Application of support vector machine–based CNC machining in furniture product visual design and production control process

Traditional furniture design control systems often suffer from complex design, low efficiency, and high production costs. The research aims to explore the application of SVM algorithm to improve furniture design control system, thereby improving production efficiency and reducing production costs. Through feature extraction, research is conducted on transforming raw data into numerical features that can be used for modeling. Based on the extracted features, SVM algorithm is used to establish a furniture design control system model. It performs classification or regression tasks by searching for the optimal hyperplane in the dataset to predict control parameters in the furniture design process. The results indicate that the furniture design control system based on SVM algorithm can effectively improve production efficiency and product quality. Compared to traditional control systems, this system can more accurately control parameters during the machining process, reducing error and scrap rates, and can be flexibly adjusted according to different design requirements.