Int. J. Mechatronics and Manufacturing Systems, Vol. 6, No. 2, 2013 195 Copyright © 2013 Inderscience Enterprises Ltd. Online model-based milling process condition monitoring Abolfath Nikranjbar* and Ali Asghar Atai Mechanical Engineering Group, School of Engineering, Islamic Azad University, Karaj Branch, Iran E-mail: a.nikranjbar@kiau.ac.ir E-mail: atai@kiau.ac.ir *Corresponding author Abstract: In line with demanding the higher efficiency of milling operation, the requests for reliable cutting tool condition monitoring techniques are encountered with considerable interest. Cutting forces are considered the most significant process parameters for developing the condition monitoring techniques. In this article, a real time condition monitoring of milling process utilising recursive least square (RLS) algorithm is proposed. The developed model provides an immediate continuous supervision on the quality of cutting process through monitoring the performance of each tool teeth. The proposed mechanical-analytical approach facilitates the calculation of the cutting force components with any number of cutting edges, which are used not only for cutting tool condition monitoring purposes, but also provide the ground for investigation of the effects of the most influencing parameters of the cutting process such as geometry of cutting, work piece material, feed rate, and tool turning speed and so on. Inherently, due to correlation of the roughness with cutting forces through stiffness of the tool, the reliable estimation of the surface condition (roughness) is also available once the cutting forces are provided. Keywords: milling process; online condition monitoring; cutting force; recursive least square; RLS. Reference to this paper should be made as follows: Nikranjbar, A. and Atai, A.A. (2013) ‘Online model-based milling process condition monitoring’, Int. J. Mechatronics and Manufacturing Systems, Vol. 6, No. 2, pp.195–212. Biographical notes: Abolfath Nikranjbar received his BSc in Mechanical Engineering from Sharif University of Technology, MSc in Mechanical Engineering from Amir Kabir University of Technology, Tehran, Iran and PhD in Mechanical Engineering from University of Bradford, UK, respectively. He is currently with the Mechanical Engineering Department of Islamic Azad University of Karaj, Iran. His main research interests include the fault detection and diagnosis of dynamical systems, application of artificial intelligence systems in identification and control, the dynamics and control of mechanical systems, numerical analysis and software development for scientific applications. Ali Asghar Atai received his BSc in Mechanical Engineering from the University of Tehran, Iran, in 1990. He obtained his MSc and PhD from the University of Alberta, Canada, in 1994 and 1998, respectively. He is currently a Professor at the Department of Mechanical Engineering, Islamic Azad University, Karaj Branch, Iran. His areas of research interest include flexible structural mechanics, continuous media and dynamics of machines.