. Optimization of parameters such as frequency, step, and pulse amplitude improved the precision and selectivity of the analysis. Bi³? concentration was optimized for maximum electroanalytical response, with a concentration of 1.25 mg L?¹ selected. Deposition time was also optimized, with 300 seconds providing the best results. Metal removal efficiency using precipitating agents (Ca(OH)? and Na?CO?) was analyzed, showing higher efficiency for lead and cadmium with Ca(OH)?. The study highlights the significance of pH and agent concentration in the removal process. This study evaluated the removal of heavy metals (zinc, cadmium, and lead) from landfill leachate using chemical precipitation with calcium hydroxide and sodium carbonate. The process achieved high removal rates, particularly for lead (97.97%). Square wave voltammetry was successfully developed for precise quantification, with statistical validation confirming its reliability for this application.

Recombination Effects on Base Transit Time in Exponentially-Doped SiGe Heterojunction Bipolar Transistor (HBT): An Analytical Approach

Muhammad Johirul Islam — 2025-03-06

In conventional models of base transit time in SiGe HBTs, the recombination in the base is usually neglected because the base is considered very thin to justify simplifications in the numerical calculation. Indeed, this assumption turns out not to be very valid at moderate injection levels, since carrier recombination could have important consequences for transistor performance. The paper presents an analytical model, which includes the recombination process in the exponentially doped base of SiGe HBTs operating under intermediate injection conditions. The inclusion of recombination makes the mathematical solution very complicated. In this work, an exponential approximation technique together with perturbation theory is applied, making it possible to solve the equations with a high degree of accuracy while reducing the mathematical complications. The model that is proposed clearly depicts that the recombination effects might play an important role in defining the base transit time of SiGe HBTs with a non-uniform doping profile. It also shows that any neglect of recombination could be seriously wrong, particularly in devices operating under an intermediate injection level. The insight obtained from the results of this study will lead to a deeper understanding of the behavior of SiGe HBT and its design and optimization for improved performance in various applications. A practical method developed herein is applied to the analysis of recombination effects with limited complication.

Academic Success: tutoring and machine learning programs used to generate personal and professional development in university students

Luísa Raeder Ribeiro — 2025-02-24

The objective of this work was to prepare an academic and critical review based on the scientific article: Effectiveness of Tutoring at School: a machine learning evaluation, published in 2024, in English, in the scientific journal Technological Forecasting & Social Change. The article addresses central concepts, such as Artificial Intelligence and Machine Learning, in analyses of public policies and tutoring programs aimed at university students. As a main premise, the original work, through extensive quantitative research, demonstrates how supervision, support and guidance of students can reduce the chances of dropping out of studies and academic failure. Machine Learning, from the perspective of an analytical tool and methodological innovation, can, according to the article, influence and optimize student performance. The review contributes to undergraduate and graduate students seeking training in emerging technologies, developing skills in research topics and motivating the generation of skills in a scenario of high competitiveness and rapid technological evolution, so that they can achieve and sustain academic and professional success.

Keywords: Artificial Inteligence. Tutoring Program. Machine Learning.

Comparative Study of the Effects of Accelerated Corrosion on Conventional Concrete and Concrete with Recycled Aggregates

Luiz Felipe Brioschi Caliman — 2025-01-27

In addition to the consumption of non-renewable raw materials, the growth of the construction sector generates a high volume of waste, which is harmful to the environment when improperly disposed of. Aiming for sustainable development focused on reducing such impacts, technologies such as recycling construction and demolition waste have been developed to enable the replacement of natural aggregates in concrete with recycled aggregates. However, the distinct characteristics and properties of these materials also suggest behavioral changes. Thus, the present research aims to compare the effects of accelerated corrosion on conventional concrete and concrete with 25% and 50% of natural coarse aggregates replaced with recycled coarse aggregates in their mix proportions. The study begins with the physical and chemical characterization of the components used in concrete production to identify their properties, a process conducted through standardized tests. The specific and bulk densities of the recycled aggregate showed lower values, whereas water absorption was approximately 47 times higher than that of natural aggregate. Once the characteristics were identified, it was possible to determine the mix proportions using the ABCP/ACI mix design method. After defining the mix, test specimens were cast with mass-based substitutions and then subjected to compression tests and water absorption tests by immersion. The modified immersion accelerated corrosion method was used to compare the mass loss of steel due to chloride ion action. Finally, the analysis of the results showed similar mechanical behaviors for both formulations, with recycled aggregate concrete being less susceptible to accelerated corrosion.