Optimization of Extrusion-based Silicone Additive Manufacturing Process Parameters Based on Improved Kernel Extreme Learning Machine

Silicone material extrusion (MEX) is widely used for processing liquids and pastes. Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation, products may exhibit geometric errors and performance defects, leading to a decline in product quality and affecting its service life. This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs. To improve the quality of silicone printing samples and reduce production costs, three machine learning models, kernel extreme learning machine (KELM), support vector regression (SVR), and random forest (RF), were developed to predict these three factors. Training data were obtained through a complete factorial experiment. A new dataset is obtained using the Euclidean distance method, which assigns the elimination factor. It is trained with Bayesian optimization algorithms for parameter optimization, the new dataset is input into the improved double Gaussian extreme learning machine, and finally obtains the improved KELM model. The results showed improved prediction accuracy over SVR and RF. Furthermore, a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model. The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.