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.
Highlights
A surface roughness appearing as a result of cutting processes has a big impact on functional properties of their products, as well as the quality of products in use
In the experiment conducted by drilling holes depth l = 3d with different input parameters of the drilling process, the the drilling process, the values of torque M (Nm) and arithmetic mean deviation of the surface roughness profile roughness deviation (Ra) were values of torque M (Nm) and arithmetic mean deviation of the surface roughness profile Ra acquired at the beginning of the process, at the moment of medium wear, and at the moment of tool were acquired at the beginning of the process, at the moment of medium wear, and at the moment of blunting
If the experimental results are compared with the results of the model based on artificial neural networks (ANNs) s, it can be noticed that the error in the model is in range from 0.5% to 4.58%, while a mean error of the model based on ANN s with torque is 2.13%
Summary
A surface roughness appearing as a result of cutting processes has a big impact on functional properties of their products, as well as the quality of products in use. Determining a co-dependency between the set input and/or measurable process parameters occurring during machining and parameters of surface roughness contributes to the managing of the process in terms of a timely prediction of the process from the aspect of a satisfactory product quality. A large number of experimental research projects have tried to establish a co-dependency between surface roughness and input parameters for machining processes. Rodrigues et al [1] used a regression analysis for developing a model linking a speed n (rev/min), feed f (mm/rev), and a depth of a cut a (mm) with the surface roughness using an arithmetic mean deviation Ra (μm), by conducting a full plan of the experiment, as well as by varying referred parameters at three levels, when turning a construction steel using tools made of high speed steel (HSS). The corrected determination coefficient in this case was 66.1%, indicating a strong correlation between the surface roughness and referred parameters
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