Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20.

ABSTRACT: The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to est

SUBMITTER: Guo M 

PROVIDER: S-EPMC10518346 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature