Technical gazette, Vol. 29 No. 5, 2022.
Original scientific paper
https://doi.org/10.17559/TV-20211012084632
An Integrated Intelligent CAD/CAPP Platform: Part II - Operation Sequencing Based on Genetic Algorithm
Stevo Borojević
orcid.org/0000-0001-6576-4683
; University of Banja Luka, Faculty of Mechanical Engineering, Stepe Stepanovića 71, 78 000 Banja Luka, Bosna i Hercegovina
Dragan Matić
; University of Banja Luka, Faculty of Natural Sciences and Mathematics, Mladena Stojanovića 2, 78 000 Banja Luka, Bosna i Hercegovina
Miroslav Dragić
; University of Banja Luka, Faculty of Mechanical Engineering, Stepe Stepanovića 71, 78 000 Banja Luka, Bosna i Hercegovina
Abstract
We present a platform for integrated CAD/CAPP part design based on Elementary Machining Features (EMF) and intelligent approach for setup planning and operation sequencing based on a genetic algorithm through two papers. In this paper, as Part II of this platform, CAD/CAPP integration was realized via information from the enriched EMF, as well as production rules and a genetic algorithm. This is done for the purpose of the automated machining operation sequencing. Operation sequencing was conducted by using the improved genetic algorithm (GA).The improved GA uses integer representation for operations and implements modified genetic operators, enabling the achievement of high results in a reasonable computational time. In the paper we present a comprehensive case study applied to some existing and one new industrial example, confirming a high level of usability of the proposed GA and overall platform. Experimental results show that the improved GA algorithm gives slightly better results than similar algorithms in literature. For industrial example, we use body of the hydraulics cylinder which consists of 52 EMF. After implementation of the proposed methodology, the optimal machining operation sequence was identified, as well as the total machining cost of 142.49 BAM.
Keywords
elementary machining features; genetic algorithm; operation sequencing
Hrčak ID:
281685
URI
Publication date:
15.10.2022.
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