Sérgio Ricardo Lourenço

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry.

Production Planning and Control is directly involved with production activities in the polymer packaging industry, aiming to produce more with fewer resources. This reduces polymer scrap, generating positive results in economic and environmental terms. The importance of reducing polymer waste during the extrusion process and the use of theoretical models and simulation tools, along with recycling and reprocessing practices, emerges as an effective strategy to improve production and minimize environmental impacts. This study aims to evaluate the economic and environmental gains of adopting activities/tools used in production planning and control in the polymer packaging industry. The research method adopted was a case study conducted through semi-structured interviews. It was concluded that polymer scrap in the production system was reduced through actions taken by production planning and control, contributing to the micro-level circular economy, generating significant economic and environmental gains.

O artigo avalia os impactos ambientais associados às camisetas feitas de algodão, PET reciclado e poliamida 6.6, com o objetivo de fornecer informações sobre a produção têxtil sustentável. A metodologia envolve a Avaliação do Ciclo de Vida (ACV) que examina vários fatores, como consumo de água, emissões de CO₂, uso de energia, durabilidade e reciclabilidade durante todo o ciclo de vida das camisetas. Os resultados indicam que as camisetas de PET reciclado apresentam maior eficiência hídrica e menor pegada de carbono em comparação às camisetas de algodão, que se destacam por sua biodegradabilidade. Em contraste, as camisetas de poliamida 6.6 são conhecidas por sua durabilidade e elasticidade superiores. O estudo enfatiza a importância de entender a dinâmica ambiental dentro da cadeia de produção têxtil, contribuindo para a crescente conscientização sobre sustentabilidade no setor. As conclusões extraídas da análise sugerem que as partes interessadas, incluindo fabricantes e consumidores, podem tomar decisões mais informadas sobre seleção de materiais e práticas de produção, resultando em práticas mais sustentáveis no setor têxtil. Os gráficos e análises detalhados apresentados no estudo apoiam essas descobertas, fornecendo uma visão geral abrangente das implicações ambientais de diferentes materiais de camisetas. Esta pesquisa serve como um recurso valioso para promover práticas sustentáveis e orientar a tomada de decisões na indústria têxtil, destacando a necessidade de um equilíbrio entre as preocupações ambientais e o desempenho do produto.

A geração distribuída solar fotovoltaica desponta como elemento central da transição energética no Brasil, impulsionada por políticas públicas, avanços tecnológicos e ampla disponibilidade de recursos solares. Este estudo avalia a eficiência relativa de 20 usinas solares de geração distribuída, com dados operacionais, técnicos e financeiros consolidados para 2023. Aplicou-se o método Data Envelopment Analysis (DEA), modelos CCR (CRS) e BCC (VRS), orientados a output, considerando variáveis de entrada relacionadas a desempenho técnico, recurso energético e custos operacionais, e tendo como variável de saída a receita bruta anual. A abordagem possibilitou identificar benchmarks e usinas com potencial de otimização, evidenciando diferenças significativas de eficiência entre as unidades. Os resultados oferecem subsídios práticos para operadores, investidores e formuladores de políticas públicas, apoiando decisões de investimento e estratégias de gestão. A pesquisa também estabelece bases para análises futuras por clusters regionais, reforçando o papel do DEA como ferramenta para ampliar a competitividade, eficiência e sustentabilidade da geração distribuída no Brasil.

A energia eólica tem se destacado no Brasil, a capacidade instalada em 2023 atingiu o segundo lugar com uma participação de 14% (28 GW) na matriz elétrica. Para que a energia eólica se apresente como uma fonte de energia viável, são necessários estudos que avaliem o balanço energético da cadeia produtiva do energético. Um dos principais métodos para verificar o quão vantajoso é a utilização de um determinado energético é mensurar a quantidade de energia gerada em relação à quantidade de energia empregada durante o processo produtivo. O objetivo do presente trabalho é calcular a eficiência energética do processo de conversão de energia elétrica de um parque eólico teórico localizado no litoral do Nordeste do Brasil com 15 aerogeradores de 2 MW. Como metodologia foi utilizado o EROI para relacionar à quantidade de energia utilizada nos processos de construção do aerogerador e quanto desta energia irá representar no final do processo, ou seja, até a entrega deste energético para a rede. O resultado obtido para o EROI foi de 26. Para cada unidade de energia consumida, serão disponibilizadas 26 unidades para a rede.

This study investigated the integration of computer-aided design (CAD) and additive manufacturing (AM) in prototype production, particularly in the automotive industry. It explores how these technologies redefine prototyping practices, with a focus on design flexibility, material efficiency, and production speed. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, this study encompasses a systematic review of 28 scholarly articles. It undertakes a comprehensive analysis to identify key themes, trends, and gaps in the existing research on CAD and AM integration in automotive prototyping. This study revealed the significant advantages of CAD and AM in prototype manufacturing, including improved design capabilities, efficient material usage, and the creation of complex geometries. It also addresses ongoing challenges, such as technology integration costs, scalability, and sustainability. Furthermore, this study foresees future developments by focusing on enhancing CAD and AM technologies to meet evolving market demands and optimize performance. This study makes a unique contribution to the literature by providing a detailed overview of the integration of CAD and AM in the context of automotive prototyping. This study incorporates valuable insights into the current practices and challenges and future prospects, potentially leading to more advanced, sustainable, and customer-oriented prototyping methods in the automotive sector.

In Brazil, sugarcane ethanol competes directly with gasoline as a fuel for motor vehicles, emerging as a challenging biofuel to traditional fossil fuels. The problem this article solves and presents is the Return on Energy Investment (EROI) for the production cycle of first-generation ethanol derived from sugarcane in the central-southern region of Brazil, with the main objective to compare this EROI with the gasoline marketed in Brazil, as documented in the scientific literature. The methodology for the energy analysis of the ethanol production cycle is the ratio between the energy present in a quantity of sugarcane delivered for processing and the energy consumption required for the entire process. This analysis occurs from the agricultural phase through the distribution phase of ethanol for consumption, enabling the calculation of the EROI of sugarcane ethanol and a comparative assessment with the EROI values of the gasoline marketed in Brazil. The results for EROI of sugarcane ethanol fluctuate between 8.20 and 6.52. Therefore, for each unit of energy utilized in processing ethanol, 6.52 to 8.20 units of energy are available for end use. In contrast, the EROI values for gasoline range between 2.34 and 5.50, underscoring the competitive advantage of ethanol in this context.

Despite presenting several advantages in relation to the mortar coating performed by hand labor traditionally carried out in Brazil, there is little knowledge available about mortar pumping systems. Thus, selecting a mortar pump system is not a trivial procedure, and there are no significative studies published in the specialized literature to fully understand the decision criteria applying to this equipment selection. Therefore, the present study aimed to identify the relevant criteria and quantify their importance in the selection of this equipment, by developing a framework combining both mortar pump specialists expertise and fuzzy analytic hierarchy process method to aid this decision making process. Results showed that acquisition cost and quality are the most relevant criteria, representing togheter around 2/3 of the decision weight, while technical and operational aspects account for aproximatelly 1/10 of relevance in the equipment selection.

The garment industry demands stamping processes that are increasingly more agile and less damaging to the environment. In this scenario, digital printing, with the sublimation transfer printing technique, presents itself as a viable option for synthetic textile substrates. Among the synthetic fibres, polyamide (P.A.) fibres stand out, as they are light, soft, durable, and boast moderate sweat absorption; however, before sublimation, superficial treatment is necessary in order to present good results such as withstanding washing and maintaining colour intensity. This study addresses the surface modification of the PA6.6 textile substrate by activating non-thermal plasma at atmospheric pressure to receive dye through the sublimation method with dispersed dye. The knitted PA6.6 fabric surface treatment was performed with plasma application at atmospheric pressure using air in the Plasmatreater AS400 equipment. The sublimation transfer effects were evaluated by wash fastness and colourimetric tests. To assess the wettability effect of the control and treated samples, a contact angle test was carried out on PA6.6 samples. Fourier transform infrared spectroscopy (FTIR) proved the changes in chemical functional groups in the fibres. The results showed a decrease in the contact angle of the textile surface, 4–5 grayscale results for colour change and transfer for washing, and an increase in colour strength. In the FTIR tests, there is an increase in the transmittance value of aromatic, carboxylic groups (C=O, 580 cm⁻¹), amides (N=H, 1630 cm⁻¹), and methyl groups (CH 1369 to 1463 cm⁻¹) as well as the presence of new functional groups in the 3064 cm⁻¹ and 2860 cm⁻¹ bands. These conditions allowed sublimation in the knitted PA6.6 fabric and showed increased colour strength and good wash fastness.

Qualis indexed Engineering Journal and Science Journal to publish paper with DOI, NAAS Rating and journal has global recognized indexing