The invention offers a parameter optimization method for 3D printing to address existing issues in prior art. The method includes several steps:
1. Building a function model based on parameters in 3D printing.
2. Establishing a three-dimensional interpolation model using the polynomial interpolation method.
3. Processing the function model with the three-dimensional interpolation model to determine parameter correspondences.
4. Sampling the parameters in 3D printing to acquire an empirical data sample set.
5. Utilizing the Gaussian process regression model to process the empirical data sample set and parameter correspondences to obtain optimized 3D printing process parameters. This method enables more efficient and precise optimization of 3D printing parameters, enhancing printing quality and efficiency.
