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Parameters Optimization Of Micro-End-Milling Based On Simulated Annealing Genetic Algorithm

Posted on:2011-11-23Degree:MasterType:Thesis
Country:ChinaCandidate:J F LiuFull Text:PDF
GTID:2131330338980305Subject:Mechanical Manufacturing and Automation
Abstract/Summary:PDF Full Text Request
With the increasing demand for miniaturized products, people pay more attention to its processing. The micro cutting especially the micro-end-milling has become the main method to produce complex parts. Compare to the micro machining on the conventional ultra-precision machine, the micro cutting on the micro machine has its own advantages, such as cost-reducing, precision-improving and space-saving. Therefore, it has become a hot topic. The cutting force and surface roughness of the parts manufactured by micro machine has been the key point. Many scholars have conducted the related research theoretically, but the analysis is not yet mature. Lacking of the research on workability by micro machining makes it difficult to determine the processing procedure. The development of random searching optimization method has provided a way for the parameter optimization of micro-end-milling.On the base of micro-end-milling analysis, this article conducts the research on the micro cutting force and surface roughness, and carries on the optimization to the milling processing parameters.Analyze the micro-end-milling; establishment of the micro cutting force model and surface roughness model under micro-end-milling considering the entry angel and exit angel.Design the experiments based on response surface method by selecting the depth of cut, feed per tooth and spindle speed as variable, establish micro cutting force and surface roughness model by experiments which adapt to the existing processing conditions, verify and analyze the model.Optimize the surface roughness, cutting force and double-objects by using genetic algorithm and simulated annealing genetic algorithm respectively based on the cutting force and surface roughness model, and compare the results of the two optimization methods. Optimize the processing efficiency by using annealing extract penalty function methods to handle the constraints taking the surface roughness value as the restraints to provide the basis for the micro milling processing.
Keywords/Search Tags:micro-end-milling, model establishment, optimization, simulated annealing algorithm, genetic algorithm
PDF Full Text Request
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