MATEC Web Conf.
Volume 74, 2016The 3rd International Conference on Mechanical Engineering Research (ICMER 2015)
|Number of page(s)||7|
|Published online||29 August 2016|
Statistical Analysis of Deep Drilling Process Conditions Using Vibrations and Force Signals
1 Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang
2 Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang
a Corresponding author: firstname.lastname@example.org
Cooling systems is a key point for hot forming process of Ultra High Strength Steels (UHSS). Normally, cooling systems is made using deep drilling technique. Although deep twist drill is better than other drilling techniques in term of higher productivity however its main problem is premature tool breakage, which affects the production quality. In this paper, analysis of deep twist drill process parameters such as cutting speed, feed rate and depth of cut by using statistical analysis to identify the tool condition is presented. The comparisons between different two tool geometries are also studied. Measured data from vibrations and force sensors are being analyzed through several statistical parameters such as root mean square (RMS), mean, kurtosis, standard deviation and skewness. Result found that kurtosis and skewness value are the most appropriate parameters to represent the deep twist drill tool conditions behaviors from vibrations and forces data. The condition of the deep twist drill process been classified according to good, blunt and fracture. It also found that the different tool geometry parameters affect the performance of the tool drill. It believe the results of this study are useful in determining the suitable analysis method to be used for developing online tool condition monitoring system to identify the tertiary tool life stage and helps to avoid mature of tool fracture during drilling process.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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