Development of Mathematical Modeling and its Exploration Based on Genetic Algorithm for Blanking Die Design Parameters Optimization

R.C. Mohan Kumar; C. Velmurugan

doi:10.4028/www.scientific.net/AMM.877.54

Paper Titles

Mesoporous Iron-Manganese Magnetic Bimetal Oxide for Efficient Removal of Cr(VI) from Synthetic Aqueous Solution
p.33

Studies on Notch Tensile Properties of Ti-5Al-2.5Sn ELI at Cryogenic Temperature for Hydrogen Embrittlement Effect
p.39

Application of Composites in Orthotic Calipers and its Experimental Validations
p.44

Study on Mechanical Property of Aluminium 6061 with E Glass Fiber Reinforced Composite
p.50

Development of Mathematical Modeling and its Exploration Based on Genetic Algorithm for Blanking Die Design Parameters Optimization
p.54

Effect of Cast Geometries on Particle Dispersion in Metal Matrix Composites
p.60

Predicting the Deposition Efficiency of Plasma Sprayed Alumina Coatings on AZ31B Magnesium Alloy by Response Surface Methodology
p.66

Characterisation of Micro Channels Machined with ECDM for Fluidic Applications
p.82

FEM of ECDM Process on Semi Conducting Materials
p.87

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 877Development of Mathematical Modeling and its...

Development of Mathematical Modeling and its Exploration Based on Genetic Algorithm for Blanking Die Design Parameters Optimization

Abstract:

The blanking operation have several die design parameters which affect the quality of the blank and its productivity. The main input parameters are sheet thickness and the punch and die clearance and the dependent output parameters are tool life and the burr height. The selected values should be in optimal value. The optimum value is achieved by using the genetic algorithm. The genetic algorithm is an optimization process to find the better results as an output. Then the development of mathematical modeling by using the equations derived from the multiple regression analysis is performed. It is achieved by converting the linear equations into the matrix form and then solving it using mathematical relations. This output is compared with the genetic algorithm results, to get the better results.

You might also be interested in these eBooks

Recent Advances in Materials, Mechanical and Civil Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 877)

Pages:

54-59

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.877.54

Citation:

Cite this paper

Online since:

February 2018

Authors:

R.C. Mohan Kumar, C. Velmurugan*

Keywords:

Blanking, Genetic Algorithm, Mathematical Modeling, Multiple Regression Analysis, Optimization

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] Pankaj, G. Dhoble, Dr. R. R. Deshmukh, Mr. Y. M. Wankhade& Mr. S. D. Yeole, (2015), Optimization of Sheet Metal Blanking Process Parameters Using Taguchi Method and GreyRelationalAnalysis, Indian Research Transaction, Vol. 5, No. 2, pp.100-104.

Google Scholar

[2] Chan. H.Y. and Abdullah. A.B. (2014), Geometrical Defect in Precision Blanking/Punching: A Comprehensive Review on Burr Formation, Research Journal of Applied Sciences, Engineering and Technology, Vol. 8, pp.1139-1148.

DOI: 10.19026/rjaset.8.1078

Google Scholar

[3] Quazi. T.Z., R. Shaikh, &AmolTotre , (May 2013), Blanking process optimization using Taguchi method, International Journal of Engineering Research and Development, Volume 7, Issue 2 , pp.45-51.

Google Scholar

[4] Soumya Subramonian, TaylanAltan, &BogdanCiocirlan (2013), Optimum selection of variable punch-die clearance to improve tool life in blanking non-symmetric shapes, International Journal of Machine Tools & Manufacture, Vol. 75, PP. 63-71.

DOI: 10.1016/j.ijmachtools.2013.09.004

Google Scholar

[5] AmolTotre, Rahul Nishad, &SagarBodke (2013), An Overview of Factors Affecting in Blanking Processes, Vol. 3, pp.390-395.

Google Scholar

[6] Doriana M. D' Addona& Roberto Teti, (2013), Genetic algorithm-based optimization of cutting parameters in turning processes, Procedia CIRP, Vol. 7, p.323 – 328.

DOI: 10.1016/j.procir.2013.05.055

Google Scholar

[7] Rahul Nishad, AmolTotre, SagarBodke and AngadChauhan (2013), An Overview of The Methodologies Used In The Optimization Processes In Sheet Metal Blanking, International Journal of Mechanical Engineering and Robotics Research, Vol. 2, pp.307-314.

Google Scholar

[8] Quazi. T. Z. &R.S. Shaikh, (2012), An Overview of Clearance Optimization in Sheet Metal Blanking Process, International Journal of Modern Engineering Research, Vol. 2, Issue. 6, pp.4547-4558.

Google Scholar

[9] Emad Al-Momani, & Ibrahim Rawabdeh (2008), An Application of Finite Element Method and Design of Experiments in the Optimization of Sheet Metal Blanking Process, Vol. 2, pp.53-63.

Google Scholar

[10] Liu Wei, & Yang yuying (2008), Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm, Journal of materials processing technology, Vol. 2 0 8, pp.499-506.

DOI: 10.1016/j.jmatprotec.2008.01.014

Google Scholar

[11] Ridha Hambli, (June 2005), Optimization of blanking process using neural network simulation, The Arabian Journal for Science and Engineering, Vol 30, PP. 135-139.

Google Scholar

[12] Naceur. H, Y.Q. Guo, J.L. Batoz (2004), Blank optimization in sheet metal forming using an evolutionary algorithm, Journal of Materials Processing Technology, pp.183-191.

DOI: 10.1016/j.jmatprotec.2004.04.036

Google Scholar