An online tool wear detection system in dry milling based on machine vision
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Tool wear is accelerated with the friction in the tool–workpiece contact during dry cutting. Tool changing early or late will affect the quality of tool and workpiece. An online and machine system vision-based is built to monitor tool condition in real time. MATLAB is used to compile the self-matching algorithm, which considers the features of interested targets on the flank face. Furthermore, a corresponding GUI is designed and encapsulated for both the bottom and flank edges. It is shown that the absolute value of the error on the maximum wear width is not more than 0.007 mm for the bottom edge. For the flank edge, the absolute value of the error is not more than 0.030 mm owing to the local highlighting interference. It is proved that the system can guarantee the quality of tool and workpiece and avoid unnecessary waste significantly. This platform can enhance the utilization of the tool in dry cutting.
KeywordsOnline detection Tool wear Dry milling Machine vision Self-matching algorithm
The authors want to extend sincere thanks to all the previous researches that contributed to this paper.
This study is supported by the AVIC Cheng Du Aircraft Industrial (Group) Co. Ltd. (grant no. 2014-063), Key R & D project of Shandong Province (grant no. 2017GGX30141), and difital workshop of aeronautic large-scale complex structural (intelligent manufacturing special support by Ministry of Industry and Information Technology in 2015) (grant no. 40205000150X).
- 5.Sun X G, Sun L, Wang E H (2014) Study on joint surface parameter identification method of shaft-toolholder and toolholder-tool for vertical CNC milling machine. Mach Tool & Hydr 42(23):106-109.Google Scholar
- 18.Xin Y C (2016) The parameters of tool wear detection technology research. Dissertation, Xi’an Technological UniversityGoogle Scholar
- 22.Sun LL, Li Y, Zheng JM, Li PY (2010) Fractal analysis of PCA reconstruction for tool wear monitoring. Mech Sci Tech Aero Eng 29(3):395–397Google Scholar
- 24.Wang C C (2014) On-site tool wear detection based on high precision computer vision. Dissertation, Donghua UniversityGoogle Scholar
- 25.Hou QL, Sun J, Huang PL (2018) A novel algorithm for tool wear online inspection based on machine vision. Int J Adv Manuf Technol 101(9):2415–2423Google Scholar