Optimization of Cutting Parameters on Surface Roughness and Productivity when Milling Wood Materials
1
Faculty of Mechanical Engineering, Ho Chi Minh city University of Technology (HCMUT), Vietnam
2
VietNam National University of Ho Chi Minh City (VNUHCM), Vietnam
Submission date: 2021-09-19
Final revision date: 2021-12-01
Acceptance date: 2021-12-02
Online publication date: 2021-12-05
Publication date: 2021-12-08
Corresponding author
Nguyen Huu Loc
Mechanical Engineering, HoChiMinh City University of Technology (HCMUT)- VNUHCM, 268 Ly Thuong Kiet, Dít 10, HoChiMinh City, 700000, Ho Chi Minh City, Viet Nam
Mechanical Engineering, HoChiMinh City University of Technology (HCMUT)- VNUHCM, 268 Ly Thuong Kiet, Dít 10, HoChiMinh City, 700000, Ho Chi Minh City, Viet Nam
Journal of Machine Engineering 2021;21(4):72-89
KEYWORDS
TOPICS
ABSTRACT
The quality of the machined surface is one of the most important criteria when products are processed. In this paper, the research on surface roughness of machining tropical wood by milling method is presented. It is necessary to establish and solve the optimal problems with such aims as the highest surface quality, minimum cutting power and the highest productivity in the optimal cutting mode. Using a great amount of experimental planning and many constrained nonlinear optimization problem solving methods, the authors built a process and solved the problem to determine the optimal cutting parameters such as feed per tooth Sz, tool tip radius ρ, depth of cut h, etc. that satisfy the above object. Research object is tropical wood chukrasia and this is the database to design woodworking machines by milling method and choose a reasonable working mode when processing on CNC machines
REFERENCES (33)
1.
KILIC M., HIZIROGLU S., BURDURLU E., 2005, Effect of Machining on Surface Roughness of Wood, Building and Environment, 41, 1074–1078.
2.
WILKOWSKI J., ROUSEK M., SVOBODA E., KOPECKY Z., CZARNIAK P., 2013, Analysis of the Influence of Cutting Parameters on Surface Roughness of Milled Wood Based on Taguchi Techniques, Forestry and Wood Technology, 84, 321–325.
3.
PEREIRA K.M., GARCIA R.A., NASCIMENTO A.M., 2018, Surface Roughness of Amazonian Woods, Scientia Forestalis, 119, 347–356.
4.
WILKOWSKI J., CZARNIAK P., GÓRSKI J., JABLONSKI M., PACEK P., PODZIEWSKI P., SZYMANOWSKI K., SZYMONA K., 2015, Influence of Cutting Parameters on Surface Roughness of MDF Board After Milling and Sanding, Forestry and Wood Technology, 92, 4–476.
5.
LOC N.H., 1992, Researching Reasonable Tropical Wood Cutting by Milling Method, Ph.D Thesis, Belarusian National Technical University, Minsk.
6.
KROL O., 2020, Selection of Machine Tools Optimal Cutting Modes for Designers, Prof. Marin Drinov Publishing House of Bulgarian Academy of Sciences, SOFIA.
7.
KOC K.H., ERDINLER E.S., HAZIR E. et al, 2017, Effect of CNC Application Parameters on Wooden Surface Quality, Measurement, 107, 12–18.
8.
ISLEYAN U.K., KARAMANOGLU M., 2019, The Influence of Machining Parameters on Surface Roughness of MDF in Milling Operation, BioResources, 14, 3266–3277.
9.
SOGUTLU C., 2017, Determination of the Effect of Surface Roughness on the Bonding Strength of Wooden Materials, BioResources, 12, 1417–1429.
10.
KILIC M., 2015, Effects of Machining Methods on the Surface Roughness Values of Pinus Nigra Arnold Wood, BioResources, 10, 5554–5562.
11.
ZHONG Z.W., 2021, Surface Roughness of Machined Wood and Advanced Engineering Materials and Its Prediction: A review, Advances in Mechanical Engineering, 13, 1–19.
12.
KETURAKIS G., JUODEIKIENE I., 2007, Investigation of Milled Wood Surface Roughness, Materials Science, 13, 47–51.
13.
DOBRZYNSKI., M., ORLOWSKI K.A. I BISKUP M., 2019, Comparison of Surface Quality and Tool- Life of Glulam Window Elements after Planing, Drvna industrija, 70, 7–18.
14.
NEMLI G., AKBULUT T., ZEKOVIÇ E., 2007, Effects of Some Sanding Factors on the Surface Roughness of Particleboard, Silva Fennica, 41/2, https://doi.org/10.14214/sf.30....
15.
SANDAK J., ORLOWSKI K.A., SANDAK A., CHUCHALA D.I., TAUBE P., 2020, On-Line Measurement of Wood Surface Smoothness, Drvna industrija, 71, 193–200.
17.
ROZENBLIT G.C., 1937, About the Direction of Feed When Milling Wood, Machines and Tools, 16.
19.
MARTELLOTTI M.E., 1941, An Analysis of the Milling Process, Trans. ASME, 677–700.
20.
LOC N.H., HUNG T.Q., 2021, Study the Surface and Chip Formation of Wood Materials by Milling Method, Materials Science Forum, 1047, 74–81.
21.
ELSISY M.A., HAMMAD D.A., EL-SHORBAGY M.A., 2020, Solving Interval Quadratic Programming Problems by Using the Numerical Method and Swarm Algorithms, Complexity, Article ID 6105952,| https://doi.org/10.1155/2020/6....
22.
STEFANOVA M., MINEVICH O., BAKLANOV S., PETUKHOVA M., LUPULEAC S., GRIGOREV B., KOKKOLARAS M., 2020, Convex Optimization Techniques in Compliant Assembly Simulation, Optimization and Engineering, 21, 1665–1690.
23.
WADOOD A., KIM C.H., KHURSHIAD T., FARKOUSH S.G., RHEE S.B., 2018, Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method, Energies, 11, 1–20.
24.
SABERMAHANI S., ORDOKHANI Y., YOUSEFI S.A., 2019, Fractional-order Lagrange polynomials: An Application for Solving Delay Fractional Optimal Control Problems, Transactions of the Institute of Measurement and Control, 41, 2997–3010.
25.
SUMIN M.I., 2012, On the Stable Sequential Kuhn-Tucker Theorem and Its Applications, Applied Mathematics, 3, 1334–1350.
26.
KHORASANI A., SYAZDI M.R., 2017, Development of a Dynamic Surface Roughness Monitoring System Based on Artificial Neural Networks (ANN) in Milling Operation, The International Journal Advanced Manufacturing Technology, 93, 141–151.
27.
HAZIR E., OZCAN T., 2019, Response Surface Methodology Integrated with Desirability Function and Genetic Algorithm Approach for the Optimization of CNC Machining Parameters, Arabian Journal for Science Engineering, 44, 2795–2809.
28.
GHOSH G., MANDAL P., MONDAL S.C., 2019, Modeling and Optimization of Surface Roughness in Keyway Milling Using ANN, Genetic Algorithm, and Particle Swarm Optimization, The International Journal of Advanced Manufacturing Technology, 100, 1223–1242.
29.
LOC N.H., Van THUY T.., 2022, Applying FCCD Method in Studying the Cutting Power of the Wood Milling Machine, Solid State Phenomena, 330, 25–31.
31.
LOC N.H., 2021, Experimental Study of Tool Wear when Milling Tropical Wood with Various Tool Materials, Key Engineering Materials.
32.
LOC N.H., HUNG N.P., 2021, Utilizing Response Surface Methods Designs for Optimization of Technological Parameters on the Vibration Amplitude of CNC Router Spindle, ASEAN Engineering Journal, 11, 34–44.
33.
HAZIR E., ERDINLER E.S., KOC K.H., 2018, Optimization of CNC Cutting Parameters Using Design of Experiment (DOE) and Desirability Function, Journal of Forestry Research, 29, 1423–1434.
| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
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