Multi-objective Optimization Using Backpropagation Neural Network and Teaching–Learning-Based-Optimization Method in Surface Grinding Under Dry and Minimum Quantity Lubrication Conditions (MQL)
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-0867-5_40
Reference11 articles.
1. Vashista M, Gaddam A, Paul S (2012) Study of surface integrity of ground bearing steel using Barkhausen noise technique. Int J Adv Manuf Technol 63(5):771–783
2. Shao Y, Fergani O, Ding Z, Li B, Liang SY (2015) Experimental investigation of residual stress in minimum quantity lubrication grinding of AISI 1018 steel. J Manuf Sci Eng 138(1)
3. Mia M, Gupta MK, Singh G, Królczyk G, Pimenov DY (2018) An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions. J Clean Prod 187:1069–1081
4. Springer Homepage. https://www.springer.com/gp/book/9783319227313. Accessed 02 Sept 2021
5. Liu C, Ding W, Li Z, Yang C (2017) Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm. Int J Adv Manuf Technol 89(5):2277–2285
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