Leveraging classifier-guidance diffusion model for improved surface roughness prediction through synthesized audible sound signal

Surface roughness is an essential attribute of surface quality in the machining process to ensure the mechanical functionality of machined parts. Effective in-process monitoring of surface roughness is indispensable, which can be achieved through intelligent monitoring and prediction systems, utilizing data-driven models and dynamic process signatures. Notably, the performance of these systems, particularly in terms of prediction accuracy and robustness, is hindered by the limited application of data-driven models, especially deep learning models, which are intensive data-demanding. The inaccessibility of ample surface roughness data and corresponding process signatures leveraged for model building poses a significant challenge. In response to this challenge, as an emerging generative modelling approach, a classifier guidance diffusion model renowned for process stability and image generation with high fidelity and sufficient diversity is introduced to surmount data scarcity. The pre-trained diffusion is applied to synthesize audible sound signals collected from a machining process with the guidance of gradients from a classifier. After that, ground truth signal data mixed with synthesized ones in different proportions are leveraged to train and test the pre-trained model – VGG16, for surface roughness prediction in a transfer learning process. The results demonstrate that the proposed model can effectively generate synthesized 2D Mel-spectrogram images of audible sound signals with superior quality, further boosting the prediction performance of VGG16 in terms of its prediction accuracy and generalization under an enriched dataset.