Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting

Surface roughness is one of the most critical attributes of machined components, especially those used in high-performance systems. Online surface roughness monitoring offers advancements comparable to post-process inspection methods, reducing inspection time and costs and concurrently reducing the likelihood of defects. Currently, online monitoring approaches for surface roughness are constrained by several limitations, including the reliance on handcrafted feature extraction, which necessitates the involvement of human experts and entails time-consuming processes. Moreover, the prediction models trained under one set of cutting conditions exhibit poor performance when applied to different experimental settings. To address these challenges, this work presents a novel deep-learning-assisted online surface roughness monitoring method for ultraprecision fly cutting of copper workpieces under different cutting conditions. Tooltip acceleration signals were acquired during each cutting experiment to develop two datasets, and no handcrafted features were extracted. Five deep learning models were developed and evaluated using standard performance metrics. A convolutional neural network stacked on a long short-term memory network outperformed all other network models, yielding exceptional results, including a mean absolute percentage error as low as 1.51% and anR2 value of 96.6%. Furthermore, the robustness of the proposed model was assessed via a validation cohort analysis using experimental data obtained using cutting parameters different from those previously employed. The performance of the model remained consistent and commendable under varied conditions, asserting its applicability in real-world scenarios.