"Experimental Dataset on Design and Performance Parameters of FDM 3D Printed Parts Using Line and Grid Infill Patterns"

"Current dataset presents a comprehensive experimental collection focused on the design and performance optimization of 3D printed parts manufactured using Fused Deposition Modeling (FDM). The data was generated through a series of controlled experiments where two commonly used infill patterns\u2014line and grid\u2014were applied across a wide range of process parameters. For each infill type, more than 250 samples were fabricated and analyzed, providing a dataset of significant size and utility for researchers working in additive manufacturing, design optimization, and machine learning\u2013based predictive modeling. The current dataset captures four critical input process parameters: nozzle temperature (\u00b0C), printing speed (mm\/s), layer height (mm), and infill density (%). These parameters were systematically varied to examine their influence on print quality, structural integrity, and time efficiency. Along with the input conditions, the dataset records a key dimensional deviation index (DD) that reflects accuracy, and a detailed breakdown of process-specific time distribution, including inner wall filling time, outer wall filling time, infill time, travel time, and other auxiliary times. This combination of parameters provides both dimensional accuracy indicators and insights into the energy\u2013time distribution during printing, offering a holistic understanding of FDM performance.The dual datasets\u2014line infill and grid infill\u2014allow for comparative studies between two widely adopted structural patterns in 3D printing. The inclusion of more than 500 total samples enhances the statistical robustness, making it suitable for advanced data-driven approaches such as regression modeling, optimization algorithms, and machine learning predictions. Researchers can leverage this dataset to identify optimal parameter windows for accuracy, strength, and time efficiency, as well as to develop intelligent models for automated process parameter tuning. By providing both raw and processed values, this dataset serves as a benchmark resource for the academic and industrial communities working on additive manufacturing. It enables exploration of trade-offs between dimensional precision, productivity, and material usage in FDM-based 3D printing, ultimately contributing toward sustainable and high-performance manufacturing solutions."