Edge sealing technology is a key link in the furniture manufacturing process,and its quality and efficiency have a significant impact on the performance of the final product.However,traditional edge banding machines often lack the ability to automatically optimize and adjust,leading to resource waste and quality issues in the production process.To address this issue,the article proposes an intelligent edge banding machine design based on self-learning algorithms and multi-objective optimization.Firstly,real-time monitoring of various parameters of the edge banding process is carried out using sensors and image recognition technology.Then,by establishing multi-objective optimization models and support vector regression(SVR)models,comprehensive optimization of edge sealing quality,efficiency,and material utilization rate is achieved.The experimental results show that compared with traditional edge banding machines,the proposed intelligent edge banding machine can significantly improve edge banding quality and production efficiency.
维普
万方数据