Quantum-Driven Multi-Objective Optimization for Defect Minimization in Friction Stir Additive Manufacturing

Friction-stir additive manufacturing (FSAM) of aluminium alloys allows the creation of solid-state builds but is faced with the challenges of heat accumulation in between each pass of the tool, and residual stress. With a total of 36 builds with rotation speeds of tool as the independent variable varied from 400 – 1200 rpm, traverse speeds from 50 – 300 mm/min, axial force of the tool torque remained between 4 – 12 kN, and four cooling strategies were implemented. The results showed water-mist cooling increased the ultimate tensile strength (UTS) to 429 MPa, and decreased residual stress by 18% when compared to builds without cooling. In addition, the defect rate was reduced from 0.61 to 0.31, the porosity count dropped by ~30% when cooling was applied, and the build was conforming the desired property values for aerospace applications. A hybrid Analytic Hierarchy Process (AHP) – TOPSIS framework was applied to the results for ranking the total builds as each variable or strategy was examined separately to conclude the overall optimal process combination (Run 8 - Dwell-only). These results further validate that controlling the inter-pass temperatures (< 200°C) is critical to preserve grain refinement and stability of dynamic recrystallization. Overall, these results in the hybrid AHP-TOPSIS framework combines with quantum optimization and NV-based sensing can cultivate reproducible, low-defect FSAM builds used in aerospace applications.