Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-31366 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 20 article 911 pages: 123-130

Van Canh Nguyen
Faculty of Mechanical Engineering, Hanoi University of Industry, 298 Cau Dien Street, Hanoi, Vietnam

Ba Nghien Nguyen
Faculty of Information Technology, Hanoi University of Industry, 298 Cau Dien Street, Hanoi, Vietnam

Dung Hoang Tien
Faculty of Mechanical Engineering, Hanoi University of Industry, 298 Cau Dien Street, Hanoi, Vietnam

Van Que Nguyen
Faculty of Mechanical Engineering, Hanoi University of Industry, 298 Cau Dien Street, Hanoi, Vietnam

Xuan Truong Nguyen
Faculty of Mechanical Engineering, Hanoi University of Industry, 298 Cau Dien Street, Hanoi, Vietnam

Thuy Duong Nguyen*
School of Mechanical Engineering, Hanoi University of Science and Technology, No.1 Dai Co Viet Street, Hanoi, Vietnam

The modern machining industry faces reducing manufacturing cost pressure and improve product quality expectations. Due to this competition a manufacturer must continually identify cost cutting opportunities in manufacturing process. The keys technology represents cost-saving opportunities associated with reducing cutting fluid consumption, cutting energy consumption, and improves the overall performance of machining operations at the same time. Hence, multiple response optimization techniques have recently become the focus of research to improve product quality by increasing surface quality and reduce costs by reducing cutting force, cutting fluids, and energy consumption of the cutting process recently. Among various optimization methods, few Taguchi-based likes as TOPSIS, COPRAS, MOORA, VIKOR... were chosen to solve complex multiple criteria problems. However, the limitation of these techniques is that it was helped to rank and select the best parameters set for the implemented experiments only. In this work, an attempt was made to streamline the milling and coolant condition parameters of S50C carbon steel under MQL condition. Experiments were performed based on Taguchi’s L27 orthogonal array. Five input factors includes machining parameters including cutting speed (Vc), feed (fz), and depth of the cut (ap) combined with two coolant parameters such as coolant air pressure (P), flow rate of lubricant (Q) were considered the variants, while specific cutting energy (Ec), material removed rate (MRR) and surface roughness were response variables. Particle swarm optimization Support Vector Machines (SVM) was used to generate the regression model, then Non-dominated Sorting Genetic Algorithm (NSGA) was used to optimize surface roughness (Ra), specific cutting energy (Ec), and production rate (MRR).

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The authors highly appreciate the support from Hanoi University of Industry (HaUI - to support the experimental research.

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