Abstract
Surface roughness is an important physical quantity which can influence the mechanical properties of materials, lubrication properties and fatigue strength. In most cases, it is a technical requirement for mechanical products and an index of performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205(1–3):439–450
Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8):833–844
Ramesh S, Karunamoorthy L, Palanikumar K (2012) Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Measurement 45(5):1266–1276
Hanief M, Wani MF (2015) Modeling and prediction of surface roughness for running-in wear using Gauss-Newton algorithm and ANN. Appl Surf Sci 357:1573–1577
Asiltürk I, Neşeli S, Ince MA (2016) Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement 78:120–128
Sangwan KS, Saxena S, Kant G (2015) Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP 29:305–310
Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robotics Comput Integr Manuf 18(5–6):343–354
Razfar MR, Farshbaf Zinati R, Haghshenas M (2011) Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm. Int J Adv Manuf Technol 52(5):487–495
Stout KJ, Davis EJ, Sullivan P (2010) Atlas of machined surfaces. Springer Science and Business Media
Kurella A, Dahotre NB (2005) Surface modification for bioimplants: the role of laser surface engineering. J Biomater Appl 20(1):5–50
Gandhi R, Moreno C, Wang Z et al (2014) Novel methods for scalable manufacturing of micro-textured surfaces. In: IIE Annual conference. proceedings. institute of industrial and systems engineers (IISE), 3757
Mann JB, Guo Y, Saldana C et al (2011) Enhancing material removal processes using modulation-assisted machining. Tribol Int 44(10):1225–1235
Mann JB, Saldana CJ, Guo Y et al (2013) Effects of controlled modulation on surface textures in deep-hole drilling. Int J Mater Manuf 6(1):24–32
Altintas Y, Ber AA (2001) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Appl Mech Rev 54(5):B84–B84
Friedrich CR, Kulkarni VP (2004) Effect of workpiece springback on micromilling forces. Microsyst Technol 10(6–7):472–477
Guo P (2014) In: Development of the elliptical vibration texturing process. Northwestern University
Boothroyd G (1989) Non-conventional machining processes. Fundamentals of Machining and Machine Tools 491
Dong WP, Sullivan PJ, Stout KJ (1992) Comprehensive study of parameters for characterizing three-dimensional surface topography I: Some inherent properties of parameter variation. Wear 159(2):161–171
Mereuta V, Buciumeanu M, Palaghian L (2013) 3D roughness parameters as factors in determining the evolution of effective stress concentration factors in fatigue processes. Appl Mech Mater 248:504–510
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2023 Huazhong University of Science and Technology Press
About this chapter
Cite this chapter
Bai, W., Gao, Y., Sun, R. (2023). Surface Topography and Roughness in Vibration Assisted Machining. In: Vibration Assisted Machining. Research on Intelligent Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-19-9131-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-19-9131-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9130-1
Online ISBN: 978-981-19-9131-8
eBook Packages: EngineeringEngineering (R0)