Creep feed grinding optimization by an integrated GA-NN system

Sedighi, M.; Afshari, Davood

doi:10.1007/s10845-009-0243-4

Cited by 63 publications

(31 citation statements)

References 17 publications

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“…With the increase of the manufacturing and system complexity, the prediction-based method gradually requires the short-time and dynamic invoking of intelligent optimization algorithm to better serve different situation with iterative searching. For the prediction-based method in manufacturing field, refer to references [118][119][120][121][122][123][124][125].…”

Section: Prediction-based Methodsmentioning

confidence: 99%

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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Due to its good versatility and independence, intelligent optimization algorithm has largely shortened the time of decision-making in large-scale optimization problems of manufacture. However, lower searching time often conflicts with the searching accuracy in most cases. To improve the problem solving capability, research in intelligent optimization algorithm based on different domain characteristics never stopped. From the view of manufacturing, this chapter classified and comprehensively analyzed all kinds of manufacturing optimization problems and their general methods, illustrated the application features and challenges of intelligent optimization algorithm in manufacturing, and summarized the development needs and trends of intelligent optimization algorithm in the field of manufacturing system.

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Section: Prediction-based Methodsmentioning

confidence: 99%

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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“…Hung and Huang (2006) improved the plastic ball grid array assembly yield using ANN and GA. Similarly, Huang and Tang (2006) performed parameter optimisation of a melt spinning process using ANN and GA. Wang et al (2012a) performed a multi-objective process optimisation of the serial-parallel hybrid polishing machine tool using ANN and GA, similarly to the research presented by Sedighi and Afshari (2010) that used ANN-GA approach to optimise creep feed grinding process. Modelling and optimisation for microstructural properties of Al/SiC nanocomposite was performed using ANNs and GA, considering two responses (Esmaeili and Dashtbayazi 2014).…”

Section: Multiresponse Optimisation Based On Genetic Algorithmmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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“…GA is one of the most powerful and broadly applicable optimization techniques in engineering design problems, especially approaches of GA integrated with other techniques have been applied in production planning (Morad and Zalzala 1999;Moon et al 2006) and process optimization (Sedighi and Afshari 2009). Shen et al (2007) proposed a combining artificial neural network and genetic algorithm method to optimize the injection molding process.…”

Section: Ga Optimization Based On Ann Modelmentioning

confidence: 99%

Process optimization of the serial-parallel hybrid polishing machine tool based on artificial neural network and genetic algorithm

Wang

Zhao

et al. 2010

J Intell Manuf

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A kind of serial-parallel hybrid polishing machine tool based on the elastic polishing theory is developed and applied to finish mould surface with using bound abrasives. It mainly consists of parallel mechanism of three dimensional moving platform, serial rotational mechanism of two degrees of freedom and the elastic polishing tool system. The active compliant control and passive conformity of polishing tool are provided by a pneumatic servo system and a spring, respectively. Considering the contradiction between the machining quality and efficiency, the optimization model of process parameters is found according to different machining requirements, namely single objective optimization and multi-objective optimization, which provide a choice of parameters as a basis for the operators in practice. Many polishing experiments are conducted to collect the data samples. The genetic algorithm integrated with artificial neural network is used for researching for the optimal process parameters in term of the various optimization objectives. This research also lays the foundation for further establishing polishing expert system.

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Creep feed grinding optimization by an integrated GA-NN system

Cited by 63 publications

References 17 publications

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Review of Multiresponse Process Optimisation Methods

Process optimization of the serial-parallel hybrid polishing machine tool based on artificial neural network and genetic algorithm

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