Fused filament fabrication (FFF) is a promising additive manufacturing (AM) technology due to its ability to build thermoplastics parts with advantages in the design and optimization of models with complex geometries, great design flexibility, recyclability and low material waste. This technique has been extensively used for the manufacturing of conceptual prototypes rather than functional components due to the limited mechanical properties of pure thermoplastics parts. In order to improve the mechanical performance of 3D printed parts based on polymeric materials, reinforcements including nanoparticles, short or continuous fibers and other additives have been adopted. The addition of graphene nanoplatelets (GNPs) to plastic and polymers is currently under investigation as a promising method to improve their working conditions due to the good mechanical, electrical and thermal performance exhibited by graphene. Although research shows particularly promising improvement in thermal and electrical conductivities of graphene-based nanocomposites, the aim of this study is to evaluate the effect of graphene nanoplatelet reinforcement on the mechanical properties, dimensional accuracy and surface texture of 3D printed polylactic acid (PLA) structures manufactured by a desktop 3D printer. The effect of build orientation was also analyzed. Scanning Electron Microscope (SEM) images of failure samples were evaluated to determine the effects of process parameters on failure modes. It was observed that PLA-Graphene composite samples showed, in general terms, the best performance in terms of tensile and flexural stress, particularly in the case of upright orientation (about 1.5 and 1.7 times higher than PLA and PLA 3D850 samples, respectively). In addition, PLA-Graphene composite samples showed the highest interlaminar shear strength (about 1.2 times higher than PLA and PLA 3D850 samples). However, the addition of GNPs tended to reduce the impact strength of the PLA-Graphene composite samples (PLA and PLA 3D850 samples exhibited an impact strength about 1.2–1.3 times higher than PLA-Graphene composites). Furthermore, the addition of graphene nanoplatelets did not affect, in general terms, the dimensional accuracy of the PLA-Graphene composite specimens. In addition, PLA-Graphene composite samples showed, in overall terms, the best performance in terms of surface texture, particularly when parts were printed in flat and on-edge orientations. The promising results in the present study prove the feasibility of 3D printed PLA-graphene composites for potential use in different applications such as biomedical engineering.

The evolution of fused filament fabrication (FFF) technology, initially restricted to the manufacturing of prototypes, has led to its application in the manufacture of finished functional products with excellent mechanical properties. However, FFF technology entails drawbacks in aspects, such as dimensional and geometric precision, and surface finish. These aspects are crucial for the assembly and service life of functional parts, with geometric qualities lagging far behind the optimum levels obtained by conventional manufacturing processes. A further shortcoming is the proliferation of low cost FFF 3D printers with low quality mechanical components, and malfunctions that have a critical impact on the quality of finished products. FFF product quality is directly influenced by printer settings, material properties in terms of cured layers, and the functional mechanical efficiency of the 3D printer. This paper analyzes the effect of the build orientation (Bo), layer thickness (Lt), feed rate (Fr) parameters, and plate-extruder movements on the dimensional accuracy, flatness error, and surface texture of polylactic acid (PLA) using a low cost open-source FFF 3D printer. The mathematical modelling of geometric properties was performed using artificial neural networks (ANN). The results showed that thinner layer thickness generated lower dimensional deviations, and feed rate had a minor influence on dimensional accuracy. The flatness error and surface texture showed a quasi-linear behavior correlated to layer thickness and feed rate, with alterations produced by 3D printer malfunctions. The mathematical models provide a comprehensive analysis of the geometric behavior of PLA processing by FFF, in order to identify optimum print settings for the processing of functional components.

In this work, the effect of short carbon fibre (CF) on the mechanical and geometric properties of 3D printed polylactic acid (PLA) composite parts processed using the Fused Filament Fabrication (FFF) technique have been analysed. Tensile, flexural and interlaminar shear strength (ILSS) tests were performed to obtain the mechanical performance of the different samples. The surface quality and geometric accuracy of the printed specimens were also evaluated. Finally, Scanning Electron Microscope (SEM) images of the printed samples are analysed. The results revealed that the addition of carbon fibres effectively improved all assessed mechanical properties of PLA-CF composites as compared to the neat PLA. In particular, Flat PLA-CF samples showed an average increase in tensile performance of 47.1% for the tensile strength and 179.9% for the tensile stiffness in comparison to the neat PLA. From the flexural behaviour point of view, Flat PLA-CF samples revealed an increase in average flexural strength and stiffness of 89.75% and 230.95%, respectively in comparison to the neat PLA. Furthermore, PLA-CF samples depicted the best ILSS performance. In general, the use of short carbon fibre as reinforcement did not affect the dimensional accuracy of the PLA-CF samples, and even improved the surface roughness in certain cases, particularly in Flat and On-edge orientations.

Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

Electropolishing (EP) processes require high electrical consumption that must be optimized to minimize production costs without sacrificing the objectives of surface quality and dimensional accuracy. The aim of the present paper was to analyze the effects of the interelectrode gap, initial surface roughness, electrolyte temperature, current density, and EP time on aspects of the EP process applied to AISI 316L stainless steel, which have not been examined in the literature, such as polishing rate, final surface roughness, dimensional accuracy, and electrical consumption cost. In addition, the paper aimed to obtain optimum individual and multi-objective considering criteria of surface quality, dimensional accuracy, and electrical consumption cost. The results showed that the electrode gap was not significant on the surface finish or current density, and the EP time was the parameter having the greatest effect on all criteria analyzed, with a temperature of 35 °C showing the best electrolyte performance. The initial surface texture with the lowest roughness ${Ra}_1^0$ (0.5 ≤ Ra ≤ 0.8 μm) obtained the best results with a maximum polishing rate of ~90% and minimum final roughness (Ra) of ~0.035 μm. The response surface methodology showed the EP parameter effects and the optimum individual objective. The desirability function obtained the best global multi-objective optimum, while the overlapping contour plot provided optimum individual and simultaneous per polishing range.

The primary aim of this study was to design and develop an on-line control system of finished surfaces in automated machining process by CNC turning. The control system consisted of two basic phases: during the first phase, surface roughness was monitored through cutting force signals; the second phase involved a closed-loop adaptive control system based on data obtained during the monitoring of the cutting process. The system ensures that surfaces roughness is maintained at optimum values by adjusting the feed rate through communication with the PLC of the CNC machine. A monitoring and adaptive control system has been developed that enables the real-time monitoring of surface roughness during CNC turning operations. The system detects and prevents faults in automated turning processes, and applies corrective measures during the cutting process that raise quality and reliability reducing the need for quality control.