Given the application of a multiple regression and artificial neural networks (ANNs), this paper describes development of models for predicting surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling enhancement steel EN 42CrMo4, thermally treated to the hardness level of 28 HRC, using cruciform blade twist drills made of high speed steel with hardness level of 64-68 HRC. The model was developed using process parameters (nominal diameters of twist drills, speed, feed, and angle of installation of work pieces) as input variables varied at three levels by Taguchi design of experiment and measured experimental data for a torque and arithmetic mean deviation of a surface roughness for different values of flank wear of twist drills. The comparative analysis of the models results and the experimental data, acquired for the inputs at the moment when a wear span reaches a limit value corresponding to a moment of the drills blunting, demonstrates that the neural network model gives better results than the results obtained in the application of multiple linear and nonlinear regression models.
The paper presents a comparative analysis of the models for predicting machined surface quality developed by the application of multiple regression and artificial neural networks. The models were developed using experimental data for the mean arithmetic deviation of surface roughness and the axial cutting force obtained by implementing the Taguchi experiment plan. Comparative analysis of the models has shown that artificial neural networks give the best results in terms of predicting the mean arithmetic deviation of surface roughness on the basis of process parameters and axial cutting force. Keyword-models, axial force, prediction, surface roughness I. INTRODUCTION Experimental research seeks to establish dependence between quality of the machined surface and parameters of the cutting process. Prediction of the machined surface quality through the mean arithmetic deviation of the surface roughness (R a) is made by using a multiple regression mathematical model and by applying models based on artificial neural networks that connect machining process input parameters with the quality of the machined surface. Çiçek, Kivak and Samtaş [1], using the Taguchi experiment design in a drilling operation on austenitic stainless steel AISI 316 with high-speed steel (HSS) twist drills, conventionally and cryogenic, varying feed f (mm/o) and the cutting speed v c (m/min) at two levels, develop a regression model that combines the indicated parameters with the machined surface quality through the mean arithmetic deviation R a (μm), with a 96,3% coefficient of determination. Rodrigues et al. [2] use a regression analysis to obtain a mathematical model that links the spindle speed n (rpm), feed f (mm/rev) and cutting depth a (mm) with the machined surface quality through the mean arithmetic deviation R a (μm) by conducting a full experiment plan and varying of the mentioned parameters at three levels in a turning operation on structural steel with high-speed steel (HSS) tools. The adjusted coefficient of determination in this case is 66,1% which indicates a strong connection between the machined surface quality and the mentioned parameters. Raghunandan, Bhandarkar and Pankaj [3], based on the data obtained using the Taguchi experiment design in a truning operation on EN-19 material with cemented carbide inserts, come up with a model linking a mean arithmetic deviation of surface roughness R a (μm), cutting speed v c (m/min), feed f (mm/rev) and cutting depth a (mm). The adjusted coefficient of determination, which describes the given connection, in this case is 52,8%. Ficici, Koksal and Karacadag [4] investigate the effect of tool modification (twist drill cutting edge grinding in μm), cutting speed v c (m/min) and feed f (mm/rev), using Taguchi experiment design in a drilling operation on austenitic stainless steel AISI 304 with high-speed steel (HSS) twist drills. Development of the regression model links the stated parameters with the machined surface quality through the mean arithmetic deviation R a (μm) and the implementati...
This paper presents a model of dependence between the parameters of surface roughness and the parameters of cylindricity and eccentricity in drilling operation for the enhancing steel EN 42CrMo4, hardness 28 HRC, with twist drills DIN 338 made of high-speed steel EN HS6-5-2, with normal blade. The quality of machining, besides the accuracy of measures, completely determined with the values of the parameters of the surface roughness and the parameters of form and location, hence this paper is oriented to the creating models between parameters of the quality of a machined surface and parameters of deviations from form and position. By the developing models based on artificial neural networks and using experimental results, it is possible to analyse the quality of machining on the basis of parameters of a surface roughness.
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