Composite materials have gained widespread application in the aerospace field due
to their advantages, such as high specific strength, high specific modulus, and
corrosion resistance. Automated placement technology, as an emerging automated
manufacturing method, is gradually replacing traditional manual placement
processes and demonstrating significant advantages in composite manufacturing.
Currently, the automated placement process for composite materials faces
challenges such as insufficient experimental samples and strong coupling
relationships between process parameters, leading to low fitting accuracy in
process parameter optimization models. To address this, this paper proposes a
placement process parameter optimization method based on model weight adaptive
allocation. This method integrates three key technologies: a coupling-aware
Gaussian process based on combined kernel functions, a weight allocation
ensemble model based on leave-one-out cross-validation, and a multi-criteria
adaptive sampling mechanism. Experimental validation demonstrates that the
integrated model achieves a coefficient of determination R^2 = 0.82,
which represents a superior fit compared to the R^2 = 0.65 achieved by
a single-kernel Gaussian model and the 0.76 obtained from a single sampling.
Furthermore, both the Root Mean Square Error (RMSE=0.92) and Mean Absolute Error
(MAE=0.70) are lower than those of traditional baseline models. This framework
provides an effective solution for optimizing parameters in the automated
placement process for composite materials.