This study investigates the prediction of fused deposition modeling (FDM) process parameters for manufacturing biomedical implants with high dimensional accuracy and surface quality. Biomedical implants were fabricated in circular, triangular, and pentagonal geometries to accommodate different anatomical requirements, using three materials selected for their biomedical applicability and mechanical properties. These materials are polylactic acid (PLA), polyethylene terephthalate glycol, and thermoplastic polyurethane (TPU). This research utilizes the Taguchi L27 orthogonal array methodology to analyze the influence of five critical printing parameters: material type, layer thickness (200–300–400 µm), infill density (30%–60%–90%), infill pattern (zigzag, cubic, and triangle), and wall thickness (1–2–3 mm). The analysis of variance demonstrated that material type and layer thickness are the most significant factors, contributing 49.25% and 17.97%, respectively, to dimensional accuracy in circular geometries. Surface roughness measurements showed that layer thickness (30.95%) and material type (31.28%) are dominant factors affecting surface quality. The optimum parameters for dimensional accuracy were determined as PLA material, zigzag infill pattern, 2 mm wall thickness, 30% infill density and 200 µm layer thickness, while the highest surface quality was achieved with PLA material, triangle infill pattern, 3 mm wall thickness, 90% infill density and 200 µm layer thickness. An artificial neural network model was developed to predict dimensional accuracy and surface quality, achieving high correlation coefficients (R² > 0.96) between predicted and experimental results across all geometric configurations. These findings offer valuable guidelines for predicting and optimizing parameters in FDM-based biomedical implant manufacturing, advancing precision medicine by enhancing additive manufacturing processes and implant performance.

Additive manufacturing (AM), particularly fused deposition modeling (FDM), has emerged as a transformative technique for producing customized, lightweight, and environmentally sustainable components. However, identifying optimal process parameters that balance mechanical performance with environmental impact remains a critical challenge. In this study, a comprehensive experimental and data-driven approach is employed to investigate the influence of key FDM parameters—infill density (ID) (30%, 60%, 90%), layer thickness (LT) (200, 300, 400 µm), printing speed (PS) (50, 100, 150 mm/s), and infill pattern (IP) (Cubic, Triangles, Concentric)—on the mechanical and sustainability-related performance of acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and carbon fiber-reinforced polylactic acid (Cf/PLA) specimens. A Taguchi L9 orthogonal array was used to design the experiments, and the resulting samples were subjected to tensile testing, surface roughness measurement, energy consumption monitoring, and carbon emission (CO ₂ ) estimation. The maximum tensile strength was observed in Cf/PLA samples (up to 53.31 MPa), while PLA samples displayed a strong balance between surface quality (Ra ≈ 8.5–9.1 µm) and mechanical integrity. Energy consumption and carbon emission data revealed a direct correlation with ID and material type, with Cf/PLA exhibiting the highest environmental footprint. ANOVA revealed that ID was the most statistically significant factor influencing all target properties, contributing more than 60% to the total variance in experimental results across all material types. Scanning electron microscopy (SEM) analyses further illustrated microstructural differences and layer adhesion quality across different process settings. To enhance predictive capability and reduce experimental effort, three machine learning (ML) models—artificial neural networks (ANN), random forest regression (RFR), and gaussian process regression (GPR)—were developed to forecast mechanical and environmental outputs. The ANN yielded the highest accuracy across all metrics, with R ² > 0.995 and MAPE < 10.5%. Validation test results confirmed the generalizability of the models, showing that the average prediction error remained below 2% across all ML algorithms. These findings demonstrate the effectiveness of integrating Taguchi-based optimization and AI-driven prediction for achieving sustainable, high-performance FDM manufacturing, providing a robust framework for future design and process optimization in AM applications.

This study compared Box‐Behnken Design (BBD), Definitive Screening Design (DSD), and Taguchi design methods to predict and optimize surface hardness in polyethylene terephthalate glycol (PETG) parts fabricated by fused deposition modeling (FDM). Critical printing factors including layer thickness (LT), infill density (ID), nozzle temperature (NT), and printing speed (PS) were analyzed to develop accurate prediction models while minimizing experimental runs. The comparative analysis revealed that all three methods provided reliable hardness predictions, with BBD showing superior accuracy (3.74% error), followed by DSD (4.25%) and Taguchi (4.84%). ID emerged as the most influential factor on surface hardness across all methods. BBD required 27 experimental runs, while DSD and Taguchi needed only 13 and 9 runs, respectively, demonstrating significant efficiency in experimental design. The optimal parameter combinations were validated through confirmation tests, achieving maximum hardness values of 62.47 Shore D (Taguchi), 57.16 Shore D (BBD), and 59.95 Shore D (DSD). These findings provide practical guidelines for industrial applications, enabling manufacturers to select the most suitable design method based on their specific requirements for accuracy versus experimental efficiency in PETG part production.

The Ni-W/PTFE co-depositions were successfully prepared on steel via pulse electrodeposition methods. Electrodeposition was perform by dispersing 5-20 g/L PTFE particles from Ni-Watt bath. The surface morphology, phase analyses crstalytallite size, distorsion and hardness of the samples were characterized by SEM, XRD, and Vicker?s microhardness tester. The friction coefficient of the coatings were carried out using CSM microtester. As increasing with the concentration of the PTFE in the solution, the wear resistance properties of theco-depositions were increased. These experimental results determined that the PTFE concentration of 15 g/L in the electrolyte was the optimum content to obtain the best micro-structure and wear performance.