On the wavelet analysis of cutting forces for chatter identification in milling
Received date: 2016-04-20
Revised date: 2017-04-25
Online published: 2017-06-25
Supported by
The authors would like to express their gratitude to the National Council for Scientific and Technological Development (CNPq) for its financial support (Grant Nos. 483391/2013 and 481406/2013) and to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for its financial support (Grant No. AUXPE 1197/2014)
Chatter vibrations in machining operations affect surface finishing and tool behaviour, particularly in the end-milling of aluminum parts for the aerospace industry. This paper presents a methodological approach to identify chatter vibrations during manufacturing processes. It relies on wavelet analyses of cutting force signals during milling operations. The cutting-force signal is first decomposed into an approximation/trend sub-signal and detailed subsignals, and it is then re-composed using modified subsignals to reduce measurement noise and strengthen the reference peak forces. The reconstruction of the cuttingforce signal is performed using a wavelet denoising procedure based on a hard-thresholding method. Four experimental configurations were set with specific cutting parameters using a workpiece specifically designed to allow experiments with varying depths of cut. The experimental results indicate that resultant force peaks (after applying the threshold to the detailed sub-signals) are related to the presence of chatter, based on the increased correlation of such peaks and the surface roughness profiles, thereby reinforcing the applicability of the proposed method. The results can be used to control the online occurrence of chatter in end-milling processes, as the method does not depend on the knowledge of cutting geometry nor dynamic parameters.
Key words： Cutting force; End milling; Chatter; Wavelet filter
Cesar Giovanni Cabrera , Anna Carla Araujo , Daniel Alves Castello . On the wavelet analysis of cutting forces for chatter identification in milling[J]. Advances in Manufacturing, 2017 , 5(2) : 130 -142 . DOI: 10.1007/s40436-017-0179-4
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