Optimization of process route by Genetic Algorithms

Zhang, Bo; Hua, Lu Zhen; Yu, Zhu Guang

doi:10.1016/j.rcim.2005.04.001

Cited by 57 publications

(25 citation statements)

References 2 publications

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“…The goal of operation sequencing is to find the optimal operation sequence. It is known through practice that in operation sequencing, the theoretic constraints cannot be satisfied fully, so if the final operation sequence can satisfy obligatory constraints and other additive constraints, the operation sequence is reasonable (Bo et al 2006) According to this condition, the constraint aggregation of the operation sequence may be divided into two categories: a compulsive constraint aggregation and an additive constraint aggregation.…”

Section: Methodsmentioning

confidence: 99%

“…The fitness value of individual is only a relative concept (only used for value comparisons and the value itself is not concerned), so the problem of sampling error does not exist. Compared with other selection operators, the “tournament selection” is more suitable for the problem of operations sequencing (Bo et al 2006). In order to guarantee the astringency of GA, the optimal (best) individual in one generation must be kept for the next generation.…”

Section: Methodsmentioning

confidence: 99%

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Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Salehi

Bahreininejad

2010

J Intell Manuf

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Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Salehi

Bahreininejad

2010

J Intell Manuf

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“…(2) Crossover There are three crossover operators for path representation: Partially Mapped Crossover (PMX), Order Crossover (OX) and Cycle Crossover (CX) [23]. The aim of PMX crossover is keeping the important similarities of parent and child generation.…”

Section: Input Layermentioning

confidence: 99%

A STEP-compliant Intelligent Process Planning System for Milling

Hua-bing¹

2015

TOAUTOCJ

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Abstract:The new standard ISO 14649 provides CAD/CAPP/CAM/CNC with an interface and a comprehensive high level and detailed manufacturing information. CNC technology based on STEP-NC will be the future trend, and the intelligent process planning is the core of STEP-NC oriented CNC technology. The objective of this research is to present the architecture, key technologies and implementation of STEP-NC oriented intelligent computer-aided process planning system. Some key technologies, such as feature recognition, setup planning, operations selection, tool selection, machine tool selection and process planning optimization are described in detail. A hybrid approach based on artificial intelligent techniques (neural networks, fuzzy logic and genetic algorithm) is adopted as the inference engine in the proposed system. Based on Solidworks CAD platform, the corresponding prototype system was developed using VB.NET programming language. The implementation of the proposed ST-ICAPP system is demonstrated by an example case study. The result shows that the proposed system is valid and feasible.

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“…GA-based approaches were developed by Bo et al, 1 Hua et al, 2 Li et al, 3 Dereli and Filiz, 4 Reddy et al, 5 Qiao et al, 6 and Salehi and Tavakkoli-Moghaddam. 7 A simulated annealing (SA)-based approach has been used by Ma et al 8 Particle Swarm Optimization (PSO) methods have also been explored by Guo et al 9 Lv and Zhu 10 and Krishna and Rao 11 have used Ant Colony Optimization approaches.…”

Section: Review Of Previous Researchmentioning

confidence: 99%

Generation of Optimal Sequence of Machining Operations in Setup Planning by Genetic Algorithms

Kumar

Deb

2012

J. Adv. Manuf. Syst.

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This paper aims at automatic generation of optimal sequence of machining operations in setup planning by Genetic Algorithm (GA) based on minimizing the number of setup changes and tool changes, subject to various machining precedence constraints. The GA has been reconstructed as the method of representing an operation is not as simple as assigning it a binary digit as in case of a chromosome in traditional GA but it has to be a distinct real number. Accordingly, the GA operators had to be modified. At the end of each GA cycle, there might be chromosomes having high fitness values but not conforming to constraints. Moreover, due to randomness of GA, the conformable chromosomes might tend to get lost. In order to minimize such losses, the elitist model is used for selection of chromosomes. Furthermore, a special subroutine has been developed to check the chromosomes for conformability and modify/repair those that violate the constraints.

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Optimization of process route by Genetic Algorithms

Cited by 57 publications

References 2 publications

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

A STEP-compliant Intelligent Process Planning System for Milling

Generation of Optimal Sequence of Machining Operations in Setup Planning by Genetic Algorithms

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