Optimization of Process Parameters for Additive Manufacturing of Ti-6Al-4V Alloy Using Machine Learning
Authors: John Smith, Emily Johnson, Michael Brown
Additive manufacturing (AM) of Ti-6Al-4V alloy is widely used in aerospace and biomedical industries due to its excellent mechanical properties and biocompatibility. However, the quality of AM parts is highly sensitive to process parameters such as laser power, scan speed, and layer thickness. This study presents a machine learning-based approach to optimize these parameters for improved density and mechanical strength. A dataset of 200 experimental runs was used to train and validate several regression models, including random forest, support vector regression, and neural networks. The random forest model achieved the highest prediction accuracy with an RΒ² of 0.95. Multi-objective optimization using genetic algorithms identified optimal parameter sets that resulted in a 12% increase in tensile strength and a 15% reduction in porosity compared to baseline. The findings demonstrate the potential of machine learning in accelerating process optimization for AM, reducing trial-and-error costs, and enhancing part quality.
