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Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion.

This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing.

Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors.

There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high.

Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.

3D printing is gaining attention in the medical sector for the development of customized solutions for a wide range of applications such as temporary external implants. The materials used for the manufacturing process are critical, as they must provide biocompatibility and adequate mechanical properties. This study aims to evaluate and model the influence of the printing parameters on the mechanical properties of two biocompatible materials.

In this study, the mechanical properties of 3D-printed specimens of two biocompatible materials (ABS medical and PLActive) were evaluated. The influence of several printing parameters (infill density, raster angle and layer height) was studied and modelled on three response variables: ultimate tensile strength, deformation at the ultimate tensile strength and Young’s modulus. Therefore, statistical models were developed to predict the mechanical responses based on the selected printing parameters.

The used methodology allowed obtaining compact models that show good fit, particularly, for both the ultimate tensile strength and Young’s modulus. Regarding the deformation at ultimate tensile strength, this output was found to be influenced by more factors and interactions, resulting in a slightly less precise model. In addition, the influence of the printing parameters was discussed in the work.

The presented paper proposed the use of statistical models to select the printing parameters (infill density, raster angle and layer height) to optimize the mechanical response of external medical aids. The models will help users, researchers and firms to develop optimized solutions that can reduce material costs and printing time but guaranteeing the mechanical response of the parts.

The purpose of this paper is to provide a practical review of the use of the minimum quantity lubrication (MQL) system in turning operations, focussing on the application of the technique in the turning of different kind of materials.

The use of the MQL system was analysed by several researchers in the past years. Thus, in the present paper, a relevant sample of the main experimental studies that can be found in the literature was analysed to come up with a review with relevant information for researchers and industry.

The use of the MQL system can help to improve the outcomes of the turning process in several issues like surface quality or tool life. However, it was also recognised that in some cases, other cooling/lubricating methods can provide better results than the MQL system. Thus, the decision, whether to use or not the MQL system in a specific process, is of great importance.

The work is conveniently focussed to serve as a quick reference on the issue. At the same time, the work analysed the use of the turning of some of the main engineering materials that makes it useful for a wider range of researchers and metalworking firms. Finally, the review could be useful to improve the performance of the industry, especially for the metalworking firms in terms of costs, environmental impact and safety.