Abstract
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.
Highlights
Statistical parameters are established method applied in research analysis
The highest cutting speed that can be adapted through standard long series twist drill tool (Sherwood SHR-025) is through experiment number 7 with 40 m/min, feed rate 0.15mm/rev and depth of cut 60mm
The signal has been analyzed through several statistical parameters techniques
Summary
Statistical parameters are established method applied in research analysis. Through the implementation of statistical analysis it can improve the process understanding, enhanced level of control and contribute in reduces process variability and improved the output quality [3]. There are several numbers of statistical parameters such as root mean square (RMS), arithmetic mean, standard deviation, variance and kurtosis etc. It has been employed widely in manufacturing process analysis activities. Ghoreishi et al [4] applied variance to analyze the interactions between six controllable variables on the hole taper and circularity in laser percussion drilling. Gaja [7] implemented RMS to analyze data depth-of-cut detection and tool-workpiece engagement by using acoustic emission monitoring system during milling machining. RMS values were used to classified tool wear levels in lathe, milling and drilling machines [8, 9, and 10]. Kurtosis and skewness of vibration signal data be implemented to indicate the different types of drill wear [11]
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