Parametric Investigation of Process Variables in Metal Additive Manufacturing for Optimized Mechanical Strength

Abstract

Metal additive manufacturing has emerged as a transformative fabrication technology in aerospace, automotive, biomedical, and industrial engineering applications due to its ability to produce complex geometries with reduced material wastage and enhanced design flexibility. However, the mechanical performance of additively manufactured metal components is highly influenced by process variables such as laser power, scanning speed, layer thickness, build orientation, and powder feed characteristics. Improper selection of these parameters can lead to defects including porosity, residual stress, anisotropy, and reduced structural strength. This research presents a parametric investigation of process variables in metal additive manufacturing for optimized mechanical strength and improved structural performance. The proposed study evaluates the influence of critical manufacturing parameters on the microstructural characteristics, density distribution, hardness, tensile strength, and fatigue behavior of metal components produced using additive manufacturing techniques. Experimental analysis and numerical modeling approaches are employed to investigate parameter interactions and identify optimal processing conditions for enhanced mechanical integrity. Statistical optimization techniques and response surface methodologies are utilized to analyze the relationship between process variables and material performance characteristics. Performance evaluation is conducted using parameters such as ultimate tensile strength, yield strength, hardness, porosity level, surface quality, residual stress distribution, and fatigue resistance. Comparative analysis demonstrates that optimized process parameter combinations significantly improve bonding quality, reduce manufacturing defects, and enhance the mechanical reliability of additively manufactured metal structures.