An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece
- Resource Type
- Authors
- Ruhai Zhang; Dedao He; Kun Wang; Xiaojun Wu; Ruiyuan Wang; Yulin Tong; Haicheng Yu
- Source
- Mathematical Problems in Engineering, Vol 2021 (2021)
- Subject
- 0209 industrial biotechnology
Article Subject
Computer science
General Mathematics
020208 electrical & electronic engineering
Fast Fourier transform
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
General Engineering
Skew
Mechanical engineering
02 engineering and technology
Surface finish
Engineering (General). Civil engineering (General)
Fractal dimension
Digital image
020901 industrial engineering & automation
Machining
QA1-939
0202 electrical engineering, electronic engineering, information engineering
TA1-2040
Tool wear
Mathematics
Reliability (statistics)
ComputingMethodologies_COMPUTERGRAPHICS
- Language
- ISSN
- 1563-5147
1024-123X
Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.