ORIGINAL ARTICLE Prediction of optimal stability states in inward-turning operation using neurogenetic algorithms K. Rama Kotaiah & J. Srinivas & M. Sekar Received: 4 November 2008 / Accepted: 6 March 2009 / Published online: 21 March 2009 # Springer-Verlag London Limited 2009 Abstract This paper proposes a neurogenetic-based optimi- zation scheme for predicting localized stable cutting param- eters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary- coded genetic algorithms. Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminum operated over a high speed steel tool. Keywords Critical chatter length . Tool overhang . Neural networks . Optimum parameters . Orthogonal turning Nomenclature v Cutting speed (m/min) f Feed rate (mm/rev) d Depth of cut (mm) = DOC l Tool overhang length (mm) HSS High speed steel CCL (C c ) Critical chatter length(mm) F x , F y , F z Cutting forces in x, y , and z directions in Newton V o Optimum cutting speed (m/min) f o Optimum feed rate(mm/rev) d o Optimum depth of cut (mm) f w Flank wear (mm) R a Surface roughness (μm) 1 Introduction The most detrimental phenomenon to productivity is unstable cutting. This reduces tool life and surface quality of workpiece. Many theoretical investigations are available in literature for prediction of stable and unstable cutting states in orthogonal cutting. In most of the cases, the stability lobe diagram is generated from an analytical linear model, by varying one operating parameter at a time. In orthogonal turning, it is well known that the cutting forces depend on the operating variables such as feed, depth of cut, and speed. These variables are often used to control the forces or machining stability by establishing appropriate regression relations. Recently, it is found that other parameters such as tool geometry [1], tool wear [2], variations in shear angle [3], and compliance of workpiece [4–6] have great influence on cutting dynamics. To distinguish stability states of cutting, the output features such as surface roughness [7, 8] and type of chips [9] can be employed effectively in addition to cutting force data and stability states. In practice, there are several other operating parameters like tool overhanging length and type of material that may also have influence on the critical operating conditions. For example, variation of tool overhang length changes the stiffness of tool holder, which in turn alters the tool wear and life during unstable conditions. Likewise, the effects of cutting fluids on the surface roughness and tool wear have been predicted [10]. In Int J Adv Manuf Technol (2009) 45:679–689 DOI 10.1007/s00170-009-2007-x K. Rama Kotaiah (*) Department of Industrial and Production Engineering, K.L. College of Engineering, Vaddeswaram, Guntur 522502, Andhra Pradesh, India e-mail: krk_ipe@yahoo.co.in J. Srinivas : M. Sekar School of Mechanical Engineering, Kyungpook National University, Daegu, South Korea