In the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks’ inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2^k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models’ predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity.

In recent years, the rapid advancement of Additive Manufacturing (AM) technology has led to resource-efficient processes and reduced gaps between design and product/prototypes. Particularly, sectors like medicine and dentistry stand to gain from these advancements if materials with suitable mechanical characteristics for clinical use are available. One such evolving AM process is VAT photopolymerization through LCD 3D Printer technology, characterized by the curing of photosensitive polymeric resins. This study aims to assess the impact of the angle of layer photopolymerization of UV resin in three positions (horizontal, vertical, and inclined at 45°) using a cost-effective LCD 3D Printer. Additionally, specimens, distributed across five positions on the printing platform, underwent testing with three types of UV light-curing resin. Mechanical properties, including tensile stress, elasticity modulus, and hardness, were evaluated and analyzed statistically. Results indicate that both the chosen manufacturing platform position and the inclined layer angle significantly influence the final characteristics of the workpiece.

Due to the recent innovations in 3D-printed polymers research, this study presents a systematic overview of the area, exposing gaps and interesting directions for future research. The current study investigated the trend of research growth using 2558 research papers with 97131 references data collected from the Web of Science Core Collection database (WOS), over the period from 2005 to May 2022, using bibliographic coupling and keyword co-occurrence. The research results allow the authors to conclude that the number of publications in this field's importance has grown tremendously over the last 20 years, with the United States, China, United Kingdom, and India emerging as the countries that publish the most. The top five researchers in 3D printed polymer composite were also identified.

In Brazil, along its history, the Technical Professional Education in High School level (TPEHS) has evolved into various proposals and, currently, the technical professional capacitation is amply disseminated as High School Technical Courses. In this educative level some authors believe that the learning divergence and limitations brought from the Elementary School interferes directly on the school success. However, what is observed is that from the beginning of the course, an educational homogeneity among the students is taken for granted, which is not true. Motivated by this, various authors have proposed the use of methodological instruments destined to obtaining information about the students’ learning gap in previous years over the contents assumed as pre-requirements for the course subjects, highlighting among these methodologies the Diagnostic Evaluation (DE). In this paper we present a Systematic Literature Review (SLR) related to the Diagnostic Evaluation in the TPEHS, specifically on the entrant students. Thus, a SLR methodology was developed and is described in this investigation. The results of the SLR final selection are described in a summarized way highlighting the main methodologies and contributions. We concluded that the proposals presented in the papers aim at contributing to reduce the failure rate and evasion from TPEHS and, furthermore, contribute to divulge the theme and open opportunities for further and stronger developments and interventions in the TPEHS.

This study employs a bibliometric approach to analyze the evolution of highly cited research in machining of reinforced plastics from 1986 to June 2021. The research examines various document formats, such as journal articles, reviews, chapters, and books. Focusing on reinforced plastics and composites, the study evaluates prolific contributors, temporal trends, and citation metrics to assess scholarly prominence. The research uses a curated dataset of 161 sources from the Web of Science Core Collection database, aiming to identify emerging research directions and gaps in the field. The study uncovers quantitative dimensions and interrelationships among research domains, providing insights into scholarly development. The data categorizes document types, indicating that articles form the majority (85.005%) of the dataset, followed by proceeding papers (16.387%) and review articles (2.536%). The dataset comprises various other types, with each representing a smaller portion. The study's empirical foundation is drawn from the Web of Science Core Collection database.

Fiber-reinforced polymer (FRP) matrices have gained widespread traction across diverse industries for their exceptional attributes and lightweight nature, outperforming traditional materials and alternative composites. Consequently, the study of machining these composite matrices has gained significant attention. This review aims to establish robust physical and statistical models, opening avenues for optimizing the machining of polymer matrix composites. Encompassing various machining aspects, including crucial variables and criteria, the synthesis notably focuses on refining surface finish and cutting force dynamics. Curated from acclaimed scientific literature, this review provides an exhaustive overview of the prevailing landscape and intricate nuances inherent to FRP machining. The gleaned insights are poised to steer future empirical endeavors and refine precision cutting models.