A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

A soft computing system used to optimize deep drilling operations under high-speed conditions in the manufacture of steel components is presented. The input data includes cutting parameters and axial cutting force obtained from the power consumption of the feed motor of the milling centres. Two different coolant strategies are tested: traditional working fluid and Minimum Quantity Lubrication (MQL). Themodel is constructed in three phases. First, a new strategy is proposed to evaluate and complete the set of available measurements. The primary objective of this phase is to decide whether further drilling experiments are required to develop an accurate roughness prediction model. An important aspect of the proposed strategy is the imputation of missing data, which is used to fully exploit both complete and incomplete measurements. The proposed imputation algorithm is based on a genetic algorithm and aims to improve prediction accuracy. In the second phase, a bag of multilayer perceptrons is used to model the impact of deep drilling settings on borehole roughness. Finally, this model is supplied with the borehole dimensions, coolant option and expected axial force to develop a 3D surface showing the expected M. Grzenda (B) Faculty of Mathematics and Information Science, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland e-mail: [email protected] A. Bustillo Department of Civil Engineering, University of Burgos, Edificio C. C/Francisco de Vitoria, s/n, 09001 Burgos, Spain e-mail: [email protected] P. Zawistowski Faculty of Electronics and Information Technologies, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland e-mail: [email protected] borehole roughness as a function of drilling process settings. This plot is the necessary output of themodel for its use under real workshop conditions. The proposed system is capable of approximating the optimal model used to control deep drilling tasks on steel components for industrial use.