Modelling Surface Roughness in the Function of Torque When Drilling

Abstract

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.

WITHDRAWN: Model of Dependence Between the Parameters of Surface Roughness and the Parameters of Form and Location During the Machining by Drills with the use of Artificial Neural Networks

“…Krivokapić, Z., et al [1], give the application of a multiple regression and arti cial neural networks (ANNs), and this paper describes the development of models for the predicting a surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling.…”

“…By the development of models based on neural networks by using experimental results, it is possible to analyse the quality of machining on the basis of the parameters of a surface roughness. Krivokapić, Z., et al [1], give the application of a multiple regression and arti cial neural networks (ANNs), and this paper describes the development of models for the predicting a surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling.…”

Investigation of the Effect of Drill Bit Feature on Hole Surface Quality and Tool Wear

“…The values obtained as a result of the study are quite compatible with the relevant literature. In addition, in the literature [7,12], it is stated that there is more surface roughness at the hole exit under all experimental conditions. In this respect, the results are in line with the literature.…”

“…It was observed that the surface roughness increased by about 30% with an increase of 50% in the feed rate. Krivokapić et al [7] studied the drilling of 42CrMo4 steel material with 3 mm, 5 mm and 8 mm diameter HSS drill bits. They applied the cutting speed as 20 m/min and the feed as 0.03, 0.05 and 0.10 mm/rev.…”

Emerging Trends in Single Point Incremental Sheet Forming of Lightweight Metals

“…The incremental forming process does not load these machines in the same way as the processes that these machines were originally designed for. Comparison of the loads when machining on a five-axis CNC centre shows [91] that the loads on the machining centre in the vertical "z" axis are significantly lower than those appearing in the metal-forming operations of thicker and/or "difficult-to-form" materials. A similar effect can be observed when the robot arms are applied to ISF.…”