Journal ArticleDOI
A review of optimization techniques in metal cutting processes
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TLDR
The application potential of several modelling and optimization techniques in metalcutting processes, classified under several criteria, has been critically appraised, and a generic framework for parameter optimization in metal cutting processes is suggested for the benefits of selection of an appropriate approach.About:
This article is published in Computers & Industrial Engineering.The article was published on 2006-05-01. It has received 496 citations till now. The article focuses on the topics: Engineering optimization.read more
Citations
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Prediction of surface roughness in the end milling machining using Artificial Neural Network
TL;DR: The model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface Roughness performance measure.
Journal ArticleDOI
Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality
Jihong Yan,Lin Li +1 more
TL;DR: In this article, a multi-objective optimization method based on weighted grey relational analysis and response surface methodology is applied to optimize the cutting parameters in milling process in order to evaluate trade-offs between sustainability, production rate and cutting quality.
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Evolutionary techniques in optimizing machining parameters
TL;DR: An overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining are given.
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Performance optimization of Jatropha biodiesel engine model using Taguchi approach
TL;DR: In this paper, the authors proposed a methodology for thermodynamic model analysis of Jatropha biodiesel engine in combination with Taguchi's optimization approach to determine the optimum engine design and operating parameters.
Journal ArticleDOI
Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process
TL;DR: The analysis of this study has proven that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.