PREDICTING SURFACE EXCELLENCE USING PARAMETRIC DESIGN CONCEPT: A PRACTICAL APPROACH WITH MATHEMATICAL MODEL

Authors

  • Nataraj M Department of Mechanical Engineering, Government college of Technology, Coimbatore, TamilNadu, 641013 - India

Keywords:

Regression Model, OA, DoE, Surface Roughness, CNC Machining, Parametric Design

Abstract

This paper discusses empirical model development to predict surface roughness of components machined in CNC turning centre via parametric design concept. Process variables selected in parametric design are spindle speed, feed rate, cutter nose radius and depth of cut. Non linear regression analysis with logarithmic data transformation is used for the model development. The near optimum combination of machining parameters for the best surface roughness is achieved using Design of Experiments. Confirmation trial runs are conducted to get foolproof results. The regression model is validated with a case study

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References

Onwubolu G C (2005), “A Note on Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools”, American Journal of Applied Sciences, Vol. 2 (60), 1109- 1112.

Yang J L and Chen J C (2001), “A Systematic Approach for Identifying Optimum Surface Roughness Performance in End Milling Operations”, Journal of Industrial Technology, Vol. 17(2), 1-8.

Feng C X (2001), “An Experimental Study of the Impact of the Turning Parameters on Surface Roughness”, Proceedings of the Industrial Engineering Research Conference: Institute of Industrial Engineers, paper no. 2036.

Lou M S, Chen J C and Li C M (1998), “Surface Roughness Prediction Technique for CNC End Milling”, Journal of Industrial Technology, Vol. 15 (1), 1-6.

Huang L and Chen J C (2001), “Multiple Regression Model to Predict In-process Surface Roughness in Turning Operation via Accelerometer”, Journal of Industrial Technology, Vol. 17 (2), 1-8.

Savage M D and Chen J C (1999), “Effects of Tool Diameter Variations in On-Line Surface Roughness Recognition System”, Journal of Industrial Technology, Vol. 15(4), 1-7.

Mounayri H E, Kishawy H and Tandon V (2002), “Optimized CNC End Milling: A Practical Approach”, International Journal of Computer Integrated Manufacturing, Vol. 15 (5), 453-470.

Pawade R S, Joshi S S and Brahmankar P K(2008), “Effect of Machining Parameters and Cutting Edge Geometry on Surface Integrity of High-Speed Turned Inconel 718”, International Journal of Machine Tools and Manufacture, Vol. 48(1), 15–28.

Nataraj M (2006), “Studies on Process and Design Optimization of Typical Products Using Design of Experiments Approach”, Ph D Thesis, Bharathiar University, India.

Wu Y (1989), “Taguchi methods”, Case Studies from the US and Europe, ASI Press, Dearborn, Michigan.

Belavendram N (1999), “Quality by Design”, McGraw Hill, Prentice Hall.

Chen W (1996), “A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors”, ASME Journal of Mechanical Design Vol. 118, 478–485.

Kacker R N (1985), “Off-line Quality Control Parameter Design and the Taguchi Method”, Journal of Quality Technology Vol. 17 (4), 176–188.

Nataraj M, Arunachalm V P and Balaji B (2008), “A Practical Approach to Optimize the Coating Parameters to Win Customer Confidence”, Proc. Instn. Mech. Engrs, Part B; Journal of Engineering Manufacture, Vol. 222 (B4), 495 – 506.

Nataraj M, Arunachalam V P and Ranganathan G (2006), “Risk Analysis of to Find the Near Optimum Combination of Design Parameters Using Taguchi’s Robust Design Method for Quality Improvement and Functional Reliability: A Case Study With Concurrent Engineering Approach on an Auto Electrical Part”, International Journal of Advanced Manufacturing Technology, Vol, 27 (5-6), 445-454.

Nataraj M, Arunachalm V P and Dhandapni N (2005), “Optimizing Diesel Engine Parameters for Low Emissions Using Taguchi Method: Variation Risk Analysis Approach - Part I”, Indian Journal Engineering & Material Science, Vol. 12, 169-181.

Nataraj V P, Arunachalm N and Dhandapni (2005), “Optimizing Diesel Engine Parameters for Emission Reduction Using Taguchi Method: Variation Risk Analysis Approach - Part II”, Indian Journal Engineering & Material Science, Vol. 12, 505-514.

Nataraj M and Arunachalm V P (2006), “Optimizing Impeller Geometry for Performance Enhancement of a Centrifugal Pump Using Taquchi Quality Concept”, Proc. Instn. Mech. Engrs, Part A Journal of Power Energy, Vol. 220 (A7), 765-782.

Nataraj M, Arunachalm V P and Suresh K G (2006), “Optimizing Planer Cam Mechanism in Printing Machine for Quality Improvement Using Taguchi Method: Risk Analysis With Concurrent Engineering Approach”, International Journal of Computer Applications in Technology, Vol. 26(3), 164-173.

Richard A J (2002), “Probability and Statistics for Engineers, Miller & Freund’s, Prentice Hall Private Limited”.

Bendell T (1989), “Taguchi Methods”, First European Conference papers, Elsevier, Amsterdam.

Taguchi G (1986), “Introduction to Quality Engineering”, Asian Productivity Organization, UNIPUB, New York.

Park S H (1996), “Robust Design and Analysis for Quality Engineering”, Chapman and Hall, London.

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Published

2010-09-01

How to Cite

[1]
“PREDICTING SURFACE EXCELLENCE USING PARAMETRIC DESIGN CONCEPT: A PRACTICAL APPROACH WITH MATHEMATICAL MODEL”, JME, vol. 5, no. 3, pp. 170–176, Sep. 2010, Accessed: Nov. 22, 2024. [Online]. Available: https://smenec.org/index.php/1/article/view/466

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