Fused deposition modelling (FDM) is one of the mostly used additive technologies, due to its ability to produce complex parts with good mechanical properties. The selection of FDM process parameters is crucial to achieve good mechanical properties of the manufactured parts. Therefore, in this paper, a hybrid multi-criteria decision-making (MCDM) approach based on Preference Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed for the selection of optimal process parameters in FDM printing of polylactic acid (PLA) parts. Printing temperature, layer thickness and raster angle were considered as input process parameters. In order to prove the effectiveness of the proposed hybrid PSI – TOPSIS method, the obtained results were compared with the results obtained with different MCDM methods. The obtained best option of process parameters was confirmed by other MCDM methods. The optimal combination of process parameters to achieve the maximal flexural strength, maximal flexural modulus and maximal compressive strength is selected using the hybrid PSI-TOPSIS method. The results show that the hybrid PSI-TOPSIS approach could be used for optimisation process parameters for any machining process.

The quality management system at the University of Sarajevo is guided by the following principles: promotion of constant improvement of the quality of the study programmes and the application of the best educational approaches and practices. The Bologna process gives special importance to outcome based education (OBE), so learning outcomes are established as a new measure of institutional excellence, shifting a change in focus from "What should student do to successfully complete her/his studies?" to "What competences student acquired during her/his studies?" or more precisely "What can student do after finishing her/his studies?". In this paper, after basic information about outcome based education and the importance of this approach with the implementation of the Bologna process in the European Higher Education Area (EHEA), an overview of the approach to quality assurance and to study programmes improvements based on learning outcomes is given. Focus on learning outcomes enables the fundamental change of the focus from the teacher to the student, which is one of the foundations of the Standards and Guidelines for Quality Assurance in the EHEA (ESG).

Reinforcing the polymer with nanoparticles and fibers improves the mechanical, thermal and electrical properties. Owing to this, the functional parts produced by the FDM process of such materials can be used in industrial applications. However, optimal parameters’ selection is crucial to produce parts with optimal properties, such as mechanical strength. This paper focuses on the analysis of influential process parameters on the tensile strength of FDM printed parts. Two statistical methods, RSM and ANN, were applied to investigate the effect the layer thickness, printing speed, raster angle and wall thickness on the tensile strength of test specimens printed with a short carbon fiber reinforced polyamide composite. The reduced cubic model was developed by the RSM method, and the correlation between the input parameters and the output response was analyzed by ANOVA. The results show that the layer thickness and raster angle have the most significant influence on tensile strength. As for machine learning, among the nine different tested ANN topologies, the best configuration was found based on the lowest MAE and MSE test sample result. The results show that the proposed model could be a useful tool for predicting tensile strength. Its main advantage is the reduction in time needed for experiments with the LOSO (leave one subject out) k-fold cross validation scheme, offering better generalization ability, given the small set of learning examples.

Microneedles (MNs) represent the concept of attractive, minimally invasive puncture devices of micron-sized dimensions that penetrate the skin painlessly and thus facilitate the transdermal administration of a wide range of active substances. MNs have been manufactured by a variety of production technologies, from a range of materials, but most of these manufacturing methods are time-consuming and expensive for screening new designs and making any modifications. Additive manufacturing (AM) has become one of the most revolutionary tools in the pharmaceutical field, with its unique ability to manufacture personalized dosage forms and patient-specific medical devices such as MNs. This review aims to summarize various 3D printing technologies that can produce MNs from digital models in a single step, including a survey on their benefits and drawbacks. In addition, this paper highlights current research in the field of 3D printed MN-assisted transdermal drug delivery systems and analyzes parameters affecting the mechanical properties of 3D printed MNs. The current regulatory framework associated with 3D printed MNs as well as different methods for the analysis and evaluation of 3D printed MN properties are outlined.

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

This paper investigates the influence of the fuel injector nozzle geometry on the liquid fuel contraction coefficient and Reynolds number. The main three fuel injector nozzle geometry parameters: nozzle diameter (d), nozzle length (l) and nozzle inlet radius (r) have a strong influence on the liquid fuel contraction coefficient and Reynolds number. The variation of the nozzle geometry variables at different liquid fuel pressures, temperatures and injection rates was analyzed. The liquid fuel contraction coefficient and Reynolds number increase with an increase in the nozzle diameter, regardless of the fuel injection rate. An increase in the r/d ratio causes an increase in the fuel contraction coefficient, but the increase is not significant after r/d = 0.1. A nozzle length increase causes a decrease in the fuel contraction coefficient. Increase in the nozzle length of 0.5 mm causes an approximately similar decrease in the contraction coefficient at any fuel pressure and any nozzle length. Fuel injectors should operate with minimal possible nozzle lengths in order to obtain higher fuel contraction coefficients.