Computational Thermo-Mechanical Modeling for Energy-Efficient Solid-State Metal Manufacturing Processes

Abstract

This study examined computational thermo-mechanical modeling for energy-efficient solid-state metal manufacturing processes using a quantitative quasi-experimental simulation design. A total of 120 validated simulation cases were analyzed, covering a wide range of process parameters including tool velocity (200–1200 rpm), applied force (5–25 kN), and varying thermal boundary conditions. The results showed that peak temperature varied significantly from 420.00 K to 980.00 K, with a mean value of 685.40 K (SD = 112.35), while stress ranged from 85.00 MPa to 410.00 MPa, averaging 236.70 MPa (SD = 68.45). Strain values exhibited a mean of 0.42 (SD = 0.15), and energy consumption ranged between 1.85 J/mm³ and 6.75 J/mm³, with a mean of 3.92 J/mm³ (SD = 1.12). Statistical analysis confirmed that tool velocity (F = 18.45, η² = 0.42), applied force (F = 15.72, η² = 0.38), and thermal conditions (F = 12.89, η² = 0.34) had significant effects on thermo-mechanical outputs (p < 0.05). Regression modeling demonstrated strong predictive capability, with an R² value of 0.81 for energy consumption and low standard error (0.42), indicating high model accuracy. Subgroup analysis revealed that friction-based processes generated higher peak temperatures (765.30 K) and energy consumption (4.35 J/mm³) compared to diffusion-based processes (612.85 K and 3.25 J/mm³). High-speed conditions further increased temperature to 825.60 K and stress to 285.90 MPa, while low-speed conditions maintained lower energy demand at 2.95 J/mm³. Optimal performance was observed at moderate parameter ranges, where energy consumption remained below 3.50 J/mm³ and stress levels were controlled below 240 MPa. These findings confirmed that thermo-mechanical coupling significantly influenced process efficiency, and precise parameter optimization was essential for achieving balanced energy-efficient manufacturing performance.