An intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness
Email:
trungthanhk21@mta.edu.vn
Từ khóa:
Internal burnishing, Surface roughness, Vickers hardness, Minimum quantity lubrication, Optimization.
Tóm tắt
Boosting machining quality is a prominent solution to save production costs for burnishing operations. In this work, a machining condition-based optimization has been performed to decrease surface roughness (SR) and enhance Vickers hardness (VH) of the minimum quantity lubrication-assisted burnishing operation (MQLABO). The burnishing factors are the spindle speed (S), depth of penetration (D), and the air pressure (P). The burnishing trails of the hardened material labeled 40X have been conducted on a milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and MQLABO responses. The non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to determine the optimal parameters. The scientific outcomes revealed that the optimal values of the S, D, and P are 800 RPM, 0.09 mm, and 4.0 Bar, respectively. The SR is decreased by 53.8%, while the VH is enhanced by 3.1%, respectively, as coBarred to the initial values.Tài liệu tham khảo
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[3]. T. Cobanoglu, S. Ozturk, Effect of burnishing parameters on the surface quality and hardness, P. I. Mech. Eng. B-J. Eng., 229 (2015) 286-294. https://doi.org/10.1177/0954405414527962
[4]. Q. N. Banh, F. J. Shiou, Determination of optimal small ball-burnishing parameters for both surface roughness and superficial hardness improvement of STAVAX, Arab. J. Sci. Eng., 41 (2016) 639-652. https://doi.org/10.1007/s13369-015-1710-1
[5]. X. L. Yuan et al., Effect of roller burnishing process parameters on the surface roughness and microhardness for TA2 alloyInt, J. Adv. Manuf. Tech., 85 (2016) 1373-1383. https://doi.org/10.1007/s00170-015-8031-0
[6]. H. Amdouni, H et al., Modeling and optimization of a ball-burnished aluminum alloy flat surface with a crossed strategy based on response surface methodology, Int. J. Adv. Manuf. Tech., 88 (2017) 801-814. https://doi.org/10.1007/s00170-016-8817-8
[7]. J. Huuki, S. V. A. Laakso, Integrity of surfaces finished with ultrasonic burnishing, P. I. Mech. Eng. B-J. Eng., 227 (2012) 45-53. https://doi.org/10.1177/0954405412462805
[8]. R. Teimouri, S. Amini, A. B. Bami, Evaluation of optimized surface properties and residual stress in ultrasonic assisted ball burnishing of AA6061-T6, Measurement., 116 (2018) 129-139. https://doi.org/10.1016/j.measurement.2017.11.001
[9]. R. Teimouri, S. Amini, Analytical modeling of ultrasonic burnishing process: Evaluation of active forces, Measurement., 131 (2019) 654-663. https://doi.org/10.1016/j.measurement.2018.09.023
[10]. T. T. Nguyen, X. B. Le, Optimization of interior roller burnishing process for improving surface quality, Mater. Manuf. Process., 33 (2018) 1233-1241. https://doi.org/10.1080/10426914.2018.1453159
[11]. T. T. Nguyen, X. B. Le, Optimization of roller burnishing process using Kriging model to improve surface properties, P. I. Mech. Eng. B-J. Eng., 233 (2019) 2264-2282. https://doi.org/10.1177/0954405419835295
[12]. T. T. Nguyen et al., Multi-objective optimization of the flat burnishing process for energy efficiency and surface characteristics, Mater. Manuf. Process., 34 (2019) 1888-1901. https://doi.org/10.1080/10426914.2019.1689266
[13]. T. T. Nguyen et al., Multi-response optimization of the roller burnishing process in terms of energy consumption and product quality, J. Clean. Prod., 245 (2020) 119328 https://doi.org/10.1016/j.jclepro.2019.119328
[14]. T. T. Nguyen, V. T. Tran, M. Mia, Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity, Materials, 13 (2020), 2897. https://doi.org/10.3390/ma13132897
[15]. E. Cakit, W. Karwowski, Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection, Artif. Intell. Rev., 48 (2017) 139-155. https://doi.org/10.1007/s10462-016-9497-3
[16]. T. T. Nguyen, T. C.Vu, Q. D. Duong, Multi-responses optimization of finishing honing process for surface quality and production rate, J Braz. Soc. Mech. Sci. Eng., 42 (2020) 604. https://doi.org/10.1007/s40430-020-02690-y
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
22/12/2020
Nhận bài sửa
26/01/2021
Chấp nhận đăng
17/03/2021
Xuất bản
27/05/2021
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Can Xuan, K., Le Xuan, B., Nguyen Truong, A., Trinh Quang, H., & Nguyen Trung, T. (1622048400). An intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness . Tạp Chí Khoa Học Giao Thông Vận Tải, 72(4), 395-410. https://doi.org/10.47869/tcsj.72.4.1
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