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Prediction of Surface Roughness Based on Machining Condition and Tool Condition in Boring Stainless Steel-304
Background/Objectives: Modern manufacturing industries aim to increase production rate with less production cost and high quality. To achieve high production rate with minimum cost, machining parameters must be optimized in industries. Therefore, the main objective of this study is to establish the relationship between the influence of cutting parameters and surface roughness in dry boring operation. Methods/Statistical analysis: A full factorial design was used to evaluate the effect of four independent variables (feed rate, spindle speed, depth of cut, tool flank wear). Stainless steel 304 was selected as a work piece due to high hardness, chemical stability and various applications. Carbide tipped tool insert was used for machining. Findings: During machining, Statistical features were extracted from the vibration signal. The extracted statistical features, machining condition and tool flank wear were considered to establish the various surface roughness prediction models. Multilayer perceptron and decision tree models were developed to predict the surface roughness. From these two models, the best suitable prediction model was selected based on maximum correlation coefficient and minimum root mean squared error values. Application/Improvements: The selected best model can be used for variety of machining conditions to predict the surface roughness of the machined surface.
Cutting Parameters, Decision Tree, Flank Wear, Linear Regression, Vibration Signals.
- Yousssef A. Investigation of cutting parameter effects on surface roughness in lathe boring operation by use of a full factorial design. Computers & Industrial Engineering.1996; 31(3):645–51.
- Egashia K, Iwata M, Nomura Y. Boring and face grooving using micro turning tools. CIRP Annals-Manufacturing Engineering. 2011; 60(6);81–4.
- Chern G, Liang J. Study on boring drilling with vibration cutting. International Journal of Machine Tools &Manufacture. 2007; 47(6):133–40.
- Acayaba GMA, Escalona PMD. Prediction of surface roughness in low speed turning of American Iron and Steel Institute (AISI) 316 austenitic stainless steel. Manufacturing Science and Technology. 2015; 1(2):62–7.
- Andren, Hakansson L, Brandt A, Claesson I. Identification of dynamic properties of boring bar vibration in a continuous boring operation. Mechanical Systems and Signals Processing. 2004; 18(5):869–901.
- Rao KV, Murthy BSN, Rao MN. Cutting tool condition monitoring by analyzing surface roughness, Work Piece Vibration and Volume of Metal removed for AISI 1040 Steel in Boring. Measurement. 2013; 46(10):4075–84.
- Nayak SK, Patro JK, Dewangan S, Gangopadhyay SS. Multiobjective optimization of machining parameters during dry turning of American Iron and Steel Institute (AISI) 304 austenitic stainless steel using grey relational analysis. Procedia Materials Science. 2014; 6(5):701–8.
- Paul PSS, Varadarajan AS, Gnanadurai RR. Study on the influence of fluid application parameters on tool vibration and cutting performance during turning of hardened steel.Engineering Science and Technology. 2015 Mar; 19:241–53.
- Das B, Roy S, Rai RN, Saha SC. Studies on effect of cutting parameters on surface roughness Ol Al-Cu-Tic Mmcs: an ANN approach. Procedia Computer Science. 2015; 45:745– 52.
- Qehaja NN, Jakupi KK, Bunjaku AA, Brucj MM, Osmani HH. Effect of machining parameters and machining time on surface roughness in dry turning process. Procedia Engineering. 2015; 100:135–40.
- Beauchamp Y, Thomas M. Investigation of cutting parameters effects on surface roughness in lathe boring operating by use of a full factorial design. Computers & Industrial Engineering. 1996; 31(3–4):645–51.
- Sangwan KS, Saxena SS, Kant G. Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP. 2015; 29:305–10.
- Benardos PG, Vosniakos VGC. Predicting surface roughness in machining: a review. International Journal of Machine Tools and Manufacturing. 2003; 43(8):833–44.
- Anthiony X, Adithan M. Determining the influence of cutting fluids on tool wear and surface roughness during turning of American Iron and Steel Institute (AISI) 304 austenitic stainless steel. Journal of Materials Processing Technology. 2009; 209(2):900–9.
- Pandit SM, Subramanian TL, Wu W. Modeling machine tool chatter by time series. Journal of Engineering for Industry. 1975; 97(1):211–5.
- Pandit SM, Subramanian TL, Wu W. Stability of random vibrations with special reference to machine tool chatter.Journal of Engineering for Industry. 1975; 97(1):216–9.
- Parker EW. Dynamic stability of a cantilever boring bar with machined flats under regenerative cutting conditions.Journal of Engineering for Industry. 1970; 12(1):104–15.
- Bendat JS, Piersol AG. Random data analysis and measurement procedures. 3rd edition, Wiley, New York; 2000.
- Sturesson P, Hakansson L, Claesson I. Identification of the statistical properties of the cutting tool vibration in a continuous turning operation – correlation to structural properties. Journal of Mechanical Systems and Signals Processing. 1997; 11(3):459–89 .
- Bendat JS. Random data analysis and measurement procedures.Wiley, New York; 1986.
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