By R. Venkata Rao

ISBN-10: 0857290142

ISBN-13: 9780857290144

ISBN-10: 0857290150

ISBN-13: 9780857290151

*Advanced Modeling and Optimization of producing Processes* offers a accomplished evaluate of the most recent foreign examine and improvement developments within the modeling and optimization of producing strategies, with a spotlight on machining. It makes use of examples of varied production procedures to illustrate complicated modeling and optimization recommendations. either easy and complex thoughts are provided for numerous production tactics, mathematical types, conventional and non-traditional optimization options, and actual case stories. the result of the appliance of the proposed tools also are coated and the publication highlights the main precious modeling and optimization concepts for attaining most sensible strategy functionality. as well as masking the complex modeling, optimization and environmental features of machining approaches, *Advanced Modeling and Optimization of producing Processes* additionally covers the most recent technological advances, together with swift prototyping and tooling, micromachining, and nano-finishing. *Advanced Modeling and Optimization of producing Processes* is written for designers and production engineers who're liable for the technical elements of product consciousness, because it offers new types and optimization innovations to make their paintings more straightforward, extra effective, and more desirable. it's also an invaluable textual content for practitioners, researchers, and complicated scholars in mechanical, business, and production engineering.

**Read or Download Advanced Modeling and Optimization of Manufacturing Processes: International Research and Development PDF**

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From the preface:

This is an introductory textbook in regards to the techniques and methods of mathematical/computational

modeling and research built within the rising interdisciplinary box of complex

systems technological know-how. advanced structures should be informally outlined as networks of many

interacting elements which could come up and evolve via self-organization. Many realworld

systems might be modeled and understood as complicated structures, reminiscent of political

organizations, human cultures/languages, nationwide and overseas economies, stock

markets, the web, social networks, the worldwide weather, foodstuff webs, brains, physiological

systems, or even gene regulatory networks inside of a unmarried cellphone; primarily, they

are in all places. In all of those structures, an enormous volume of microscopic components

are interacting with one another in nontrivial methods, the place very important info is living in

the relationships among the elements and never unavoidably in the components themselves. It

is as a result primary to version and study how such interactions shape and function in

order to appreciate what is going to emerge at a macroscopic scale within the system.

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**Additional info for Advanced Modeling and Optimization of Manufacturing Processes: International Research and Development**

**Example text**

T ð1:52Þ where T is the number of iterations (generation cycles); Cij(t) is the revised concentration of pheromone associated with option lij at iteration t, Cij (t - 1) is the concentration of pheromone at the previous iteration (t - 1); DCij = change in pheromone concentration; and q = pheromone evaporation rate (0–1). The reason for allowing pheromone evaporation is to avoid too strong influence of the old pheromone to avoid premature solution stagnation. In Eq. 53, the change in pheromone concentration DCij is calculated as: DCij ¼ Rk R=Fitnessk ; ¼ 0; if option is chosen if option is not chosen ð1:53Þ where R is a constant called the pheromone reward factor; and fitnessk is the value of the objective function (solution performance) calculated for ant k.

However, it usually takes long time for the binary coding AIA to obtain convergence. Furthermore, it is very difficult for AIA to break away from the local optimal value, which can restrict the search process to the zone around this value and can easily lead to premature termination of the process. Qiao et al. [67] proposed an improved affinity calculation approach by combining the Euclidean distance with the difference between fitness values, and make the threshold value a dynamic parameter in order to overcome the drawback of the tendency of GA and AIA towards local optimum value and premature completion.

Therefore, by controlling the temperature, ‘‘T,’’ and assuming that the search process follows Boltzman probability distribution, the convergence of an algorithm can be controlled. At any current point, X(t), the new value of the variables for the successive iterations is calculated using the formula, ! N X X ð t þ 1Þ ¼ X ð t Þ þ r Ri À 0:5N ð1:48Þ i¼1 where r = (Xmax - Xmin)/6, R is random number and N is number of random numbers used. Using the Metropolis algorithm [51], the probability of the next point being accepted at X(t ?

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