Abstract
Micro-electric discharge machining is a non-conventional electrothermal process used to produce microfeatures with precision and accuracy on difficult-to-cut materials. Efforts are made all over the globe for making the µ-EDM process robust for application through optimization. An attempt has been made to optimize the machining parameters of µ-EDM process using JAYA, teaching-learning-based optimization and novel multi-objective optimization techniques. To assess the performance of these algorithms, the results are compared with particle swarm optimization technique. The work focused on drilling of high-aspect-ratio microholes using brass electrode of 290 µm on Stainless Steel 317L (SS317L) as base material and deionized water as dielectric in an advanced EDM drill machine while minimizing surface roughness and overcut simultaneously. Optimum condition was produced at pulse-on time of 3.0776 µs, current of 1 A, servo voltage of 3 V and gap voltage of 30 V. JAYA algorithm produced optimum condition in a smaller number of generations compared to TLBO and PSO. Results showed that JAYA, TLBO and PSO are effective tools to obtain optimum condition for input parameters.
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
Similar content being viewed by others
Abbreviations
- EDM:
-
Electric discharge machining
- µ-EDM:
-
Micro-EDM
- I p :
-
Current (A)
- T on :
-
Pulse-on time (µs)
- SV:
-
Servo voltage (V)
- GV:
-
Gap voltage (V)
- SOO:
-
Single-objective optimization
- TLBO:
-
Teaching-learning-based optimization
- OC:
-
Overcut (µm)
- SR:
-
Surface roughness (µm)
- ANOVA:
-
Analysis of variance
- PSO:
-
Particle swarm optimization
- RSM:
-
Response surface methodology
- MOO:
-
Multi-objective optimization
References
Masuzawa T, Tönshoff HK (1997) Three-dimensional micromachining by machine tools. CIRP Ann 46(2):621–628
Shivraj Narayan Y, Nagabhushana Ramesh N, Balu Naik B, Ramya A (2018) Pulse parameter characterization in microdrilling of maraging steel 300 alloy. In: Li B et al (eds) Characterization of minerals, metals, and materials 2018. TMS 2018, pp 387–398
Oliaei SNB, Jahan MP, Perveen A (2019) Micro-EDM Drilling. In: Kibria G, Jahan M, Bhattacharyya B (eds) Micro-electrical discharge machining processes. Materials forming, machining and tribology. Springer, Singapore
Jahan MP, Wong YS, Rahman M (2010) A comparative experimental investigation of deep-hole micro-EDM drilling capability for cemented carbide (WC-Co) against austenitic stainless steel (SUS 304). Int J Adv Manuf Technol 46:1145–1160
Masuzawa T (2000) State of the art of micromachining. CIRP Ann 49(2):473–488
Mukherjee I, Kumar Ray P (2006) A review of optimization techniques in metal cutting processes. Comput Ind Eng 50:15–34
Ali MY, Rahman B, Adnan N (2016) High aspect ratio micro-EDM drilling with nano surface finish. In: Yacob N, Mohamed M, Megat Hanafiah M (eds) Regional conference on science, technology and social sciences. Springer, Singapore
Ramversingh A, Pradeep (2020) EDM of high aspect ratio micro-holes on Ti-6Al-4V alloy by synchronizing energy interactions. Mater Manuf Process 1–16
Somashekhar KP, Ramachandran N, Jose M (2010) Optimization of material removal rate in micro-EDM using artificial neural network and genetic algorithms. Mater Manuf Process 25:467–475
Maity K, Himanshu (2018) ANN modelling and Elitist teaching learning approach for multi-objective optimization of μ-EDM. J Intell Manuf 29:1599–1616
Zhang L, Jia Z, Wang F et al (2010) A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM. Int J Adv Manuf Technol 51:575–586
Manivannan R, Kumar MP (2016) Multi-response optimization of micro-EDM process parameters on AISI304 steel using TOPSIS. J Mech Sci Technol 30:137–144
Rao RV (2016) Teaching-learning-based optimization algorithm. In: Teaching learning based optimization algorithm. Springer, Cham
Rao RV (2019) Jaya: an advanced optimization algorithm and its engineering applications. Springer International Publishing, Cham
Groysman A (2017) Corrosion failures and solutions at units. In: Corrosion problems and solutions in oil refining and petrochemical industry. Topics in safety, risk, reliability and quality, vol 32. Springer, Cham
Sahu N, Andhare A (2019) Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms. J Comput Des Eng 6:1–12
Jahan M, Yoke S, Rahman (2010) A comparative experimental investigation of deep-hole micro-EDM drilling capability for cemented carbide (WC-Co) against austenitic stainless steel (SUS 304). Int J Adv Manuf Tech 46:1145–116
Wang M, Li W, You C, Wang Qi, Zeng X, Chen M (2017) Triboelectric nanogenerator based on 317L stainless steel and ethyl cellulose for biomedical applications. RSC Adv 7(11):6772–6779
Lee Kwang Y, Jong-Bae P (2006) Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. In: 2006 IEEE PES power systems conference and exposition. IEEE, pp 188–192
Acknowledgements
Authors convey their appreciation to S.N.R.M. EDM Drill works, Balanagar, Hyderabad, for valuable assistance and support in carrying out experiments. Sincere appreciation to ARCI Balapur, Hyderabad, for allowing to use their facilities. Finally, authors acknowledge the support of all faculty and students who were involved in direct or indirect completion of research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajashekar, V., Yeole, S.N. (2022). Multi-objective Optimization of Machining Parameters in µ-EDM Drilling of SS317L Using Novel JAYA and TLBO Algorithms. In: Govindan, K., Kumar, H., Yadav, S. (eds) Advances in Mechanical and Materials Technology . EMSME 2020. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-2794-1_115
Download citation
DOI: https://doi.org/10.1007/978-981-16-2794-1_115
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2793-4
Online ISBN: 978-981-16-2794-1
eBook Packages: EngineeringEngineering (R0)