As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters.

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.