Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

To improve machining quality and processing efficiency, the Taguchi analysis method is employed to design the milling tests of titanium alloy TC17. According to results based on the signal-to-noise ratio method, the cutting depth plays a critical role in improving the surface roughness and tool wear. The grey correlation analysis is a multi-objective optimization method that can help to acquire process parameters combination of the optimal surface roughness and the optimal tool wear. Finally, the correctness of multi-objective optimization results is verified through comparison experiments. The research results can provide process guidance and data reference for the actual production processing.

[1]  Dinghua Zhang,et al.  Formation mechanism of surface metamorphic layer and influence rule on milling TC17 titanium alloy , 2021, The International Journal of Advanced Manufacturing Technology.

[2]  A. T. Abbas,et al.  Towards Optimization of Surface Roughness and Productivity Aspects during High-Speed Machining of Ti–6Al–4V , 2019, Materials.

[3]  Funda Kahraman,et al.  Modeling and optimization for fly ash reinforced bronze-based composite materials using multi objective Taguchi technique and regression analysis , 2018, Industrial Lubrication and Tribology.

[4]  Yusuf Fedai,et al.  Optimization of machining parameters in face milling using multi-objective Taguchi technique , 2018, Tehnički glasnik.

[5]  S. N. Joshi,et al.  Measurement and analysis of cutting force and product surface quality during end-milling of thin-wall components , 2018, Measurement.

[6]  H. Ghorbani,et al.  Multi-objective optimization of parameters in turning of N-155 iron-nickel-base superalloy using gray relational analysis , 2018 .

[7]  Yusuf Fedai,et al.  Modeling and optimization of face milling process parameters for aisi 4140 steel , 2018 .

[8]  T. Aktas,et al.  Changing of viscosity and thermal properties of olive oil with different harvesting methods and waiting period , 2018 .

[9]  L. Prabhu,et al.  Optimization of Machining Parameters for Surface Roughness in End Milling of Magnesium AM60 Alloy , 2017 .

[10]  Dinghua Zhang,et al.  Optimization of process parameters for surface roughness and microhardness in dry milling of magnesium alloy using Taguchi with grey relational analysis , 2015, The International Journal of Advanced Manufacturing Technology.

[11]  Hasan Kurtaran,et al.  Application of response surface methodology in the optimization of cutting conditions for surface roughness , 2005 .

[12]  M. Boujelbene,et al.  Optimization of the surface roughness in ball end milling of titanium alloy Ti-6Al-4V using the Taguchi Method , 2018 .

[13]  Girish Kant,et al.  Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm , 2015 .

[14]  Wu Jian-ju,et al.  Robust optimization design of flexible hinge flexibility based on Taguchi method , 2015 .

[15]  Amit Banerjee,et al.  Analysis of Cutting Forces and Optimization of Cutting Parameters in High Speed Ball-end Milling Using Response Surface Methodology and Genetic Algorithm , 2014 .

[16]  V. Krishnaraj,et al.  Machining Parameters Optimization in End Milling of Ti-6Al-4 V☆ , 2013 .