.png){kind=icon}
.png){kind=icon}
Return
Integrated machining error compensation method using OMM data and modified PNN algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijmachtools.2005.10.002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents an integrated machining error compensation method based on polynomial neural network (PNN) approach and inspection database of on-machine-measurement (OMM) system. To improve the accuracy of the OMM system, geometric errors of the CNC machining center and probing errors are compensated. Machining error distributions of a specimen workpiece are measured to obtain error compensation parameters. To efficiently analyze the machining errors, two machining error parameters, W(err) and D(err), are defined. Subsequently, these parameters can be modeled using the PNN approach, which is used to determine machining errors for the considered cutting conditions. Consequently, by using ail iterative algorithm, tool path can be corrected to effectively reduce machining errors in the end-milling process. Required programs are developed using Ch language, and modified termination method are applied to reduce computation times. Experiments are carried out to validate the approaches proposed in this paper. The proposed integrated machining error compensation method can be effectively implemented in a real machining situation, producing much fewer errors. (C) 2005 Elsevier Ltd. All rights reserved.
Keywords:
machining error compensation
on-machine measurement
polynomial neural network
CAD/CAM/CAI integration
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
18.8
Papers:
3.6K
Citations:
1.8W
Organization
No organization information available