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Year 2015, Volume: 12 Issue: 2, - , 01.11.2015

Abstract

References

  • [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.

Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications

Year 2015, Volume: 12 Issue: 2, - , 01.11.2015

Abstract

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.

References

  • [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.
There are 42 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Oluwaseyi A. Ajibade This is me

Johnson O. Agunsoye This is me

Sunday A. Oke This is me

Publication Date November 1, 2015
Published in Issue Year 2015 Volume: 12 Issue: 2

Cite

APA Ajibade, O. A., Agunsoye, J. O., & Oke, S. A. (2015). Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications. Cankaya University Journal of Science and Engineering, 12(2).
AMA Ajibade OA, Agunsoye JO, Oke SA. Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications. CUJSE. November 2015;12(2).
Chicago Ajibade, Oluwaseyi A., Johnson O. Agunsoye, and Sunday A. Oke. “Metal Removal Process Optimisation Using Taguchi Method - Simplex Algorithm (TM-SA) With Case Study Applications”. Cankaya University Journal of Science and Engineering 12, no. 2 (November 2015).
EndNote Ajibade OA, Agunsoye JO, Oke SA (November 1, 2015) Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications. Cankaya University Journal of Science and Engineering 12 2
IEEE O. A. Ajibade, J. O. Agunsoye, and S. A. Oke, “Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications”, CUJSE, vol. 12, no. 2, 2015.
ISNAD Ajibade, Oluwaseyi A. et al. “Metal Removal Process Optimisation Using Taguchi Method - Simplex Algorithm (TM-SA) With Case Study Applications”. Cankaya University Journal of Science and Engineering 12/2 (November 2015).
JAMA Ajibade OA, Agunsoye JO, Oke SA. Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications. CUJSE. 2015;12.
MLA Ajibade, Oluwaseyi A. et al. “Metal Removal Process Optimisation Using Taguchi Method - Simplex Algorithm (TM-SA) With Case Study Applications”. Cankaya University Journal of Science and Engineering, vol. 12, no. 2, 2015.
Vancouver Ajibade OA, Agunsoye JO, Oke SA. Metal Removal Process Optimisation using Taguchi Method - Simplex Algorithm (TM-SA) with Case Study Applications. CUJSE. 2015;12(2).