Artificial Neural Network Model for Tool Condition Monitoring in Stone Drilling Danko Brezak 1,a , Tomislav Staroveski 2,b , Ivan Stiperski 3,c , Miho Klaic 4,d and Dubravko Majetic 5,e 1,2,3,4,5 Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, Zagreb, HR-.10000, Croatia a danko.brezak@fsb.hr, b tomislav.staroveski@fsb.hr, c stiperski.ivan@gmail.com, d miho.klaic@fsb.hr, e dubravko.majetic@fsb.hr Keywords: Stone drilling, tool wear classification, neural networks Abstract. This paper explores the possibility of tool wear classification in stone drilling. Wear model is based on Radial Basis Function Neural Network which links tool wear features extracted from motor drive current signals and acoustic emission signals with two wear levels – sharp and worn drill. Signals were measured during stone drilling under different cutting conditions, and then filtered before tool wear features extraction. Features were obtained from time and frequency domain. They have been analyzed individually and in combinations. The results indicate tool wear monitoring capacity of the proposed model in stone drilling, and its potential for simple and cost- effective integration with CNC machine tools. Introduction Tool wear monitoring is one of the most important segments in the development of fully automated and highly autonomous CNC machine tools. Except in machine tool diagnostics, it is also necessary in the implementation of machining process control systems which could prevent tool breakage and/or maintain predefined tool wear dynamic [1, 2]. Tool wear monitoring in drilling has been continuously in the research focus for the past 20 years. A number of machine learning algorithms, sensor combinations and tool wear features have been analyzed and proposed, mainly using metal and composite materials [3]. Only a few studies considered wear monitoring in stone machining. They have usually included wear identification of diamond tools applied in cutting and/or milling using cutting forces sensors [4-7]. The aim of this study was to analyze capabilities of neural network-based tool wear classification model in stone drilling using cost-effective combination of internal drive signals or currents (instead of cutting forces) and acoustic emission sensor. For this purpose, a type of Radial Basis Function Neural Network (RBF NN) algorithm for solving classification types of problems has been chosen. This type of neural network is known for its learning in one step and a capability of simple and quick hidden layer structure adaptation. Experiments were conducted using a custom-made machine tool testbed with open architecture control platform. Experimental Work Machine Tool Testbed. Experimental work has been performed using the three-axis bench-top CNC mini milling machine with an internal and external measurement systems (Fig. 1). The machine has been retrofitted with the 0.4 kW (1.27 Nm) permanent magnet synchronous motors with integrated incremental encoders (Mecapion SB04A), corresponding motor controllers (DPCANIE-030A400 and DPCANIE-060A400), ball screw assemblies, and LinuxCNC open architecture control (OAC) system [8]. Considering the nature of the drilling process, two types of signals were sampled from those controllers: vertical or Z-axis feed drive current (I Z ) and main spindle current (I MS ). Beside motor drive currents, acoustic emission signals (AE) were also measured using 8152B piezoelectric AE sensor and 5125 coupler (Kistler) connected to PCI- Applied Mechanics and Materials Vol 772 (2015) pp 268-273 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.772.268 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (ID: 89.164.241.35-29/04/15,21:32:07)