Data-Driven Design and Optimisation of Eutectic-Assisted Alumina Ceramics Using RSM and Symbolic Regression

Lowering the processing temperature of alumina while retaining mechanical performance requires control of additive chemistry and powder processing. This study examines CaO–SiO2–Al2O3-assisted alumina through a 27-condition dataset, response surface methodology (RSM) and symbolic regression using GPTIPS 2.0. Sintering temperature, coarse-alumina mass fraction, lubricant content and additive content are the input variables; flexural strength and density are performance responses, while shrinkage is treated as a dimensional response. The highest tabulated strength is 173.52 MPa at 1550 °C, with a density of 3.610 g/cm3. GPTIPS evaluation-subset variation explained is 89.55%, 87.39% and 81.24% for strength, density and shrinkage, respectively. These statistics describe model selection rather than independent predictive validation. A comparison with published reduced-cubic RSM models gives strength and density R2 values of 0.9825 and 0.9934, whereas full-quadratic models yield lower adjusted R2 values. The results demonstrate the influence of model specification on fitted agreement and distinguish the maximum tabulated response from a constrained literature optimum. The combined analysis provides a basis for assessing processing–property relationships while recognising the dimensional and service-performance trade-offs associated with liquid-phase-assisted sintering.