Current issue
Online first
Archive
About
Ethics standards
Guidelines
4/2021 vol. 21
Get citation
Optimization of Cutting Parameters on Surface Roughness and Productivity when Milling Wood Materials
Nguyen Huu Loc 1,2
,
 
Trinh Quoc Hung 1,2
 
 
 
More details
Hide details
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
 
 
Journal of Machine Engineering 2021;21(4):72-89
DOI: https://doi.org/10.36897/jme/144426
 
 
Article (PDF)
References (33) Citations (3)
 
KEYWORDS
Surface roughness
milling method
Response surface
Optimzation
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
3.
PEREIRA K.M., GARCIA R.A., NASCIMENTO A.M., 2018, Surface Roughness of Amazonian Woods, Scientia Forestalis, 119, 347–356.
Google Scholar
 
 
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.
Google Scholar
 
 
5.
LOC N.H., 1992, Researching Reasonable Tropical Wood Cutting by Milling Method, Ph.D Thesis, Belarusian National Technical University, Minsk.
Google Scholar
 
 
6.
KROL O., 2020, Selection of Machine Tools Optimal Cutting Modes for Designers, Prof. Marin Drinov Publishing House of Bulgarian Academy of Sciences, SOFIA.
Google Scholar
 
 
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.
Google Scholar
 
 
8.
ISLEYAN U.K., KARAMANOGLU M., 2019, The Influence of Machining Parameters on Surface Roughness of MDF in Milling Operation, BioResources, 14, 3266–3277.
Google Scholar
 
 
9.
SOGUTLU C., 2017, Determination of the Effect of Surface Roughness on the Bonding Strength of Wooden Materials, BioResources, 12, 1417–1429.
Google Scholar
 
 
10.
KILIC M., 2015, Effects of Machining Methods on the Surface Roughness Values of Pinus Nigra Arnold Wood, BioResources, 10, 5554–5562.
Google Scholar
 
 
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.
Google Scholar
 
 
12.
KETURAKIS G., JUODEIKIENE I., 2007, Investigation of Milled Wood Surface Roughness, Materials Science, 13, 47–51.
Google Scholar
 
 
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.
Google Scholar
 
 
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....
CrossRef
 
Google Scholar
 
 
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.
Google Scholar
 
 
16.
FISHER H., 1905, Die Werkzeugmaschine, J. Springer, Berlin.
Google Scholar
 
 
17.
ROZENBLIT G.C., 1937, About the Direction of Feed When Milling Wood, Machines and Tools, 16.
Google Scholar
 
 
18.
SALOMON C., 1918, Zur Theorie der Fräs Vorganges, Z.d.VDI No., 15.
Google Scholar
 
 
19.
MARTELLOTTI M.E., 1941, An Analysis of the Milling Process, Trans. ASME, 677–700.
Google Scholar
 
 
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.
Google Scholar
 
 
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....
CrossRef
 
Google Scholar
 
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
25.
SUMIN M.I., 2012, On the Stable Sequential Kuhn-Tucker Theorem and Its Applications, Applied Mathematics, 3, 1334–1350.
Google Scholar
 
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
30.
NGUYEN T.B, 2003, Optimizing the Cutting Process, Education Publisher.
Google Scholar
 
 
31.
LOC N.H., 2021, Experimental Study of Tool Wear when Milling Tropical Wood with Various Tool Materials, Key Engineering Materials.
Google Scholar
 
 
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.
Google Scholar
 
 
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.
Google Scholar
 
 
 
CITATIONS (3):
1.
Optimization of technological parameters in ultrasonic welding of the polypropylene fabric using Taguchi and FCCCD methods
Thanh Quang Le, Thanh Hai Nguyen, Loc Huu Nguyen
EUREKA: Physics and Engineering
 
CrossRef
 
 
2.
Entropy and EDAS-based multi-criteria decision-making approach to optimize CNC machining parameters
Ali Cakmak, Bahadir Cagri Bayram
Wood Material Science & Engineering
 
CrossRef
 
 
3.
Dimensional Stability and Technological Performance of 13-Year-Old Simarouba amara Aubl. Thinning Wood: Pathways to High-Value Product Development
Luiz Paulo Amaringo-Córdova, Tatiana Mildred Ucañay-Ayllon, Jefferson Alexander Rodriguez-Sotelo, Hilter Fasabi-Pashanasi, Cesar Mori-Montero, Oscar Amado Ruiz-Torres, Wilson Francisco Guerra-Arévalo, Héctor Guerra-Arévalo, Uriel Aldava-Pardave, Rosario Marilú Bernaola-Paucar, Ana Lucía Milagros Vásquez-Vela, Edinson Eduardo López-Galán, Jhinmy Karc Hemeryth-Bartra, Kevin Isaac Rodriguez-Vasquez
Forests
 
CrossRef
 
 
Share
Send by email
RELATED ARTICLE
A study on the Ability to Fabricate Mold Using 3D Printing Technology and Evaluate the Surface Roughness of Products in Investment Casting
Analysing the Impact of Cutting Force and Vibration on Surface Roughness in External Cylindrical Grinding of SKH2 Steel
Indexes
 
Keywords index
Topics index
Authors index
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
© 2006-2026 Journal hosting platform by Bentus
Scroll to top