Abstract
This experimental study presents a combination of Machine learning and Artificial Neural Network to solve a multiple objective optimization problem in finishing milling of Titanium Alloys Ti-6Al-4V under a minimum quantity lubrication (MQL) environment. The three milling technological parameters, such as feed per tooth, cutting speed and depth of cut; and two lubrication parameters including air pressure and lubricant flow rate are considered as the variants. The approach of this work is to minimize the cut value of surface roughness and maximize the production rate at the same time. The Support Vector Machine (SVM) is applied to generate the regression vectors, then the artificial neural algorithm Non-dominated Sorting Genetic Algorithm (NSGA II) is used to find the optimum technological and lubrication input. The ANOVA analysis is also used to predict the influence of input factors on surface roughness and production rate.
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The experimental study work is funded by the Ministry of Education & Training (MoET) under grant number B2021-BKA-11.
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Nguyen, VC., Hoang Tien, D., Pham, VH., Nguyen, TD. (2022). Investigation and Optimization of Surface Roughness and Material Removal Rate in Face Finishing Milling of Ti-6Al-4V Under MQL Condition. In: Le, AT., Pham, VS., Le, MQ., Pham, HL. (eds) The AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering. RCTEMME 2021. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-1968-8_68
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