REPOSITÓRIO P.PORTO
Repositório Científico do Politécnico do Porto
Entradas recentes
Mutual promotion on the mechanical and tribological properties of the nacre-like self-lubricant film designed for demanding green tribological applications
Publication . Hongbo, Ju; Luan, Jing; Wang, Yiping; Bondarev, Andrey; Evaristo, Manuel; Geng, Yaoxiang; Xu, Junhua; Cavaleiro, Albano; Fernandes, Filipe; Fernandes, Filipe
The inverse relationship between the tribological and mechanical properties of environmentally friendly self-lubricant films, induced by the addition of soft lubricant agents that can diffuse quickly at elevated temperatures, has hindered the widespread use of these materials in industrial applications. This paper took this challenge to break through the above established relationship by developing novel nacre-like multilayered Mo2N–SiNx/Ag–SiNx self-lubricant films via an radio frequency (RF) magnetron sputtering system for real applications where harsh conditions at elevated temperatures exist. The multilayered films, deposited by alternating deposition of Mo2N–SiNx and Ag–SiNx modulation layers, exhibited three phases of face-centered cubic (fcc) Mo2N, fcc Ag and SiNx, where SiNx encapsulated the nano-crystalline Mo2N and Ag phases in each layer to successfully induce a “brick and mortar” nacre-like microstructure (in the area without the coherent structure). The epitaxy growth of the Ag–SiNx layers with thickness below 6 nm on the Mo2N template resulted in an extraordinary increase in both the hardness and elastic modulus, which was able to prevent severe degradation of the mechanical properties caused by the addition of Ag. The room-temperature anti-friction property could be enhanced by increasing the Ag–SiNx layer thickness due to the excellent lubricant nature of Ag, which acts in synergy with Mo2N, while the wear rate below 4×10−8 mm3/(N·mm) was due to the high mechanical strength. The tribological properties at 600 °C also benefited from the interlocked multilayered architecture, which allowed an extreme low friction coefficient of ~0.12 and a negligible wear rate (WR). This behavior was attributed to the synergism between the lubricant action of Ag and Mo2N and the tribo-phase transformation from Ag2Mo4O13 to Ag2MoO4.
Performance analysis of steel W18CR4V grinding using RSM, DNN-GA, KNN, LM, DT, SVM models, and optimization via desirability function and MOGWO
Publication . Fernandes, Filipe; Touati, Sofiane; Boumediri, Haithem; Karmi, Yacine; Chitour, Mourad; Boumediri, Khaled; Zemmouri, Amina; Moussa, Athmani
This study presents an innovative approach to optimizing the grinding process of W18CR4V steel, a high-performance material used in reamer manufacturing, using advanced machine learning models and multi-objective optimization techniques. The novel combination of Deep Neural Networks with Genetic Algorithm (DNN-GA), K-Nearest Neighbors (KNN), Levenberg-Marquardt (LM), Decision Trees (DT), and Support Vector Machines (SVM) was employed to predict key process outcomes, such as surface roughness (Ra), maximum roughness height (Rz), and production time. The results reveal significant improvements, with Ra values ranging from 0.231 μm to 1.250 μm (up to 81.5 % reduction) and Rz from 1.519 μm to 6.833 μm (up to 77.7 % reduction). The hybrid DNN-GA model achieved R2 > 0.99, reducing prediction errors by 23–45 % compared to traditional models. Optimization via the Desirability Function achieved Ra values around 0.341 μm and Rz around 2.3 μm, with production times ranging from 1181 to 1426 s. The innovative Multi-Objective Grey Wolf Optimization (MOGWO) provided Pareto-optimal solutions, minimizing Ra to 0.3 μm, Rz to 1.5 μm, and production times between 2000 and 3000 s, offering better balance between surface quality and machining efficiency. This work highlights the unique integration of machine learning models with optimization techniques to significantly enhance grinding performance and manufacturing efficiency in high-precision industries.
Zirconium aluminum nitride thin films for temperature sensing applications
Publication . Fernandes, Filipe; Martins, Bruno; Patacas, Carlos; Cavaleiro, Albano; Faia, Pedro; Alves, Cristiana F. Almeida; Carbo-Argibay, Enrique; Ferreira, Paulo J.
This study explores the development and characterization of zirconium aluminum nitride (ZrAlN) thin films produced via magnetron sputtering for temperature sensing applications. The sensor film is integrated into a fully nitride multilayer coating and designed to work in harsh environments. The ZrAlN demonstrated stable semiconductor behavior up to 750 °C, making it suitable for high-temperature thermistors, with a β value of approximately 850 K after signal stabilization. Detailed structural characterization confirmed a mixed-phase structure of poorly crystalline cubic ZrN and orthorhombic Zr3N4. This structure is believed to be responsible for the high resistivity of 8.0 × 105 µΩ·cm observed in Zr1-xAlxN with x = 0.3. The examination of Zr0.7Al0.3N integrated into the multilayer coating revealed a columnar morphology with diffuse nanolayers, alternating between aluminum-rich and aluminum-poor zones, caused by the two-fold rotational deposition. The sensor coating was further tested on a cutting tool substrate, with the Zr0.7Al0.3N layer exhibiting a sensitivity of 800 K and demonstrating effective temperature measurements up to 400 °C. The Zr0.7Al0.3N layer inserted in a nitride-based multilayer coating, combined with Arduino® for signal acquisition, resulted in a measured error of approximately 7 %. The setup presented the potential for integration into manufacturing environments aligned with Industry 4.0.
Abordagem fuzzy híbrida para detecção de fraudes em programas de participação ativa de usuários finais de energia
Publication . CARVALHO, GUILHERME ABREU; Vale, Zita Maria Almeida do; Gomes, Luís Filipe de Oliveira; Faia, Ricardo Francisco Marcos; Costa, Edson Bruno Marques
Os avanços nas redes elétricas inteligentes têm desempenhado um papel crucial na descarbonização,
facilitando a integração de fontes de energias renováveis e otimizando o equilíbrio entre
oferta e demanda de energia. Nesse contexto, um dos principais objetivos das Smart Grids é aprimorar
a interação entre os usuários finais e o sistema elétrico, por meio da implementação de
programas que incentivam a redução do consumo e a adoção de práticas mais sustentáveis. No
entanto, apesar das vantagens significativas resultantes da expansão da infraestrutura energética,
surgem novos desafios relacionados à segurança e à integridade dos dados coletados. Sem
mecanismos robustos de validação, o sistema fica vulnerável a manipulações, o que pode comprometer
a eficácia dos mecanismos de distribuição de benefícios. Para resolver esse problema,
a presente dissertação propõe uma abordagem fuzzy híbrida: um modelo evolutivo orientado a
dados (denominado de Evolving Takagi‐Sugeno Plus) e um sistema fuzzy Mamdani baseado em
conhecimento. O método evolutivo é utilizado para modelar e prever os padrões de comportamento
dos indivíduos em programas de participação ativa de usuários finais de energia. Este é
capaz de evoluir dinamicamente, adaptando seus parâmetros e ajustando sua estrutura automaticamente
a partir das amostras recebidas. Durante a etapa de concepção do modelo, o método
foi comparado com outras técnicas disponíveis na literatura, mostrando resultados competitivos,
especialmente em relação ao tempo de execução. Por outro lado, o sistema Mamdani utiliza o
resíduo obtido entre a saída do modelo evolutivo e os dados reais de flexibilidade, combinados
com informações sobre geração e consumo de energia, para estimar um grau de alerta caso
comportamentos anômalos sejam identificados. Os resultados desta fase indicam que o sistema
proposto detecta tanto fraudes pontuais quanto aquelas que ocorrem ao longo de períodos extensos.
Dessa forma, os métodos combinados demonstram potencial de aplicação em contextos
práticos, auxiliando as entidades gestoras na tomada de decisões por meio de uma metodologia
robusta e altamente interpretável.
IoT Based Automated Moving System for Weaving Inspection
Publication . CARVALHO, JOÃO FRANCISCO FERREIRA MOREIRA DE; Figueiredo, Lino Manuel Baptista
This dissertation explores the significant impact of the textile industry on waste
production, focusing on Smartex’s commitment to eco-friendly practices. Smartex,
primarily active in the knitting market, aims to expand its sustainable solutions to
the weaving sector. The crux of this project was to develop an automated moving
system for weaving inspection. This system integrates innovative technologies such
as linear motion solutions, Internet of Things (IoT), and communication protocols,
highlighting a synergy between mechanical engineering and electrical and computer
engineering principles.
The hardware framework of the system comprises dual lead screws driven by stepper
motors, managed via a Raspberry Pi 4 and an HR8825 motor driver. This setup
is controlled by software that operates within the MQTT network. This network is
distinctive for its dynamic election broker system, which enhances the automation’s
reliability by providing network redundancy crucial for industrial applications.
While the MQTT automated solution demonstrated success, the mechanical aspect
faced challenges, signaling potential future precision issues due to slippage.
This outcome highlights a disparity between the project’s complexity and Smartex’s
philosophy of creating simplified, easy-to-install products. The project, therefore,
serves as a conduit for applying academic and industry knowledge, underscoring the
value of such collaboration for personal and professional development.