In this study, experimental studies on hole quality and machinability in drilling of unreinforced polyamide (PA6) and reinforced polyamide with 30% of glass fibers (PA66-GF30) using cemented carbide (K20) tool have been carried out. The experiments have been planned as per full factorial design of experiments. The effects of spindle speed, feed rate, and point angle on hole quality such as hole diameter and circularity error; the machinability characteristics such as thrust force and specific cutting coefficient have been analyzed by developing response surface methodology based second-order mathematical models. The parametric analysis shows that the quality of holes can be improved by proper selection of cutting parameters. The analysis also indicates the influence of reinforced fiber on proposed machinability characteristics during drilling of polyamides.

Abstract: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.

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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.