Authors: Karali Patra; Surjya K. Pal; Kingshook Bhattacharyya
Addresses: Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna-800013, Bihar, India ' Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur-721302, West Bengal, India ' Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur-721302, West Bengal, India
Abstract: In this work, strategies for artificial intelligence (AI)-based drill flank wear prediction systems have been presented. Four AI-based models, namely, back propagation neural network (BPNN), radial basis function network (RBF), normalised radial basis function network (NRBF) and fuzzy radial basis function network (FRBF) have been used. Signals have been acquired from dynamometer, current sensor and accelerometer during drilling under different cutting conditions. Wear sensitive features in time-domain and frequency domain have been extracted. Different strategies, i.e., various combinations of the selected features and process parameters used as inputs to the AI models, are formulated. For most of the strategies, BPNN model gives better wear prediction results followed by NRBF model. But for noisy features such as features from vibration signal can be better related to drill wear using FRBF model.
Keywords: drilling; flank wear; artificial intelligence; sensor features; wear prediction; intelligent prediction; drill wear; multiple sensor signals; back propagation neural networks; BPNN; radial basis function networks; RBF networks; fuzzy logic; vibration signals; tool wear.
International Journal of Mechatronics and Manufacturing Systems, 2013 Vol.6 No.5/6, pp.493 - 512
Received: 02 May 2013
Accepted: 04 Jun 2013
Published online: 02 Jan 2014 *