研究目的
To solve the performance overhead in CNC systems caused by switching between discrete and continuous descriptions in toolpath interpolation by proposing a new approach using deep generative models for direct generation of interpolated toolpaths.
研究成果
The novel AI-based approach using generative models for direct toolpath generation is feasible, with errors comparable to numerical methods. It offers advantages in parallelization and reduced computational effort, but future work is needed to increase resolution, control GAN behavior, and extend to more complex curves.
研究不足
The research is at an early stage, with low resolution in generated curves (28x28 pixels), and the behavior of GANs needs better control. The approach has only been tested on simple curves like linear and parabolic types, not on more complex curves such as splines. Deterministic executability and extension to jerk-optimal curves require further investigation.
1：Experimental Design and Method Selection:
The approach uses deep generative models, specifically Generative Adversarial Networks (GANs), to directly generate discrete toolpaths without intermediate continuous curve calculations. The method involves training GANs to produce curves like linear and parabolic types as discrete point sequences.
2：Sample Selection and Data Sources:
Curves are defined mathematically, such as linear functions f_lin(u) = a*u and parabolic functions f_par(u) = b*u^2 for u in [0,1], with parameters a and b used as labels in conditional GANs.
3：List of Experimental Equipment and Materials:
No specific equipment or materials are mentioned in the paper; the focus is on computational methods using AI models.
4：Experimental Procedures and Operational Workflow:
GANs are trained with a resolution of 28x28 pixels to represent curves as images. The output is filtered to remove noise and transformed into discrete representations. The process replaces traditional interpolation modules in the NC channel.
5：Data Analysis Methods:
The error of generated curves is compared to quantization errors from discretization, using mean approximation error metrics to evaluate performance.
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