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Route Optimization of Multi-staged Surface Treatment
Author(s) -
Ivan I. Kravchenko
Publication year - 2016
Publication title -
nauka i obrazovanie
Language(s) - English
Resource type - Journals
ISSN - 1994-0408
DOI - 10.7463/0316.0835360
Subject(s) - computer science
Development of mathematical models based on the system approach to production facilities and technological processes becomes of great importance for further development of design theory and methods. Inherently, a process operation is one of the main components of the technological process, and in multi-staged machining a piece-time of its execution is directly related to the number and parameters of transitions to be performed. The optimum number of running transitions of the process (the best route) should be considered from the standpoint of reducing piece-time operation and, consequently, improving machine capacity, with ensuring the accuracy and quality of machined surfaces. When designing an optimal variant of the multi-staged surface treatment the challenge is to define the selection mechanism of different options as well as the rules and the workflow to find the best of them. The set of possible variants forms a feasible region in which the best one for specific conditions should be found. At the design stage of the surface treatment route the state of technological system, implementing machine operation, is a priori known as an element of the technological process and when machining the surface for several cutting passes, following the theory of technological heredity, the state of each preceding cutting pass significantly affects the expected condition of the subsequent one. To solve this problem it is expedient to apply the method of dynamic programming. Dynamic programming in this case is represented as an optimal method of generating variants to start forming from a part to a workpiece. The proposed method of finding the best route of surface treatment allows to reduce the amount of computation in comparison with the guided search method and to guarantee a global extremum of the objective function

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