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四川大学 [2]
长春光学精密机械与物... [1]
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期刊论文 [2]
会议论文 [1]
发表日期
2012 [3]
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The load analysis of chisel-edge ruling tool for diffraction gratings (EI CONFERENCE)
会议论文
2012 International Applied Mechanics, Mechatronics Automation Symposium, IAMMAS 2012, September 7, 2012 - September 9, 2012, Shenyang, Liaoning, China
li G.
;
Jirigalantu
;
zhang f.
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浏览/下载:15/0
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提交时间:2013/03/25
The process of wear design on chisel-edge ruling tool of diffraction gratings is very complex because of following two reasons that one is the load distribution on minute edge area when grating ruling
and the other is the anisotropic character of natural diamond. The former is the first step when wear design on chisel-edge ruling tool of diffraction grating
In order to predicate the load distribution on minute edge area of tool
this paper simulated the load distribution on minute edge area of tool by software when ruling on Aluminum-1100 with tabular data format flow stress and Aluminum-1100 with Power law flow stress respectively
and through comparative analysis determined the approximate load distribution on minute edge area of chisel-edge ruling tool that the high load mainly focused on the edge area close to the tip of ruling tool and amount to 200MPa while about to 100MPa on the face of tool. (2012) Trans Tech Publications
Switzerland.
A NEW APPROACH OF TOOL WEAR CONDITION MONITORING BASED ON TEXTURE IMAGE RECOGNITION
期刊论文
FIFTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER THEORY AND ENGINEERING (ICACTE 2012), 2012, 页码: 173-178
作者:
Ren, XH
;
Xu, WD
;
ASME
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浏览/下载:7/0
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提交时间:2019/02/25
component
Texture image
Tool wear condition
real-time monitoring
Identification and Diagnosis
LVQ neural network
Research of Tool Wear Condition Recognition Diagnosis System Based on the Machined workpiece Surface Texture Image
期刊论文
MECHANICAL AND ELECTRONICS ENGINEERING III, PTS 1-5, 2012, 卷号: Vol.130-134, 页码: 2508-2512
作者:
Xu, WD
;
Ren, XH
;
Li, LJ
;
Yue, YG
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浏览/下载:4/0
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提交时间:2019/02/28
Tool wear condition
BP neural network
texture image
Matlab_GUI
Identification and diagnosis
gray level co-occurrence matrix
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