###### Abstract  
Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters--layer thickness, infill density, and nominal diameter--on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R\({}^{2}\) values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality. FDM 3D printing; dimensional accuracy; splined shafts and hubs; artificial neural networks; genetic algorithms +
Footnote †: journal: Applied sciences  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0  
This additive manufacturing method enables the rapid production of customized splined components with intricate geometries that are difficult to achieve through conventional machining processes. Additionally, 3D printing reduces material waste, production time, and costs associated with small-batch or prototype manufacturing. The ability to optimize printing parameters, such as layer thickness, infill density, and orientation, further enhances the dimensional accuracy and mechanical properties of the printed parts, contributing to the overall quality and performance of splined assemblies. Additive manufacturing (AM) technology has gained widespread adoption in recent decades due to its flexibility and ability to produce complex geometries with minimal material consumption. Among the various AM techniques, fused-deposition modeling (FDM) is particularly popular for its cost-effectiveness and versatility in processing different types of polymeric materials [1]. However, despite its advantages, achieving perfect dimensional accuracy remains a challenge in FDM. The process is inherently influenced by factors such as shrinkage, layer-by-layer deposition, and thermal effects during printing, leading to dimensional deviations in manufactured parts. Critical process parameters, including layer thickness, infill density, nozzle temperature, and printing speed, directly impact the final quality and precision of the printed components [2]. Ensuring high-quality production in FDM requires systematic validation and control of dimensional accuracy. The use of appropriate measurement techniques enables precise assessment of product quality and dimensional stability, allowing for better control over manufacturing parameters and minimizing variations. However, post-processing adjustments to correct dimensional inaccuracies are often difficult due to inherent distortions and shrinkage in FDM-printed parts. As the adoption of this technology expands, industries are increasingly prioritizing product quality and demanding stricter performance standards. Consequently, maintaining dimensional variations within tight tolerance limits is crucial for ensuring product reliability and reducing rejection rates in manufacturing [3].