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.

Laser beam machining of various materials has found wide applications in the industry due to its advantages of high-speed machining, no tool wear and no vibration, precision and accuracy, low cost of machining, etc. Investigations into the laser beam machining of uncommon alloy are still limited and more research is needed in this field. In this paper, an analysis of the laser beam machining of tungsten alloy was performed, for cutting and drilling machining processes. First, an experimental analysis of microhardness and microstructure on the laser-cut samples was performed, and then the numerical simulation of the laser beam drilling process and its experimental validation was carried out. The experiments were carried out on a tungsten alloy plate of two different thicknesses, 0.5 and 1 mm. No significant changes in the microhardness, nor in the microstructure characteristics in the heat-affected zone (HAZ), were observed for the cutting conditions considered. A two-dimensional axisymmetric mathematical model for the simulation of the laser beam drilling process is solved by a finite volume method. The model was validated by comparing numerical and experimental results in terms of the size of HAZ and the size and shape of the drilled hole. Experimental and numerical results showed that HAZ is larger in the 0.5-mm-thick plate than in the 1-mm-thick plate under the same drilling conditions. Good agreement was observed between the experimental and numerical results. The developed model improves the understanding of the physical phenomena of laser beam machining and allows the optimization of laser and process parameters.

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.

The research deals with the optimisation of CNC turning process parameters to determine the optimal parametric combination that provides the minimal surface roughness (Ra) and maximal material removal rate. The experiment was conducted by the CNC turning process of S355J2 carbon steel. Data from the Taguchi design of experiments were the subject of analysis with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the present study, three process parameters, such as cutting speed, feed rate and depth of cut, were chosen for the experimentation. It was found that 250 m/min cutting speed, 0.10 mm/rev feed rate and 1.8 mm depth of cut presented the optimal parametric combination by both used multi-objective optimisation methods. Analysis of variance (ANOVA) at a 95 % confidence level was used to determine the most significant parameters. Finally, the accuracy of GRA and TOPSIS results were validated by confirmation experiments.

Experimental Investigation and Modelling of FDM Process Parameters for Tensile Strength Improvement Using RSM Abstract Fused Deposition Modeling (FDM) is one of the most popular additive manufacturing technologies for various engineering applications. The FDM built part is especially anisotropic in nature due to layer-by-layer building mechanism. Therefore, the mechanical properties, especially the tensile strength severaly depend on the process parameters. Hence, the present work focuses on extensive study to understand the effect of four important parameters such as layer thickness, infill density, printing temperature and wall thickness on the tensile strength of test specimens. A total of 30 test specimens were printed using varying processing parameters according Central Composite Design of experiments (CCD) in order to reduce the experimental runs. The RSM method was used to generate a mathematical model, ie an equation (second order polynomial) which describes the process. Experimental results indicate that the wall thickness and infill density have the significant influence on tensile strength, and tensile strength increases with increasing wall thickeness and infill density. Printing temperature and layer thickness have less of an effect on tensile strength. Tensile strength increases with increasing printing temperature and decreases with increasing in layer thickness, especially at lower printing temperature. This paper examines the influence of selected FDM process parameters (layer thickness, infill density, printing temperature and wall thickness) on the tensile strength of the built parts. Design of experiment for doing the experiments makes use of Circumscribed Central Composite Design (CCCD). Empirical relationship between response and different process parameters is established using RSM, and its validity is checked using ANOVA. The developed relationship between tensile strength (output) and process parameters (input) is able to explain the 91.84% of variability in the response. Effect of various factors and their interactions are explained using response surface plots. It shown that the tensile strength is influenced significantly infill density and wall thickness; and less significantly printing temperature and layer thickness. In order to improve the tensile strength of FDM parts made of PLA materials, it is necessary to increase the infill density and wall thickness, decrease the layer thickness, and set the printing temperature in range 200 – 230 °C. The future research is to investigate the effect of all analyzed parameters on tensile strength for different building directions.

This Publication has to be referred as: Cekic, A[hmet]; Muhamedagic, K[enan]; Cohodar, M[aida]; Begic-Hajdarevic, D[erzija] & Biogradlija, S[amra] (2019). Experimental Investigation of Effect of Overhang Tool Length on Tool Vibration and Surface Roughness, Proceedings

This paper presents the use of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the optimum process parameters in plasma arc cutting of stainless steel. Two input process parameters, cutting speed and plasma gas pressure are considered and experiments are conducted based on Taguchi L9 orthogonal array. After performing the experiments, the surface roughness, cut perpendicularity and kerf width are measured. The analysis of variance (ANOVA) are performed in order to identify the effect of each input process parameters on the output responses. The results indicate that TOPSIS method is appropriate for solving multi-criteria optimization of process parameters. Results also showed that cutting speed of 2500 mm/min and plasma gas pressure of 6 bar are the optimum combination of process parameters.