MATHEMATICAL MODELING OF THE SEQUENCE OF MACHINING SECTIONS OF COMPLEX SURFACES WHEN MILLING ON A TRIAXIAL CNC MACHINE TOOL
DOI:
https://doi.org/10.17770/etr2023vol3.7194Keywords:
pure/actual/finish milling, complex surfaces, CNC machine tool, optimizationAbstract
The idle running times of the working units of a machine tool are the sum of the idle running times for the tool change and for changing the area uder treatment. The paper presents mathematical models, establishing the relationship between the additional time for performing the technological operations with the parameters of both the technological equipment and the object under treatment. The mathematical models for minimizing the idle moves when a tool passes from one machined section to another, allows to reduce the additional treatment time, which, in turn, leads to an increase in the productivity of the process of actual milling.
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А. S. Azotov and V. S. Salnikov, Optimization of the tool path in multi-operation machines. Scientific and Technical Conference on Automation and informatization in mechanical engineering, 20 – 23 January 2001, Tula, 2001.
R. Faizrakhmanov, R. Murzakaev and A. Poliakov, “Cutting Time Optimization Using Technology for CNC Machines”, Proceedings of International Conference on Applied Innovation in IT, Volume 6, Issue 1, pp.37-44, 2018.
J. Marcinčin and K. Albeloushy, “Optimization of cutting tool motion strategy for cnc milling technological processes”, Proceedings of the 16th International Conference on Manufacturing Systems – ICMaS ISSN 1842-3183, University POLITEHNICA of Bucharest, Machine and Manufacturing Systems Department Bucharest, Romania, 2019.
C. Feng, Y. Huang, Y. Wu and J. Zhang, “Feature-based optimization method integrating sequencing and cutting parameters for minimizing energy consumption of CNC machine tools”, The International Journal of Advanced Manufacturing Technology, volume 121, pages503–515, 2022.
Ö. Özçelik, Küçük Koç, “Evaluation of tool path strategies in cnc woodworking machines and a case study”, Wood Industry and Engineering, Volume 2021, Issue, 2021.
N. Cristofides, A. Mingozzi and P. Toth, C. Sandi, The vehicle routing problem: Combinatorial Optimization, Wiley, Chichester, 1979.
G. Clarke, “Scheduling of vehicles from a central depot to a number of delivery points”, Journal Operations Research, vol.12, pp.568-581, 1964.
B. Golden, T. Magnanti and H. Nguyen, “Implementing vehicle routing algorithms”, Networks vol.7 (2), pp.113-148, 1977.
G. Laporte, “Classical Heuristics for the Vehicle Routing Problem”, Les Cahier du GERAD, G98-54, Group for Research in Decision Analysis, pp.234-239, Montreal, Canada, 1998.
P. Toth and D. Vigo, The Vehicle Routing Problem, Society for Industrial and Applied Mathematics, 2002.
R. Baldacci, E. Hadjiconstantinou and A. Mingozzi, “An Exact Algorithm for the Capacitated Vehicle Routing Problem Rased on a Two-Commodity Network Flow Formulation”, Journal Operations Research, vol.52, pp.723-738, 2004.
D. S. Pisinger and A. Ropke, “A general heuristic for vehicle routing problems”, Journal Computers and Operations research, vol.34 -Issue 8, pp.2403-2435, 2007.
T.J. Gaskell, “Bases for vehicle fleet scheduling”, Journal Operational research Quarterly, vol.18, pp.281-295, 1967.
P. Yellow, “A computational modification to the savings method of vehicle scheduling”, Journal Operational research Quarterly, vol.21, pp.281-283, 1970.
H. Paessens, “The savings algorithm for the vehicle routing problem”, European Journal ot Operational research, vol.34, pp.336-344, 1988.
C. Cerrone, R. Cerulli and B. Golden, “Carousel greedy: A generalized greedy algorithm with applications in optimization”, Computers & Operations Research, Volume 85, pp. 97-112, 2017,
J. Li, Y. Du, K. Gao, P. Duan, D. Gong and Q. Pan, “A Hybrid Iterated Greedy Algorithm for a Crane Transportation Flexible Job Shop Problem”, IEEE Transactions on Automation Science and Engineering, Volume 19, Issue: 3, pp. 2153-2170, 2022.
J. Dubois-Lacoste, F. Pagnozzi and T. Stützle, “An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem”, Computers & Operations Research, Vol.81, pp. 160-166, May 2017.
A. Brum, R. Ruiz and M. Ritt, “Automatic generation of iterated greedy algorithms for the non-permutation flow shop scheduling problem with total completion time minimization”, Computers & Industrial Engineering, Volume 163,107843, January 2022,
X. Han, Y. Han, B. Zhang, H. Qin, J. Li, Y. Liu and D. Gong, “An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion”, Applied Soft Computing, Volume 129, 109502, November 2022.
A. J. Gallego, J. R. Rico-Juan and J. J. Valero-Mas, “Efficient k-nearest neighbor search based on clustering and adaptive k-values”, Pattern Recognition, Volume 122, 108356, February 2022,
L. Ribeiro de Abreu, K. A. Araújo, B. de Athayde Prata, M. S. Nagano and J. V. Moccellin, “A new variable neighbourhood search with a constraint programming search strategy for the open shop scheduling problem with operation repetitions”, Engineering Optimization, Volume 54, pp.1563-1582, 2022.
C.Friedrich and R. Elbert, “Adaptive large neighborhood search for vehicle routing problems with transshipment facilities arising in city logistics”, Computers & Operations Research, Volume 137, 105491, January 2022.
M. Alinaghian, M. Jamshidian and E. B. Tirkolaee, “The time-dependent multi-depot fleet size and mix green vehicle routing problem: improved adaptive large neighbourhood search”, A Journal of Mathematical Programming and Operations Research, Volume 71, 2022 - Issue 11: Special Issue Dedicated to the International Conference «Dynamical Control and Optimization», DCO 2021.
C. Zhang, F. Han and W. Zhang, “A cutting sequence optimization method based on tabu search algorithm for complex parts machining”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Volume 233, Issue, 2018.
Y. Xin, S. Yang, G. Wang2, R. Evans and F. Wu, ”A tool path optimization approach based on blend feature simplification for multi-cavity machining of complex parts”, Science Progress 2020, Vol. 103(1) 1–21, 2020.
D. Karimi and M.J. Nategh, “Contour maps for developing optimal toolpath and workpiece setup in hexapod machine tools by considering the kinematics nonlinearity”, Proc IMechE Part B: J Engineering Manufacture, 230: pp.1572–1583, 2016.
Abu Qudeiri JE., “Production simulator system for flexible routing optimization in flexible manufacturing systems”. Proc IMechE Part B: J Engineering Manufacture, 231: pp.1237– 1247, 2017.
Hu L, Gu Z-Q, Huang J, et al., ”Research and realization of optimum route planning in vehicle navigation systems based on a hybrid genetic algorithm”,.Proc IMechE Part D: J Automobile Engineering, 222: pp. 757–7632018, 2018.
C. Feng, H. Guo, J. Zhang, Y. Huang and S. Huang, “A systematic method of optimization of machining parameters considering energy consumption, machining time, and surface roughness with experimental analysis”, The International Journal of Advanced Manufacturing Technology, volume 119, pages7383–7401, 2022.
