Design of Intelligent Interactive Recognition System for Fiber Laser Cutting Machine

The ancient cutting process equipment not only has a large cutting deviation, but also requires the staff to fully grasp the cutting machine to process the workpiece. For this reason, the fiber laser cutting machine can be interactive identification system is planned. The voice interactive identification module in the system absorbs the voice control light issued by the staff. After the number, it is transmitted to the laser sensor module to measure the detailed processing interval and data of the workpiece to be cut and the cutting machine, and the statistical processing cutting deviation is compared to the measurement result and transmitted to the measurement modeling module for cutting. The voice interactive recognition module adopts an intelligent voice recognition algorithm based on the characteristics of inclusions. Recognize the user’s voice commands. The test results prove that the planned system not only recognizes the results better, but also has higher recognition accuracy in noise conditions.

The fiber laser cutting machine is a This equipment is widely used. The mechanical characteristics of the equipment message have a great influence on the performance of the fiber laser cutting machine. In order to optimize the supply bias for the flexible and static characteristics of the fiber laser cutting, it is divided into S1 according to the different position of the cutting head of the cutting machine. ~S9's 9 different stations, and the static finite element analysis of the whole machine is carried out for the different stations of the cutting head of the fiber laser cutting machine. The total deformation and the deformation of each deflection indicate the focus of the machine tool to optimize the deflection and the static of the cutting head The amount of deformation is very important; then the energy modal description is carried out on the very representative workstations of the machine tool. Based on the twelfth-order natural frequency and the sixth-order mode shape, the parts of the machine tool mode that have a greater influence are obtained for the follow-up The optimization mission indicates the bias.