Modeling and Analysis of the Grinding Parameters

Priya S. Shinde

doi:10.4028/www.scientific.net/AMM.110-116.2548

Paper Titles

Suboptimal Guidance and Control Design for a Missile with Onboard Strapdown Seeker
p.2513

Flow Maldistribution in a Simplified Plate Heat Exchanger Model - A Numerical Study
p.2529

A Modified Wave Approach for the Calculation of Natural Frequencies and Mode Shapes of the Rods with Variable Cross Section
p.2537

Modeling and Analysis of the Grinding Parameters
p.2548

Improved Air Turbo Rocket for Space Applications Application to Orbital Vehicles and Reentry
p.2554

Gear Fault Identification Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System
p.2562

Experimental Study of Cloud Formation
p.2570

Simulation of the Mathematical Model of a Quad Rotor Control System Using Matlab Simulink
p.2577

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 110-116Modeling and Analysis of the Grinding Parameters

Modeling and Analysis of the Grinding Parameters

Abstract:

Efficient grinding of structural ceramics requires judicious selection of operating parameters to maximize removal rate while controlling surface integrity. Grinding of ceramics is difficult because of its low fracture toughness, making it very sensitive to cracking. In the present work, experiments were carried out to study the effect of wheel parameters such as grain size and grinding parameters like depth of cut and feed rate on the surface roughness and surface damage. The significance of the grinding parameters on the selected responses is evaluated using analysis of variance. Mathematical statistics like “Minitab” is used to analyzed the grinding conditions for maximum material removal, using a multi-objective function model, by imposing surface roughness, surface force and surface damage constraints. The choice of including manufacturer & apposes constraints on the basis of functional requirements of the component for maximizing the production rate is also embedded in the Mathematical statistics. For the verification of present work, the Mathematical statistics results are compared with experimental work.

You might also be interested in these eBooks

Mechanical and Aerospace Engineering, ICMAE2011

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 110-116)

Pages:

2548-2553

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.110-116.2548

Citation:

Cite this paper

Online since:

October 2011

Authors:

Priya S. Shinde

Keywords:

ANOVA, Grinding Parameters, Minitab, Surface Damage, Surface Roughness (SR), Tangential Force

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] H.K. Tonshoff J. Peters, I. Inasaki, T. Paul, Modelling and Simulation of Grinding Processes, Annals of the CIRP 41 (2), 2003, pp.677-688.

DOI: 10.1016/s0007-8506(07)63254-5

Google Scholar

[2] T.W. Liao, L.J. Chen, A Neural Network Approach for Grinding Processes: Modeling and Optimization, International Journal of Machine Tools and Manufacture 34 (7), 1994, pp.919-937.

DOI: 10.1016/0890-6955(94)90025-6

Google Scholar

[3] R.K. Jain, V.K. Jain, Optimum Selection of Machining Conditions in Abrasive Flow Using Neural Networks, Journal of Materials Processing Technology 108, 2000, pp.62-67.

DOI: 10.1016/s0924-0136(00)00621-x

Google Scholar

[4] P.V.S. Suresh, P. Venkateswara Rao, S.G. Deshmukh, A Genetic Algorithmic Approach for Optimization of Surface Roughness Prediction Model, International Journal of Machine Tools and Manufacture 42, 2002, pp.675-680.

DOI: 10.1016/s0890-6955(02)00005-6

Google Scholar

[5] E.J.A. Armarego, R.H. Brown, The Machining of Metals, Prentice Hall, New Jersey, 1984, pp.254-291.

Google Scholar

[6] D.E. Goldberg, Genetic Algorithms, Addison Wesley Longman Inc, India, (1999).

Google Scholar

[7] D.C. Montgomery, Design and Analysis of Experiments, John Wiley and Sons (Asia) Pvt. Ltd, Singapore, (2001).

Google Scholar