On-line prediction method of surface roughness of parts based on SDAE-DBN algorithm
US12026625B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 28, 2020 |
| Grant date | Jul 2, 2024 |
| Priority date | — |
| Expiry date | Jul 28, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An on line prediction method of part surface roughness based on SDAE-DBN algorithm. The tri-axis acceleration sensor is adsorbed on the rear bearing of the machine tool spindle through the magnetic seat to collect the vibration signals of the cutting process, and a microphone is placed in the left front of the processed part to collect the noise signals of the cutting process of the machine tool; the trend term of dynamic signal is eliminated, and the signal is smoothed; a stacked denoising autoencoder is constructed, and the greedy algorithm is used to train the network, and the extracted features are used as the input of deep belief network to train the network; the real-time vibration and noise signals in the machining process are input into the deep network after data processing, and the current surface roughness is set as output by the network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.