https://repositorio.ifpe.edu.br/xmlui/handle/123456789/437 Trabalho de Conclusão de Curso - TCC Mon, 13 Jul 2026 16:18:44 GMT 2026-07-13T16:18:44Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2206 Design computacional e impressão de eletrodos porosos para confecção de gerador de hidrogênio do tipo Membraneless The combination of computational design and additive manufacturing is transforming the production of energy devices, such as membraneless hydrogen generators. Computational design involves the use of simulations and digital modeling to optimize the characteristics of porous electrodes. Through 3D modeling, it is possible to predict and adjust the structure of the electrodes, enhancing their efficiency. This process includes the optimization of porosity, geometry, and flow channel distribution, which are essential for improving the electrochemical reaction and electrode conductivity. Using 3D printers and specific materials, it is possible to create electrodes with high surface area and a controlled porous network. The main goal of this thesis was to develop topological structures based on TPMS-type functions to generate porous electrodes for the assembly of membraneless electrolyzers. By combining computational modeling, 3D printing, and conductive composite preparation, it was possible to fabricate electrodes with Schwarz P-type geometries, exhibiting good electrical properties. Furthermore, through an electrochemical electroplating process, the electrode was coated with a nickel layer over copper (Ni@Cu), which showed better hydrogen generation results from alkaline water electrolysis compared to the uncoated electrode. In conclusion, the process proved to be promising for the fabrication of electrodes for membraneless systems. Mon, 30 Sep 2024 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2206 2024-09-30T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2204 Uso da análise de dados e Machine Learning aplicado ao processo de separação e expedição em home centers: estudo de caso The order picking and customer service process in the expedition area of home centers presents high operational variability, resulting in delays in meeting internal service targets and directly affecting customer experience. Factors such as total load weight, number of items, delivery type, and operator productivity contribute to fluctuations in separation and service times, making it difficult to efficiently control operational flow. In this context, it becomes necessary to quantitatively understand the factors that influence delays and propose data-driven improvements. This Final Term Paper aims to analyze the separation and service process in the expedition area, identifying operational bottlenecks and proposing solutions through statistical analysis and Artificial Intelligence techniques. A realistically structured fictitious database containing 3,000 simulated records was developed according to the company’s operational rules. Descriptive statistical analyses were conducted to identify patterns, variability, and compliance with internal service targets. Additionally, supervised classification models, such as Decision Tree and Random Forest, were applied to predict service delays. The results showed that approximately 40% of orders exceeded the established service-time targets. Feature importance analysis indicated that total weight, operator productivity, and number of items are the main factors associated with delays. The study concludes that integrating process engineering, statistics, and Artificial Intelligence is an effective approach to support logistics optimization and improve operational decision-making. Tue, 10 Mar 2026 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2204 2026-03-10T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2057 Aplicação de ferramentas da qualidade e de análise de dados de telemetria: um estudo de caso em escavadeira hidráulica In industrial processes, quality management tools are used to ensure efficiency. The use of industrial statistics together with data from control and telemetry systems helps engineers in decision-making, with the objective of achieving product quality or improving performance indicators. Techniques such as PDCA (Plan, Do, Check, and Act) provide a robust structure for implementing improvements. In the present study, a process is improved using these tools. Telemetry data from a hydraulic excavator operating in ore loading are analyzed. A continuous improvement action using the PDCA cycle is implemented to impact three fundamental aspects of the operation: consumption, performance, and sustainability. After implementation, it was possible to reduce the annual fuel consumption cost of a single piece of equipment by approximately R$ 2.646,00. With the expansion of the improvement to the equipment fleet, savings can reach R$ 31.752,00 per year. Another outcome was the reduction in the operation cycle time, which decreased by up to 51.04% after the implementation of the process improvement. Finally, it was also possible to achieve an average reduction of 2.03% in the equipment’s emission rate between the adoption and implementation phases of the proposed procedure. Wed, 01 Jan 2025 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2057 2025-01-01T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1904 Construção de modelos de aprendizado de máquina para predição de tensão de ruptura de nanocompositos baseados em grafenos e derivados The development of nanocomposite materials through experimental studies for mechanical properties analysis is a costly activity, requiring considerable time and effort. Concurrently, advanced Machine Learning (ML) techniques have emerged as a promising alternative for efficiently generating information and predictions, also being used for predicting the mechanical properties of polymer nanocomposites based on graphene. Thus, the objective of this work was to develop an ML model for predicting the rupture stress of polymeric nanocomposites using graphene and derivatives as reinforcing materials. In this study, an independent database was created from academic literature, addressing studies on the rupture stress of polymeric nanocomposites using graphene and derivatives as reinforcing materials, and in parallel, chemoinformatics was employed, based on the unsupervised ML technique, Mol2vec, to generate information based on polymer structure. Consequently, a unique database was formed, and Auto-Sklearn was used to predict the rupture stress gain from the addition of graphene to polymeric nanocomposites. Auto-Sklearn, utilizing a pre-trained Mol2vec model with a dimension of 300, identified the Gaussian Process based regression model as the best-performing one, achieving an R² value of 0.769. This application demonstrated that unsupervised ML models efficiently provide information using molecular structures in conjunction with nanocomposites, extracting complementary information for the prediction of mechanical properties through ML models. This implementation emerges as an alternative to reduce the need for empirical data generation in mechanical property analysis, accelerating the evaluation process for nanocomposite compounds. Wed, 10 Jan 2024 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1904 2024-01-10T00:00:00Z