Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Laser–arc composite additive manufacturing holds significant potential for a wide range of industrial applications, and the control of morphological dimensions in the deposited layer is a critical aspect of this technology. The width and height dimensions within the deposited layer of laser–arc hybrid additive manufacturing serve as essential indicators of its morphological characteristics, directly influencing the shape quality of the deposited layer. Accurate prediction of the shape dimensions becomes crucial in providing effective guidance for size control. To achieve precise prediction of shape dimensions in laser–arc composite additive manufacturing and ensure effective regulation of the deposited layer’s shape quality, this study introduces a novel approach that combines a particle swarm algorithm (PSO) with an optimized support vector regression (SVR) technique. By optimizing the SVR parameters through the PSO algorithm, the SVR model is enhanced and fine-tuned to accurately predict the shape dimensions of the deposited layers. In this study, a series of 25 laser–arc hybrid additive manufacturing experiments were conducted to compare different approaches. Specifically, the SVR model was built using selected radial basis function (rbf) kernel functions. Furthermore, the penalty factors and kernel parameters of the SVR model were optimized using the particle swarm optimization (PSO) algorithm, leading to the development of a PSO-SVR prediction model for the morphological dimensions of the deposited layers. The performance of the PSO-SVR model was compared with that of the SVR, BPNN, and LightGBM models. Model accuracy was evaluated using a test set, revealing average relative errors of 2.39%, 7.719%, 9.46%, and 5.356% for the PSO-SVR, SVR, BPNN, and LightGBM models, respectively. The PSO-SVR model exhibited excellent prediction accuracy with minimal fluctuations in prediction error. This performance demonstrates the model’s ability to effectively capture the intricate and non-linear relationship between process parameters and deposition layer dimensions. Consequently, the PSO-SVR model can provide a foundation for the control of morphological dimensions in the deposition layer, offering an effective guide for deposition layer morphology dimension control in laser–arc composite additive manufacturing.