Pedro Guilherme Carvalho de Souza Marconi

Additive Manufacturing (AM) continues to grow rapidly worldwide through its different development research lines, achieving greater maturity in industrial and new potential multidisciplinary applications. One of the key advancements in extrusion-based AM is the design of screw-assisted 3D equipment and operation development, which provide a promising alternative to traditional filament-feeding 3D printers, particularly for polymer composite materials. Such equipment has enabled the expansion of experimentation engineering materials based on granulated or pelletized feedstock by lower energy consumption aligned with sustainability requirements. Monitoring and controlling the temperature profile during the extrusion process, especially in extrusion process screw-based systems, are highly strategic for ensuring phase transformation regions occur assertively, assisting in adjusting the material residence time and preventing material plasticization at the beginning of feeding and possible material degradation in the plasticization regions. This manuscript proposes a numerical-experimental model that estimates the thermal profile of customized and modular equipment for extrusion-based AM containing a miniaturized screw-assisted 3D printhead unit. An in-line passive thermal monitoring system was implemented and preliminary results indicated an approximately 98.2% numerical-experimental agreement with mean absolute percentage errors (MAPE) of 2.55% (surface) and 0.96% (interior) at 200 °C and 2.52% (surface) and 1.30% (interior) at 215 °C. The mean absolute errors (MAE) were 2.69 °C/1.26 °C and 2.95 °C/1.61 °C for surface/interior under 200 °C and 215 °C, respectively. According to the findings, both proposed procedure and numerical predictive model can capture the main thermal gradients along the screw-assisted modular printhead with satisfactory accuracy.

A automação de processos industriais tem se tornado um elemento essencial para a otimização da produção, promovendo maior eficiência e precisão nos sistemas envolvidos. Neste contexto, o presente artigo tem como objetivo apresentar o desenvolvimento de uma bancada de processos industriais automatizada, permitindo a integração de conhecimentos multidisciplinares nas rotinas de controle e nos processos de termodinâmica e mecânica dos fluidos. A metodologia adotada envolve o desenvolvimento de um sistema que possibilita a experimentação e o aperfeiçoamento de técnicas aplicadas à automação, partindo do levantamento e análise dos requisitos técnicos para a construção da bancada. Foram utilizados processos de fabricação mecânica, sensores e ferramentas de modelagem e simulação para planejar o sistema de controle e os processos envolvidos. A bancada foi validada por meio de testes práticos, verificando a eficácia na automação dos processos e no desempenho dos sistemas de controle. Os resultados obtidos revelam que a estrutura da bancada permite a aplicação de conceitos de termodinâmica e mecânica dos fluidos, possibilitando testes e ajustes em tempo real, além de facilitar futuras expansões e adaptações conforme as necessidades de estudo e pesquisa. A experiência prática adquirida na construção e utilização desse sistema proporcionou aos participantes uma compreensão mais aprofundada dos desafios e soluções na automação de processos industriais, fortalecendo a visão multidisciplinar e tornando o aprendizado mais dinâmico e aplicável às demandas da indústria.

In order to manufacture plastic parts with complex geometries on a large scale, injection molding is one of the most widely used manufacturing processes. One of the criteria commonly considered when developing new products is dimensional quality, a factor directly associated with reducing part warpage. In the automotive industry, the manufacture of parts requires high dimensional precision, as many components have an aesthetic purpose or require perfect fits. Therefore, this work employs experimental designs, numerical simulations, statistical methods, and machine learning models to predict the warpage of an automotive cup holder. The polymers used were polypropylene (PP) and acrylonitrile butadiene styrene (ABS). The results indicated that the regression models developed for predicting warpage performed better with the ABS data. Regarding the classification models, both achieved an accuracy rate exceeding 90%. These findings provide useful tools during the mold tryout phase, helping to reduce time, cost, and material waste.

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In order to manufacture plastic parts with complex geometries on a large scale, injection molding is one of the most widely used manufacturing processes. One of the criteria commonly considered when developing new products is dimensional quality, a factor directly associated with reducing part warpage. In the automotive industry, the manufacture of parts requires high dimensional precision, as many components have an aesthetic purpose or require perfect fits. Therefore, this work employs experimental designs, numerical simulations, statistical methods, and machine learning models to predict the warpage of an automotive cup holder. The polymers used were polypropylene (PP) and acrylonitrile butadiene styrene (ABS). The results indicated that the regression models developed for predicting warpage performed better with the ABS data. Regarding the classification models, both achieved an accuracy rate exceeding 90%. These findings provide useful tools during the mold tryout phase, helping to reduce time, cost, and material waste.

The thermoplastic injection moulding process is very important in the plastics industry, as it enables automated production, supports high productivity and allows the production of plastic parts with complex geometries. It is possible to split in two large groups of polymers: amorphous and semicrystalline. Cooling rate and other injection moulding parameters have great influence over the final properties of the plastic part. Regarding the use of aluminium as cavity material in injection moulds, new variables must be included in the analysis, since its thermal properties are significantly different from those presented by steels, which are traditionally used. In this way, the purpose of this study was to evaluate the effect of aluminium and steel cavities on different types of thermoplastics belonging to the two classes of polymers by assessing the injection parameters of a high-production part (automotive cup holder). In terms of productivity factors, moulds made of aluminium using semicrystalline polymers showed more significant reductions in cycle time compared to amorphous materials. Specifically, polypropylene exhibited a cycle time reduction between 40.6% and 52.5% when compared to steel moulds, while polyamide showed an even more substantial reduction, ranging between 56% and 63.5%. As for warpage, the amorphous materials displayed the lowest values for both types of moulds, but they also exhibited greater variations in isothermal simulations compared to semicrystalline materials. In relation to the mould materials, aluminium mould exhibited the lowest warping results and smaller variations compared to the isothermal analyses for all polymers.