Laser Cutting Path Planning and Experimental Optimization System Based on GAN Generative Adversarial Network

In view of the problems of low efficiency, low cutting precision and waste of resources in the traditional laser cutting path planning process, a path optimization method based on Generative Adversarial Network (GAN) is introduced to improve the efficiency and accuracy of path planning in the laser cutting process, and effectively reduce the energy and material waste in the cutting process. Firstly, based on the path planning method of Generative Adversarial Networks (GAN), a cutting path planning model is established, in which the generator is used to generate preliminary cutting paths, and the discriminator evaluate and feedback the quality of the generated paths, continuously optimizing the generator to improve the accuracy and efficiency of the paths. Then, a multilevel loss function is designed based on factors such as cutting time, cutting quality, and material waste to guide the GAN model to optimize path generation. Finally, in order to verify the effectiveness of the proposed method, an experimental optimization system is developed by integrating laser cutting equipment with GAN based path planning algorithm, and using real-time data feedback and automatic adjustment modules. The actual performance of different cutting paths is evaluated through simulation and actual cutting experiments. The cutting time range of traditional methods is 24.8 minutes to 27.3 minutes, with an average cutting time of 25.72 minutes. The cutting time range based on GAN path planning method is between 17.5 minutes and 19.6 minutes, with an average time of 18.55 minutes. The GAN based laser cutting path planning and experimental optimization system can effectively solve the shortcomings of traditional path planning, improve cutting efficiency and quality, reduce resource waste, and provide a new idea and solution for the intelligent application of laser cutting technology.