Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications
Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications
In the metal removal process industry, the current practice to optimise cutting parameters adopts a conventional method. It is based on trial and error, in which the machine operator uses experience, coupled with handbook guidelines to determine optimal parametric values of choice. This method is not accurate, is time-consuming and costly. Therefore, there is a need for a method that is scientific, costeffective and precise. Keeping this in mind, a different direction for process optimisation is taken by employing the combined Taguchi method-simplex algorithm (TM-SA) for optimal parametric setting of manufacturing processes. The process parameters were optimised and the efficiency and robustness of the method described in four literature cases. These cases involve high-speed flat-end milling, forming in hydrodynamic deep drawing, cup deep drawing and abrasive assisted drilling. The computations showed that the TM-SA exhibited superior results in one of the cases and equivalent results in others. This implies that the proposed approach could comparably serve as an optimisation framework with significant advantages of reducing experimental costs and allowing variable usages with the requirement of functional derivation. It is also easy to use. The novelty of this article is the application of a distinctly new method in optimisation for cost reduction and variable usages for the metal removal process. Potential applications of the proposed approach by material type is its usage in machining mild steel, grey cast iron, brass and aluminium with HSS and carbon steel, respectively, used as tools.
___
- [1] N. Qehaja, K. Jakupi, A. Bunjaku, M. Bruci, H. Osmani, Effect of Machining
Parameters and Machining Time on Surface Roughness in Dry Turning Process,
Procedia Engineering, 100, (2015), 135-140.
- [2] D. Goswani, S. Chakraborty, Parametric 0ptimisation of Ultrasonic Machining
Process Using Gravitational Search and Fireworks Algorithm, Ain Shams
Engineering Journal, 6, (2015), 315-331.
- [3] H. S. Lu, C. K. Chang, N. C. Hwang and C. T. Chung, Grey Relational Analysis
Coupled with Principal Component Analysis for Optimisation Design of the Cutting
Parameters in High-Speed Milling, Journal of Material Processing Technology, 209,
(2009), 3808-3817.
- [4] T. Zhang, O. Owodunni, J. Gas, Scenarios in Multi-Objective Optimisation of
Process Parameters for Sustainable Machining, Procedia CIRP, 26, (2015), 373-378.
- [5] H. M. B. De Calvarho, J. de Oliver Gomes, Energy Efficiency Evaluation for
Machining Process in Flexible Manufacturing Systems-A Case Study, Procedia
CIRP, (2015), 29, 104-108.
- [6] S. Dambhare, S. Deshmukh, A. Borade, A. Digalwar, M. Plate, Sustainability
Issues in Turning Processes: A Study in Indian Machining Industry, Procedia CIRP,
26, (2015), 379-384.
- [7] Y.–C. Yen, J. Sohner, B. Lilly, T. Altan, Estimation of Tool Wear in Orthogonal
Cutting Using the Finite Element Analysis, Journal of Materials Processing
Technology, 146, (2004), 82–91.
- [8] R. Neugebauer, W. Drossel, R. Wertheim, C. Hochmuth, M. Dix, Resource and
Energy Efficiency in Machining Using High-Performance and Hybrid Processes,
Procedia CIRP, 1, (2012), 3–16.
- [9] Y.-C. Wang, A Note on Optimization of Multi-Pass Turning Operations Using
Ant Colony Systems, International Journal of Machine Tools and Manufacture, 47,
(2007), 2057 – 2059.
- [10] K. Vijayakumar, G. Prabhaharan, P. Asokan, R. Saravan, Optimization of MultiPass
Turning Operations Using Ant-Colony System, International Journal of Machine
Tools and Manufacture, 43(15), (2003), 1633–1639.
- [11] S. K. Ong, M. J. Sun, A. Y. C. Nee, A Fuzzy Set AHP-Based DFM Tool for
Rotational Parts, Journal of Materials Processing Technology, 138, (2003), 223–230.
- [12] Y. M. Ali, L. C. Zhang, A Fuzzy Model for Predicting Burns in Surface
Grinding of Steel, International Journal of Machine Tools and Manufacture, 44,
(2004), 563–571.
- [13] M. C. Kayacan, I. H. Filiz, A. I. Sonmez, A. Baykasoglu, T. Dereli, OPPS –
ROT: An Optimized Process Planning System for Rotational Parts, Composite in
Industry, Vol. 32, (1996), 181–185.
- [14] S. Avci and M. S. Akturk, Tool Magazine Arrangement and Operations
Sequencing on CNC Machines, Computers and Operations Research, 23(11), (1996),
1069–1081.
[15] Y.-C. Wang, Y.-C. Chin, Y.-P. Hung, Optimization of Multi-Task Turning
Operations Under Minimal Tool Waste Consideration, Robotics and ComputerIntegrated
Manufacturing, 27, (2011), 674–680.
- [16] G. Quintana and J. Ciurana, Cost Estimation Support Tool for Vertical High
Speed Machines Based on Product Characteristics and Productivity Requirements,
International Journal of Production Economics, 134, (2011), 188–195.
- [17] C. Vila, H. R. Siller, C. A. Rodriguez, G. M. Bruscas, J. Serrano, Economical
and Technological Study of Surface Grinding Versus Face Milling in Hardened AISI
D3 Steel Manufacturing Operations, International Journal of Production Economics,
138, (2012).
- [18] U. Zuperl, F. Cus, B. Mursec, T. Ploj, A Hybrid Analytical-Neural Network
Approach to the Determination of Optimal Cutting Conditions, Journal of Materials
Processing Technology, 157-158, (2004), 82–90.
- [19] F. Cus, M. Milfelner, J. Balic, An Intelligent System for Monitoring and
Optimization of Ball-End Milling Process, Journal of Materials Processing
Technology, 175, (2006), 90–97.
- [20] F. Cus and J. Balic, Optimization of Cutting Process by GA Approach, Robotics
and Computer Integrated Manufacturing, 19, (2003), 113 – 121.
- [21] M. S. Shunmugan, Bhaskara-Reddy, T. T. Narendran, Selection of Optimal
Conditions in Multi-Pass Face-Milling Using a Genetic Algorithm, International
Journal of Machine Tools and Manufacture, 40, (2000), 401-414.
- [22] A. R. Yildiz, A Comparative Study of Population-Based Optimization
Algorithms for Turning Operations, Information Sciences, 210, (2012), 81–88.
- [23] P. E. Amiolemhen, A. O. A. Ibhadode, Application of Genetic Algorithms:
Determination of the Optimal Machining Parameters in the Conversion of a
Cylindrical Bar Stock into a Continuous Finished Profile, International Journal of
Machine Tools and Manufacture, 44, (2014), 1403–1412.
- [24] S. H. Yeoh, A Multi-Pass Optimization Strategy for CNC Lathe Operations,
International Journal of Production Economics, 40, (1995), 209–218.
- [25] El-Gallab and M. Sklad, Machining of Al/Sic Particulate Metal-Matrix
Composites Part I: Tool Performance, Journal of Materials Processing Technology,
83, (1998), 151–158.
- [26] S. V. Bhaskara-Reddy, M. S. Shunmugan, T. T. Narendran, Optimal SubDivision
of the Depth-of-Cut to Achieve Minimum Production Cost in Multi-Pass
Turning Using a Genetic Algorithm, Journal of Materials Processing Technology, 79,
(1998), 101–108.
- [27] Y.–H. Chen, Y.–S. Lee and S.–C. Fang, Optimal Cutter Selection and
Machining Plane Determination for Process Planning and NC Machining of
Complex Surfaces, Journal of Manufacturing Systems, 17(5), (1998), 371–388.
- [28] D. M. D’Addona and R. Teti, Genetic Algorithm-Based Optimization of Cutting
Parameters in Turning Processes, Procedia CIRP, 7, (2013), 323–328.
- [29] M. S. Akturk and S. Avci, Tool Allocation and Machining Conditions
Optimization for CNC Machines, European Journal of Operations Research, 94,
(1996), 335–348.
- [30] G. C. Onwubolu, Performance-Based Optimization of Multi-Pass Face Milling
Operations Using Tribes, International Journal of Machine Tools and Manufacture,
46, (2006), 717–727.
- [31] Q. Meng, J. A. Arsecularatine, P. Matthew, Calculation of Optimum Cutting
Conditions for Turning Operations Using a Machining Theory, International Journal
of Machine Tools and Manufacture, 40, (2000), 1709–1733.
- [32] M. Tolouei-Rad and I. M. Bidhendi, On The Optimization of Machining
Parameters for Milling Operations, International Journal of Tools and Manufacture,
37(1), (1996), 1–16.
- [33] M. Sortino, S. Belfio, G. Totis, An Innovative Approach for Automatic
Generation, Verification and Optimization of Part Programs in Turning, Journal of
Manufacturing Systems (in press), (2014).
- [34] A. R Yildiz. Hybrid Taguchi-Differential Evolution Algorithm for Optimization
of Multi-Pass Turning Operations, Applied Soft Computing, 13, (2013), 1433–1439.
- [35] Y.-M.Chiang, H.-H. Hsieh, The Use of the Taguchi Method with Grey
Relational Analysis to Optimize the Thin-Film Sputtering Process with Multiple
Quality Characteristic in Color Filter Manufacturing, Computers and Industrial
Engineering, 56(2), (2009), 648-661.
- [36] G. Taguchi, Introduction to Quality Engineering, Asian Productivity
Organization, Tokoyo, Japan, (2009).
- [37] J. L. Lin and C. L. Lin, The Use of Orthogonal Array with Grey Relational
Analysis to Optimize the Electrical Discharge Machining Process with Multiple
Performance Characteristics, International Journal of Machine Tools and
Manufacture, 42, (2002), 237-244.
- [38] O. A Ajibade., J. O. Agunsoye and S. A. Oke, A Comparative Analysis of Three
Optimisation Approaches to Free Swell Characterization of Particulate Coconut
Shell Reinforcement Composite Material, Engineering Journal, (accepted for
publication), (2015).
- [39] B. Zareh, A.H. Gorji, M. Bakhshi and S. Nourouzi, Study on the Effect of
Forming Parameters in Sheet Hydrodynamic Deep Drawing Using FEM-Based
Taguchi Method, International Journal of Advanced Design and Manufacturing
Technology, 6(1), (2013), 87–99.
- [40] H. A. Taha, Operations Research: An Introduction, Macmillan Publishing Co.
Inc. New York, USA, (1982).
- [41] B. Ozcelik and M. Bayramoglu, The Statistical Modeling of Surface Roughness
in High-Speed Flat End Milling, International Journal of Machine Tools &
Manufacture, 46, (2006), 1395–1402.
- [42] A. F. Arezodar and A. Eghbali, Evaluating the Parameters Affecting the
Distribution of Thickness in Cup Deep Drawing of ST14 Sheet, International
Conference on Advances in Systems Theory, Signal Processing and Computational
Science, Istanbul, (2012), 193–197.
- [43] K. K. Goyal, V. Jain and S. Kumari, Prediction of Optimal Process Parameters
for Abrasive Assisted Drilling of SS304, Procedia Materials Science, 6, (2014) 1572–
1579.