Abstract
Micro-Milling refers to a basic end-milling process using tools up to 1 mm in diameter. The geometry that can be produced by micro-end-milling is more flexible than those produced by lithography and other traditional micro manufacturing techniques. Furthermore, a wide range of materials could be processed using micro end milling. This chapter is based on the optimization of process parameters of micro milling performed on polymethyl methacrylate (PMMA) workpiece.
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
Bao W, Chen P, Tansel I, Reen NS, Yang S, Rincon D (2003) Selection of optimal cutting conditions by using the genetically optimized neural network system (GONNS). In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) Artificial neural networks and neural information processing—ICANN/ICONIP 2003. ICANN 2003, ICONIP 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg
Gopalsamy BM, Mondal B, Ghosh S (2009) Taguchi method and ANOVA: An approach for process parameters optimization of hard machining while machining hardened steel
Hayajneh MT, Tahat MS, Bluhm J (2007) A study of the effects of machining parameters on the surface roughness in the end-milling process. Jordan J Mech Ind Eng 1(1):1–5
Jain VK (2008) Advanced (non-traditional) machining processes. In: Machining. Springer, London, pp 299–327
Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self supervised learning behavior. In: 2013 IEEE international conference on systems, man, and cybernetics. IEEE, pp 1396–1400
Kumar SL, Jerald J, Kumanan S, Aniket N (2014) Process parameters optimization for micro end-milling operation for CAPP applications. Neural Comput Appl 25(7–8):1941–1950
Lu X, Jia Z, Wang H, Si L, Liu Y, Wu W (2016) Tool wear appearance and failure mechanism of coated carbide tools in micro-milling of Inconel 718 super alloy. Ind Lubric Tribol 68(2):267–277
Patankar NS, Kulkarni AJ (2018) Variations of cohort intelligence. Soft Comput 22(6):1731–1747
Pham DT, Elkaseera AM, Popova KP, Dimova SS, Olejnikc L, Rosochowskid A (2007) An experimental and statistical study of the factors affecting surface roughness in the micro milling process. In: Innovative Production machines and systems
Rahman M, Kumar AS, Prakash JRS (2001) Micro milling of pure copper. J Mater Process Technol 116(1):39–43
Saravanan M, Ramalingam D, Manikandan G, Kaarthikeyen RR (2012) Multi objective optimization of drilling parameters using genetic algorithm. Procedia Eng 38:197–207
Shastri AS, Kulkarni AJ (2018) Multi-cohort intelligence algorithm: an intra-and inter-group learning behaviour based socio-inspired optimisation methodology. Int J Parallel Emergent Distrib Syst 33(6):675–715
Shastri AS, Nargundkar A, Kulkarni AJ (2020) Multi-Cohort intelligence algorithm for solving advanced manufacturing process problems. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04858-y
Zain AM, Haron H, Sharif S (2011) Estimation of the minimum machining performance in the abrasive water jet machining using integrated ANN-SA. Expert Syst Appl 38(7):8316–8326
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Shastri, A., Nargundkar, A., Kulkarni, A.J. (2021). Optimization of Micro Milling Process. In: Socio-Inspired Optimization Methods for Advanced Manufacturing Processes. Springer Series in Advanced Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-15-7797-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-7797-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7796-3
Online ISBN: 978-981-15-7797-0
eBook Packages: EngineeringEngineering (R0)