Research of Tool Wear Condition Recognition Diagnosis System Based on the Machined Workpiece Surface Texture Image

Wei Dong Xu; Xiao Hong Ren; Li Jie Li; Ying Gao Yue

doi:10.4028/www.scientific.net/AMM.130-134.2508

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 130-134Research of Tool Wear Condition Recognition...

Research of Tool Wear Condition Recognition Diagnosis System Based on the Machined Workpiece Surface Texture Image

Abstract:

Aiming at the machined workpiece surface texture images,some technology about image pre-processing and the texture feature extraction based on gray level co-occurrence matrix are researched. Then it is time for the analysis of the texture characteristic parameters based on BP neural network and the identification and diagnosis of tool wear state, Finally the recognition diagnosis system interface is designed by Matlab-GUI.System simulation shows that the interface fusion of image processing and neural network is a good way to ensure the realization of tool wear condition recognition,what’more, the identification diagnosis rate is profect.

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Periodical:

Applied Mechanics and Materials (Volumes 130-134)

Pages:

2508-2512

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.130-134.2508

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Online since:

October 2011

Authors:

Wei Dong Xu, Xiao Hong Ren, Li Jie Li, Ying Gao Yue

Keywords:

BP Neural Network (BPNN), Diagnosis, Grey Level Co-Occurrence Matrix, Identification, MATLAB GUI, Texture Image, Tool Wear Condition

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References

[1] J. Liu Jian-ping, Zhen Qi-lun, On-line Identification of Cutting Tool Wear Based on Stylebook Clustrering Fuzzy Neural Network. Vol. 39, NO. 5. Shanghai: Machinery, 2001 pp.11-12.

Google Scholar

[2] J. Chen Chao, Xu Jian-lin, and Huang Jian-long. Analysis of Tool Wear Condition Based on matlab. No7. Beijing: Manufacturing technology & Machine tool, 2005, pp.72-75.

Google Scholar

[3] J. ZhangYue. Research on tool wear inspection technology based on computer vision. No4. Beijing: Manufacturing technology & Automation, 2008, pp.107-109.

Google Scholar

[4] J. Ruan Jiu-zhong, Zhou Chen-bo, Yang Guo-hua, and Liu Gui-fen, Image Texture Based on Co-occurrence Matrix for Non-Planar Surface Roughness. Vol. 6. NO. 6. Optics &Optoelectronic Technology. 2008, pp.36-40.

Google Scholar

[5] M. Yang Gao-bo, Du Qin-song. Applications and Examples of image/video processing based on Matlab. edtied by Beijing: Publishing House of Elextronics Industry, (2010).

Google Scholar

[6] M. Rafael C. Gongzalez Richard. E. Woods. SteVenL. Eddins. Digital Image Processing Using Matlab. Ruan Qiu-qi etal.T. edtied by Beijing: Electronics Industry, (2005).

Google Scholar

[7] M. Matlab Chinese Forum. 30 Cases of MATLAB Neural Network Analysis. edtied by Beijing: Publishing House of Bei Hang University, (2010).

Google Scholar