Enhancing mechanical properties of 3D-printed PLAs via optimization process and statistical modeling

This paper investigates the optimization of 3D printing by 1.75 mm filaments of poly-lactic acid (PLA) materials. The samples are printed separately and glued together to join the tensile device for the failure load and checking the surface roughness. The printing method in this research is Fused Deposition Modeling (FDM), in which the parameters of Infill Percentage (IP), Extruder Temperature (ET), and Layer Thickness (LT) are considered variable parameters for the 3D printer, and according to the Design of Experiments (DOE), a total of 20 experiments are designed. The parametric range is considered to be 15–55% for IP, 190–250 °C for ET, and 0.15–0.35 mm for LT. The optimization model is conducted according to the Response Surface Method (RSM), in which the ANOVA and plot tables are examined. Moreover, the samples’ maximum failure load, weight, fabrication time, and surface roughness are considered output responses. Statistical modeling shows that by increasing the IP and setting the ET at 220 °C, the failure load of the samples increases, and the maximum failure load reaches 1218 N. The weight and fabrication time of the specimen are optimized at the same time to achieve maximum failure load with less surface roughness. By comparing the predicted and actual output for the optimum samples, the percentage error for all results is less than 5%. The developed optimization method is revealed to be accurate and reliable for FDM 3D printing of PLAs

Optimizing selective laser melting of Inconel 625 superalloy through statistical analysis of surface and volumetric defects

This article delves into optimizing and modeling the input parameters for the selective laser melting (SLM) process on Inconel 625. The primary aim is to investigate the microstructure within the interlayer regions post-process optimization. For this study, 100 layers with a thickness of 40 µm each were produced. Utilizing the design of experiments (DOE) methodology and employing the Response Surface Method (RSM), the SLM process was optimized. Input parameters such as laser power (LP) and hatch distance (HD) were considered, while changes in microhardness and roughness, Ra, were taken as the responses. Sample microstructure and surface alterations were assessed via scanning electron microscopy (SEM) analysis to ascertain how many defects and properties of Inconel 625 can be controlled using DOE. Porosity and lack of fusion, which were due to rapid post-powder melting solidification, prompted detailed analysis of the flaws both on the surfaces of and in terms of the internal aspects of the samples. An understanding of the formation of these imperfections can help refine the process for enhanced integrity and performance of Inconel 625 printed material. Even slight directional changes in the columnar dendrite structures are discernible within the layers. The microstructural characteristics observed in these samples are directly related to the parameters of the SLM process. In this study, the bulk samples achieved a microhardness of 452 HV, with the minimum surface roughness recorded at 9.9 µm. The objective of this research was to use the Response Surface Method (RSM) to optimize the parameters to result in the minimum surface roughness and maximum microhardness of the samples

Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects

Statistical analysis of experimental factors for synthesis of copper oxide and tin oxide for antibacterial applications

This research explores the impact of Cu composition, heating temperature, and milling time on the production of copper-tin alloy nanoparticles. By employing design of experiments techniques, the study systematically evaluates these input variables and their effects on particle size, optical density, and number of colonies. The identification of new Cu3Sn phases in the nanoparticle structure contributes to the novelty of this research. The findings highlight the potential for optimizing copper-tin alloy nanoparticle synthesis and enhancing their antibacterial properties. Mechanical alloying is found to produce nanoparticles up to 15 nm in size. Increasing the percentage of copper leads to improved antibacterial properties. This work provides insights into the synthesis process of copper-tin mechanical alloying and their potential for antibacterial applications

Influence of post-processing CO2 laser cutting and FFF 3D printing parameters on the surface morphology of PLAs: Statistical modelling and RSM optimisation

This study investigates the optimization of 3D printing and CO2 laser cutting parameters by the design of experiments (DOE) and response surface methodology (RSM). In fused filament fabrication (FFF) process, the surface quality of printed curves is poor due to the stairstep defects. The aim is to determine how the laser cutting parameters affect the surface morphology of surface finishing polylactic acid (PLA) samples. A total of 13 cube shape samples are 3D-printed with a cone-shaped hole in the middle. Then, post-processing CO2 laser cutting with a maximum power of 1 kW is used to cut the 3D-printed products. Infill percentage (IP) and extruder temperature (ET) in 3D printing, and laser power (LP), scanning speed (SS), and top edge in CO2 laser cutting are the main parameters in this study. Regression equations are obtained by doing an analysis of the experimental findings using statistical software to examine the impacts of process factors on surface conditions. Results show that the infill percentage and extruder temperature have an extraordinary effect on 3D-printed products' surface quality. 30% infill percentage and 190 °C extruder temperature result in the lowest surface quality with a value of 2.151 μm using DOE and RSM optimization. Also, the lowest value of the top edge is achieved at 275 μm with 300 W laser power and a 5 mm/s cutting speed. The proposed methods can be used to reduce material consumption in the product development