Extrusion printing processes allow for manufacturing complex shapes in a relatively cheap way with low-cost machines. The present study analyzes the effect of printing parameters on dimensional error, roughness, and porosity of printed PLA parts obtained with grid structure. Parts are obtained by means of the fused filament fabrication (FFF) process. Four variables are chosen: Layer height, temperature, speed, and flow rate. A two-level full factorial design with a central point is used to define the experimental tests. Dimensional error and porosity are measured with a profile projector, while roughness is measured with a contact roughness meter. Mathematical regression models are found for each response, and multi-objective optimization is carried out by means of the desirability function. Dimensional error and roughness depend mainly on layer height and flow rate, while porosity depends on layer height and printing speed. Multi-objective optimization shows that recommended values for the variables are layer height 0.05 mm, temperature 195 ºC, speed 50 mm/min, and flow rate 0.93, when dimensional error and roughness are to be minimized, and porosity requires a target value of 60%. The present study will help to select appropriate printing parameters for printing porous structures such as those found in prostheses, by means of extrusion processes.

In recent years, metal-filled plastic filaments have begun to be used in fused filament fabrication (FFF) technology. However, the characterization of the parts obtained is still under development. In this work, the results on dimensional accuracy and porosity of copper-filled 3D-printed parts are presented. Cuboid parts were 3D-printed in the vertical position. The three dimensions of each part were measured, and the relative error was calculated for each one of them. Dimensional accuracy in terms of width and depth depends mainly on the layer height and printing temperature, while accuracy in height is mainly influenced by print speed and the interaction of layer height with print speed. Porosity is related to layer height, printing temperature and print speed. According to multiobjective optimization, to minimize dimensional error and obtain a porosity target value of 20%, it is recommended to select a low layer height of 0.1 mm, a high print speed of 40 mm/s, a low extrusion multiplier of 0.94 and a low temperature of 200 °C. The results of the present work will help to select appropriate 3D printing parameters when using metal-filled filaments in FFF processes.

This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min⁻¹. The other variables, density and pressure, would not change significantly from the first situation.

Artificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) processes. In addition, the characteristics of the networks have a direct impact on the performance of the models. In this work, a study about the use of ANN to model surface roughness in FFF processes is presented. The main objective of the paper is discovering how key ANN parameters (specifically, the number of neurons, the training algorithm, and the percentage of training and validation datasets) affect the accuracy of surface roughness predictions. To address this question, 125 3D printing experiments were conducted changing orientation angle, layer height and printing temperature, and measuring average roughness Ra as response. A multilayer perceptron neural network model with backpropagation algorithm was used. The study evaluates the effect of three ANN parameters: (1) number of neurons in the hidden layer (4, 5, 6 or 7), (2) training algorithm (Levenberg–Marquardt, Resilient Backpropagation or Scaled Conjugate Gradient), and (3) data splitting ratios (70%–15%–15% vs. 55%–15%–30%). Mean Absolute Error (MAE) was used as the performance metric. The Resilient Backpropagation algorithm, 7 neurons, and using 55% of training data yielded the best predictive performance, minimizing the MAE. Additionally, the impact of the dataset size on prediction accuracy was analysed. It was observed that the performance of the ANN gets worse as the number of datasets is reduced, emphasizing the importance of having sufficient data. This study will help to select appropriate values for the printing parameters in FFF processes, as well as to define the characteristics of the ANN to be used to model surface roughness.