Selective laser melting, Ti-6Al-4V, Additive manufacturing, Tensile properties,

This dataset contains the experimental data supporting the manuscript "Gaussian Process Regression Prediction of Tensile Properties of Selective Laser Melted Ti-6Al-4V (TC4) Alloy" submitted to Materials (MDPI). DATASET CONTENT:The dataset comprises 129 experimental samples of SLM-fabricated Ti-6Al-4V alloy, establishing process-property relationships for machine learning applications. INPUT FEATURES (Process Parameters):- Laser_Power_W: Laser power in Watts- Scan_Speed_mm_s: Laser scanning speed in mm/s - HT_Temp_C: Heat treatment temperature in degrees Celsius- HT_Time_h: Heat treatment duration in hours- VED_J_mm3: Volumetric Energy Density calculated as P/(v×h×t), J/mm³ TARGET VARIABLES (Mechanical Properties):- UTS_MPa: Ultimate Tensile Strength (MPa), range approximately 800-1200 MPa- Elongation_pct: Elongation at break (%), range approximately 2-15% DATASET CHARACTERISTICS:- Sample size: 129 data points- Data source: Compiled from systematic SLM experiments with standardized Ti-6Al-4V powder- Applicability: Suitable for Gaussian Process Regression, neural networks, and other machine learning models for property prediction and process optimization FILE FORMAT:- SLM_TC4_Dataset.csv: Primary dataset with 129 rows and 8 columns (including Sample_ID)- Missing data: Marked as "NA" or left blank- All values are numerical (continuous variables) SUGGESTED USES:- Training/validation data for surrogate models in additive manufacturing- Correlation analysis between VED and mechanical properties- Uncertainty quantification studies with limited experimental data- Physics-informed machine learning benchmarking LICENSE: CC BY 4.0