SOLUTION OF THE PROBLEM OF OPTIMIZING THE PARAMETERS OF METAL PROCESSING DURING THE TURNING OPERATION

Вячеслав Владимирович Лимаренко, Инна Петровна Хавина

Abstract


The statement and solution with the application of the intellectual decision support system (IDSS) for optimization of metal processing parameters during the turning operation are shown. The task was posed during the development of IDSS, which allows automating the development of the technological process (TP), optimizing and managing it. The absence of such systems of intellectual support for decision-making does not allow enterprises to fully utilize the capabilities of high-tech equipment and reduces the efficiency and profitability of production due to inefficient use of machine tools, material and engineering resources of enterprises. The tasks of synthesis of the optimal technological process are solved in two stages: the first – structural optimization of the technological process (synthesis of the optimal TP structure); the second – parametric optimization of the technological process (determination of optimal operating parameters for all operations of the technological process). The task of optimizing the parameters of metal processing during the turning operation is one of the subtasks of the second stage of the synthesis of the optimal structure of the technological process of metal machining. The task is solved taking into account the level of accumulated wear on the back surface of the tool, the level of machine efficiency, depending on the operating modes and the period of economically effective tool life. The task is solved in a multi-criteria setting taking into account three objective functions: the cost of operations, the specific energy costs for operations and the operation's performance and the ten existing technical and technological limitations. The solution is obtained as a Pareto-optimal solution. During the solution, an artificial neural network and the genetic algorithm FFGA were applied. This approach allows us to solve the problems of optimization of real production. Shown: the statement of the problem and its practical solution, the structure of the developed system of intellectual support for decision-making, present’s practical results of solving the task, verified the results obtained. The technological map obtained with the help of the IDSS for processing the surface of the «Filter housing» component of the GP-21 generator drive.


Keywords


IDSS; Optimization of turning the operation parameters; the accumulated wear of the tool; cost-effective tool life; multi-objective optimization problem; the Pareto-optimal solution

References


Boguslaev, A. V., Oleinik, Al. A., Oleinik, An. A. Progressivnye tekhnologii modelirovaniya, optimizatsii i intellektual'noi avtomatizatsii etapov zhiznennogo tsikla aviatsionnykh dvigatelei [Progressive technologies of modeling, optimization and intelligent automation of the life cycle stages of aircraft engines]. Zaporozh'e, OAO Motor Sich Publ., 2009. 468 p.

Kondakov, A. I. SAPR tekhnologicheskikh protsessov [CAD of technological processes]. St. Petersburg, Academia Publ., 2010. 272 p.

Prakash, M. D. Uday, S. D., Shanker, D. Modeling of Metal Forming and Machining Processes by Finite Element and Soft Computing Methods. London, Springer-Verlag London Limited Publ., 2008. 590 p.

Davim, J. P. Computational Methods for Optimizing Manufacturing Technology: Models and Techniques. USA, IGI Global Publ., 2012. 395 p.

Choudhuri, K., Pratihar, D. K., Pal, D. K. Multi-objective optimization in turning – using a Genetic Algorithm. Journal of Institute of Engineers, 2002, vol. 82, pp. 37-44.

Serra, R., Chibane, H. Multi-objective optimization of cutting parameters for turning AISI 52100 steel. 7ème Assises MUGV2012, ENISE – CETIM. Saint-Etienne, 2012, рр. 52-67.

Yashcheritsyn, P. I., Fel'dshtein, E. E., Kornievich, M. A. Teoriya rezaniya [Theory of cutting]. Minsk, Novoe znanie Publ., 2006. 512 p.

Khavina, I. P., Limarenko, V. V. Primenenie neironnykh setei v tekhnologicheskikh protsessakh mekhanoobrabotki [Application of neural networks in technological processes of machining]. Avtomatizirovannye tekhnologii i proizvodstva. Sbornik nauchnykh trudov. 2013, vol. 5, pp. 252-258.

Khavina I. P., Limarenko, V. V. Sistema podderzhki prinyatiya reshenii optimizatsii tekhnologicheskikh protsessov mekhanoobrabotki [Decision support system for optimization of machining processes]. Sistemi obrobki іnformatsіі. Zbіrnik naukovikh prats'. Kharkіvs'kii unіversitet Povіtryanikh Sil іm. І. Kozheduba. [Proc. Kharkіv KhUPS «Information processing systems»], 2015, vol. 11 (131), pp. 76-84.

Song, W. Development of predictive force models for classical orthogonal and oblique cutting and turning operations incorporating tool flank wear effects. PhD. Queensland University of Technology, 2006. 208 р.

Fonseca, C. M., Fleming, P. J. Multiobjective optimization and multiple constraint handling with evolutionary algorithms. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans. 1998, vol. 28, pр. 26-37.

Іscar. Іscar – Machining Power. Available at: http://mpwr.iscar.com/machiningpwr/machiningpower.wgx?vwginstance=57ced7e2c19045afb4edf6639f8a2fe5 (accessed 04.02.2016).


Refbacks

  • There are currently no refbacks.