Additive manufacturing (AM) or 3D printing technology is one of the preferred methods to ensure sustainability in fabrication. In addition to providing continuity in sustainability, fabrication, and diversity, it aims to improve people’s quality of life, develop the economy, and protect the environment and resources for future generations. In this study, the life cycle assessment (LCA) method was used to determine whether a product fabricated by the AM provides tangible benefits compared to traditional fabrication methodologies. LCA is an evaluation method that provides information on resource efficiency and waste generation, where the environmental impacts of a process can be calculated, measured, and reported throughout the entire life cycle, starting from the acquisition of raw materials, processing, fabrication, use, end of life, and disposal, according to ISO 14040/44 standards. This study examines the environmental impacts of the three most preferred filaments and resin materials in the AM for a 3D-printed product from the start, which consists of three stages. These stages are raw material extraction, manufacturing, and recycling. Filament material types are Acrylonitrile Butadiene Styrene (ABS), Polylactic Acid (PLA), Polyethylene Terephthalate (PETG), and Ultraviolet (UV) Resin. The fabrication process was carried out with Fused Deposition Modeling (FDM) and Stereolithography (SLA) techniques using a 3D printer. Environmental impacts for all identified steps were estimated over the life cycle using the energy consumption model. As a result of the LCA, it was seen that UV Resin was the most environmentally friendly material in the mid-point and end-point indicators. It has been determined that the ABS material also exhibits bad results on many indicators and is the least environmentally friendly. The results support those working with AM in comparing different materials’ environmental impacts and choosing an environmentally friendly material.

Temperature is very important in the fabrication of products developed for different applications, considering the widespread use of additive manufacturing (AM) technology. Thermal properties seriously affect the mechanical properties of products. In this study, the effect of printing nozzle temperature changes on the dimensional and mechanical properties of samples fabricated with acrylonitrile butadiene styrene (ABS) material was investigated. This material can be preferred over foam material for drone and model aircraft areas due to its low density. A total of thirty-six tensile test samples (ISO527–type1A) were fabricated with fused filament fabrication (FFF), one of the AM methods, and their dimensional properties (length, width, thickness, and mass) were determined. During the fabrication process, the nozzle temperature of the 3D (three-dimensional) printer was increased from 220 °C to 270 °C in 10 °C increments. All samples were subjected to tensile testing, and stress–strain values were measured. Analysis of variance (ANOVA) was applied to examine the effect of nozzle temperature change on the findings obtained as a result of the experimental study. The printing nozzle temperature significantly affected both the mechanical strength and dimensional properties of the samples. The samples showed lower viscosity and less hardness at higher nozzle temperatures. The mass and density of the samples decreased with increasing temperature. The tensile strength value decreased by 41.52%.

Additive manufacturing (AM), especially fused deposition modeling (FDM), has been widely used in industrial production processes in recent years. The mechanical properties of parts produced by FDM can be predicted through the correct selection of printing parameters. In this study, 25 machine learning (ML) algorithms were used to predict the mechanical properties (hardness, tensile strength, flexural strength, and surface roughness) of acrylonitrile butadiene styrene (ABS) samples fabricated by FDM. Experiments were conducted using three different layer thicknesses (100, 150, 200 μm), infill densities (50%, 75%, 100%), and nozzle temperatures (220°C, 230°C, 240°C). The effects of printing parameters on mechanical properties were investigated through analysis of variance (ANOVA). This analysis results indicated that infill density had the most significant effect on hardness (55.56%), tensile strength (80.02%), and flexural strength (77.13%). In addition, the layer thickness was identified as the most influential parameter on the surface roughness, with an effect of 70.89%. The prediction performance of the ML algorithms was evaluated based on the mean absolute error (MAE), root mean squared error, mean squared error, and R‐squared (R ²) values. The KSTAR algorithm best predicted both hardness and surface roughness, with MAE values of 0.006 and 0.009, respectively, and an R ² value of up to 0.99. For the prediction of tensile and flexural strength, the MLP algorithm was determined to be the most successful method, achieving high accuracy (R ² > 0.99) for both properties. In addition, comparison graphs between the predicted and actual results showed high overall accuracy, with a particularly strong agreement for hardness, tensile strength, and surface roughness. The study identified the algorithms with the best prediction performance and provided recommendations for predicting the 3D printing process based on these findings.

This study presents the design and fabrication results of an electrothermal micro-electro-mechanical system (MEMS) actuator. Unlike traditional one-directional U-shaped actuators, this bi-directional electrothermal (BET) micro-actuator can produce displacements in two directions as a single device. The BET micro-actuator was fabricated using two-photon polymerization (2PP) and digital light processing (DLP) methods, which are 3D printing techniques. These methods have been compared to see the success of BET micro-actuator fabrication. The compound of these methods and the essential coefficients through the 3D printing operation were applied. Evaluation experiments have demonstrated that in both methods, the 3D printer can print materials smaller than 95.7 μm size features. Though the same design was used for the 2PP and DLP methods, the supporting structures were not produced with the 2PP. The BET micro-actuator was manufactured by removing the supports from the original design in the 2PP. The number of supports, the diameter, and height on the arms of the micro-actuator is 18, 4 μm, and 6 μm, respectively. Although 4 μm diameter supports could be produced with the DLP, it was not possible to produce them with 3D printing device based on 2PP. Besides, the DLP was found to be better than the 2PP for the manufacturing of asymmetrical support structures. The fabrication process has been carried out successfully by two methods. When the fabrication success is compared, the surface quality and fabrication speed of the micro-actuator fabricated with DLP is better than the 2PP. Presented results show the efficiency of the 3D printing technology and the simplicity of fabrication of the micro-actuator via 2PP and DLP. An experimental study was carried out to characterize the relationship between displacement and input voltage for the micro-actuator. Experimental results show that the displacement range of the micro-actuator is 8 μm with DLP, while 6 μm with 2PP.

This study explores the mechanical behavior of polymer and composite specimens fabricated using fused deposition modeling (FDM), focusing on three material configurations: acrylonitrile butadiene styrene (ABS), carbon fiber-reinforced polyphthalamide (PPA/Cf), and a sandwich-structured composite. A systematic experimental plan was developed using the Box–Behnken design (BBD) to investigate the effects of material type (MT), infill pattern (IP), and printing direction (PD) on tensile and flexural strength. Experimental results showed that the PPA/Cf material with a “Cross” IP printed “Flat” yielded the highest mechanical performance, achieving a tensile strength of 75.8 MPa and a flexural strength of 102.3 MPa. In contrast, the lowest values were observed in ABS parts with a “Grid” pattern and “Upright” orientation, recording 37.8 MPa tensile and 49.5 MPa flexural strength. Analysis of variance (ANOVA) results confirmed that all three factors significantly influenced both outputs (p < 0.001), with MT being the most dominant factor. Machine learning (ML) algorithms, Bayesian linear regression (BLR), and Gaussian process regression (GPR) were employed to predict mechanical performance. GPR achieved the best overall accuracy with R² = 0.9935 and MAPE = 11.14% for tensile strength and R² = 0.9925 and MAPE = 12.96% for flexural strength. Comparatively, the traditional BBD yielded slightly lower performance with MAPE = 13.02% and R² = 0.9895 for tensile strength. Validation tests conducted on three unseen configurations clearly demonstrated the generalization capability of the models. Based on actual vs. predicted values, the GPR yielded the lowest average prediction errors, with MAPE values of 0.54% for tensile and 0.45% for flexural strength. In comparison, BLR achieved 0.79% and 0.60%, while BBD showed significantly higher errors at 1.76% and 1.32%, respectively.

Additive manufacturing (AM) is gaining widespread adoption in the manufacturing industry due to its capability to fabricate intricate and high-performance components. In parallel, the increasing emphasis on functional materials in AM has highlighted the critical need for accurate prediction of the mechanical behavior of composite systems. This study experimentally investigates the tensile strength and surface quality of carbon fiber-reinforced nylon composites (PA12-CF) fabricated via fused deposition modeling (FDM) and models their behavior using artificial neural networks (ANNs). A Taguchi L27 orthogonal array was employed to design experiments involving five critical printing parameters: layer thickness (100, 200, and 300 µm), infill pattern (gyroid, honeycomb, and triangles), nozzle temperature (250, 270, and 290 °C), printing speed (50, 100, and 150 mm/s), and infill density (30, 60, and 90%). An analysis of variance (ANOVA) revealed that the infill density had the most significant influence on the resulting tensile strength, contributing 53.47% of the variation, with the strength increasing substantially at higher densities. In contrast, the layer thickness was the dominant factor in determining surface roughness, accounting for 53.84% of the variation, with thinner layers yielding smoother surfaces. Mechanistically, a higher infill density enhances the internal structural integrity of the parts, leading to an improved load-bearing capacity, while thinner layers improve the interlayer adhesion and surface finish. The highest tensile strength achieved was 69.65 MPa, and the lowest surface roughness recorded was 9.18 µm. An ANN model was developed to predict both the tensile strength and surface roughness based on the input parameters. Its performance was compared with that of three other machine learning (ML) algorithms: support vector regression (SVR), random forest regression (RFR), and XGBoost. The ANN model exhibited superior predictive accuracy, with a coefficient of determination (R² > 0.9912) and a mean validation error below 0.41% for both outputs. These findings demonstrate the effectiveness of ANNs in modeling the complex relationships between FDM parameters and composite properties and highlight the significant potential of integrating ML and statistical analysis to optimize the design and manufacturing of high-performance AM fiber-reinforced composites.