Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel

• Main Authors: Amin, A. K. M. Nurul, Hafiz, A.M. Khalid, Lajis, M. A.
• Format: Book Chapter
• Language: English
• Published: IIUM Press 2011
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://irep.iium.edu.my/23599/4/chp20.pdf; http://irep.iium.edu.my/23599/; http://rms.research.iium.edu.my/bookstore/default.aspx
• Institution: Universiti Islam Antarabangsa Malaysia

Considering the demand for reduced cycle time and increased productivity hard turning and milling have become a useful alternative when high material removal rate is an immense requirement. Advantages in hard machining incorporate the complete machining process with a single fixture setup, eliminating intermediate heat treatment and final grinding process while still meeting the dimensional and surface roughness specifications [1]. Over the last decade high speed machining has been used extensively to produce mould and die from hardened material like AISI H13 tool steel. Many progressive works have been carried out to improve the high speed machining performance of H13. Despite the widespread adoption of milling process in fabricating mould and die, most of the research works till to date concentrated on hard turning. J. J. Junz Wang & M. Y. Zheng, 2003 et al [1], illustrated the machining characteristics of AISI H13 tool steels of hardness 41 and 20 HRC and found that the higher cutting and frictional energies are required in the chip shearing as well as in the nose ploughing processes of the softer tool steel. Poulachon et al. [2] , showed that the major influencing parameter on tool-wear happens to be the presence of carbides in the steel microstructure. Ghani et al. [3] applied Taguchi method to optimize cutting parameters in end milling of H13 steel at high speed cutting. They found that feed and depth of cut possess the most significant effect over tool life for a given range of cutting speed, feed and depth of cut. Recently with the advent of new fabrication and coating technology, tool insert like TiAlN coated carbide is receiving increasing attention from both industrial and research communities. Coated carbide tools enjoy lower price than CBN tools, (normally used for hard machining) but have a shorter tool life with lower material removal in comparison to PCBN. Early prediction of tool wear during high speed machining by coated carbide is quite important since high tool wear has an adverse effect on surface finish, which is considered to be the major quality criterion of finished part. In this context, in present study, an appropriate model for effective prediction of tool life has been developed during the high speed end milling of H13 tool steel using PVD-TiAlN coated tool inserts. RSM is a statistical method that combines design of experiments, regression analysis and statistical inferences [4]. RSM also reduces total number of trials needed to generate the experimental data in order to response model. The application of RSM in machining parameter optimization was first reported to be used by SM.Wu, 1965. Since then many researchers have been using this technique to design their experiments and model the responses. Alauddin et al. [5] used RSM to optimise the surface finish in end milling of Inconel 718 under dry condition. They developed contours to select a combination of cutting speed, and feed without increasing the surface roughness. Öktem et al. [6] incorporated RSM with developed genetic algorithm to optimize cutting parameters for better surface quality in case of Inconel 718. S. Saikumar and M. S. Shunmugam et al. [7] also combined RSM with differential evolution and genetic algorithms to draw a comparison between these methods. In current study, the model has been developed by RSM in terms of cutting speed (v), feed (f) and axial depth of cut (a). Experimental runs were designed based on the principles of central composite design (CCD) of RSM. Tool life data collected from the experimental trials were used to formulate the RSM models.