Experimental Investigations and Modeling of Tool Wear in Gun-Drilling Process of 37Cr4 Forge Steel, Using Artificial Neu
- PDF / 1,082,377 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 37 Downloads / 179 Views
ORIGINAL ARTICLE
Experimental Investigations and Modeling of Tool Wear in GunDrilling Process of 37Cr4 Forge Steel, Using Artificial Neural Network with Taguchi Method Amir Rezazadeh1 • Alireza Araee2 • Ahad Gholipoor3
Received: 31 July 2020 / Accepted: 28 October 2020 The Indian Institute of Metals - IIM 2020
Abstract In this study, the tool wear in gun-drilling process of 37Cr4 forge steel was studied. So, the Taguchi method of design of experiments was used to investigate the effects of inputs (tool tip angles (internal and exterior cutting edge angles, free angle of inner and outer cutting edge) and feed rate) on tool wear as output, experimentally. Also, the feedforward backpropagation neural network was used to predict the tool wear in gun-drilling process considering internal and exterior cutting edge angles, free angle of inner and outer cutting edge and feed rate as input parameters of the neural network. In order to select the best performance network with minimum mean squared error in predicting the tool wear in gun-drilling process, the Taguchi method of design of experiments was used. Once again, the input parameters of this design were the number of neurons in hidden layer, type of training function and transfer function of hidden layer of neural network, and mean squared error obtained from neural network run was the output parameter. According to the results, increasing internal and exterior cutting edge angles leads to higher tool wear, while increasing free angle of inner and outer cutting edge decreases tool wear. The tool wear increases by increasing tool feed rate at the first steps, but it decreases by further increase in feed rate. Also, the mean squared error of the neural network with the best & Amir Rezazadeh [email protected] 1
School of Mechanical Engineering, Kish International Campus, University of Tehran, Tehran, Iran
2
School of Mechanical Engineering, University of Tehran, Tehran, Iran
3
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
performance to predict the tool wear was 0.000433, the most differences of the network and experimental results were 0.096 lm and the regression R value of the network with the best performance was 0.98468. Keywords Gun-drilling process Tool wear Artificial neural network Taguchi method
1 Introduction Gun drilling is a kind of drilling process which is used to machine the high aspect ratio holes. It has many applications in different industries such as automotive, aircraft, aerospace, construction, medical, tool and die, petrochemical, hydraulics, pneumatics, where deep holes with high precision and quality with high repeatability are needed [1–3]. In the gun-drilling process, a machine, a high pressure coolant system, a drill with a single or double flute along the shank, a pre-started hole or guide bushing (to prevent tool vibration and to increase the machining accuracy) is needed [4]. The produced chips by the cutting edges of the tool are carried out along the shank by the high pressure of the coola
Data Loading...
