A Novel Fuzzy Echo State Broad Learning System for Surface Roughness Virtual Metrology

Surface roughness is one of the determining factors for evaluating the quality of machined parts. However, the inevitable time-varying and uncertain characteristics in the actual machining process brings challenges to the construction of virtual metrology model. To address the problems of time-consuming training and low prediction accuracy in conventional virtual metrology models for surface roughness, a novel fuzzy echo state broad learning system (FESBLS) is proposed by introducing a reservoir with echo state properties to capture the dynamics of the machining process and then by employing incremental learning to reduce computational complexity and improve prediction accuracy. Besides, the effectiveness of the proposed method is validated by a grooving experiment and compared with benchmark approaches. Herein, the force signal collected during the grooving process and its fusion with cutting parameters are input into the FESBLS. The results show that the proposed FESBLS outperforms other models in improving the prediction performance. All in all, FESBLS is a promising technique for virtual metrology in machining processes.