176 Int. J. Manufacturing Technology and Management, Vol. 32, No. 2, 2018 Copyright © 2018 Inderscience Enterprises Ltd. A modified Monte Carlo method to study the performance of the roughness models Y. Ech-Charqy University Hassan 1, Laboratory of Applied Chemistry and Environment, Laboratory of Mechanic, Industrial Management and Innovation, BP 577 Settat, Morocco Email: echyounes@gmail.com *Corresponding author H. Gziri University Hassan 1, Laboratory of Mechanic, Industrial Management and Innovation, BP 577 Settat, Morocco Email: hgziri@gmail.com M. Essahli University Hassan 1, Laboratory of Applied Chemistry and Environment, BP 577 Settat, Morocco Email: mohamed.essahli@uhp.ac.ma Abstract: Several empirical and mathematical models have been proposed to predict the surface roughness, but they are more or less effective in determining an approximate value to the real one, especially when we worked in a specific constraint that can cause errors in results. Hence, it is necessary to use an efficient method to determinate the more performance model, and to minimise the error range. In this work, we will propose a method modified of Monte Carlo algorithm (MMC) with an output Boolean signal and a performance ratio (PR) to study the performance of surface roughness models under specified constraints. It is a powerful and simple strategy based on Monte Carlo algorithm, which determine the possibility of finding the desired roughness values in a specified range, choosing the most efficient model to minimise inadequate results to our predicting values. Keywords: methodology; model performance; modified Monte Carlo; MMC; performance algorithm; roughness; super-finishing. Reference to this paper should be made as follows: Ech-Charqy, Y., Gziri, H. and Essahli, M. (2018) ‘A modified Monte Carlo method to study the performance of the roughness models’, Int. J. Manufacturing Technology and Management, Vol. 32, No. 2, pp.176–188. Biographical notes: Y. Ech-Charqy is a PhD Research Scholar in Hassan 1 University and a mechanical engineer and trainer in the Professional Training and Promotional Work Office (OFPPT). He has three years of industrial experience in the field of heavy engineering, and six years as trainer in mechanical manufacturing.