Abstract
This study addresses key methodological gaps in prior T6 heat-treatment research on A356 aluminum alloy, namely the ambiguity of treatment-temperature definitions, reliance on perceived (non-quantitative) porosity assessment, incomplete statistical testing across mechanical responses, and the use of single-factor discrete temperature screening instead of a continuous optimization design. A two-factor Central Composite Design (aging temperature 162-183 °C; aging time 2.3-13.7 h) following a fixed solution treatment (540 °C/6 h, water quench) was employed, comprising 13 experimental runs. Responses (tensile strength, hardness, elongation, and quantitative porosity via image analysis and Archimedes density) were modeled using Response Surface Methodology (RSM) and optimized jointly through a composite desirability function. Quadratic regression models were statistically significant (p 0.85). The findings extend existing T6 optimization literature by providing a validated, continuous, multi-response optimization framework with quantitative defect characterization for A356 components.