Mechanic of Advanced and Smart Materials
Quarterly

Experimental study of surface roughness of brass micro-drilling to optimize parameters using E-Fast statistical method

Document Type : Original Article

Authors

Vahid Tahmasbi 1
Zahra Eghdami 2

1 Department of Manufacturing Engineering, Arak University of Technology, Arak, Iran

2 Department of Mechanical Engineering, Arak University, Arak, Iran

https://doi.org/10.52547/masm.1.2.216
Abstract
Today, the drilling process is one of the most fundamental machining processes among industrial processes. The drilling process is expanding to high-speed, high-precision machining due to productivity improvements. The drill bits used in this process play an important role and can increase the surface quality and improve the surface roughness. It should be noted that the costs of breaking the drill bit are high. In the micro-drilling process, increasing the rotational speed decreases the machining time, but also causes a faster tool wear rate. Also, reducing the feed rate improves the surface quality, but on the other hand, reduces the material removal rate. Therefore, an accurate selection of various parameters in micro-drilling is necessary to achieve the desired surface roughness. In this paper, firstly, by performing experimental tests, a second-order linear regression mathematical model is presented to predict the surface roughness during micro-drilling operations of brass by input parameters of rotational speed, feed rate, and tool diameter and their effective interactions. Then, using the E-Fast statistical sensitivity analysis method, the effect of the studied parameters on the surface roughness is obtained. The results obtained from the e-Fast statistical sensitivity analysis method show that among the three input parameters, the feed rate with 62% effect on surface roughness as the most important parameter, the rotational speed with 34% effect as the second parameter affecting roughness. The final surface, as well as the tool diameter with only 4% impact, is known as the least effective parameter on the surface roughness in the micro-drilling process of brass micro-drilling.
Today, the drilling process is one of the most fundamental machining processes among industrial processes. The drilling process is expanding to high-speed, high-precision machining due to productivity improvements. The drill bits used in this process play an important role and can increase the surface quality and improve the surface roughness. It should be noted that the costs of breaking the drill bit are high. In the micro-drilling process, increasing the rotational speed decreases the machining time, but also causes a faster tool wear rate. Also, reducing the feed rate improves the surface quality, but on the other hand, reduces the material removal rate. Therefore, an accurate selection of various parameters in micro-drilling is necessary to achieve the desired surface roughness. In this paper, firstly, by performing experimental tests, a second-order linear regression mathematical model is presented to predict the surface roughness during micro-drilling operations of brass by input parameters of rotational speed, feed rate, and tool diameter and their effective interactions. Then, using the E-Fast statistical sensitivity analysis method, the effect of the studied parameters on the surface roughness is obtained. The results obtained from the e-Fast statistical sensitivity analysis method show that among the three input parameters, the feed rate with 62% effect on surface roughness as the most important parameter, the rotational speed with 34% effect as the second parameter affecting roughness. The final surface, as well as the tool diameter with only 4% impact, is known as the least effective parameter on the surface roughness in the micro-drilling process of brass micro-drilling.

Keywords

Sensitivity analysis
statistical E-Fast methods
drilling process
surface roughness
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Mechanic of Advanced and Smart Materials
Volume 1, Issue 2
Winter 2022
Pages 216-230

  • Receive Date 23 October 2021
  • Revise Date 02 December 2021
  • Accept Date 08 February 2022

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