Estimation of Gaussian Plume Model Parameters Using the Simulated Annealing Algorithm

Author(s):  
Gonçalo Cabrita ◽  
Lino Marques
2017 ◽  
Vol 12 (4) ◽  
pp. 81-92
Author(s):  
Aqeel Sabree Baden

Incremental sheet metal forming is a modern technique of sheet metal forming in which a uniform sheet is locally deformed during the progressive action of a forming tool. The tool movement is governed by a CNC milling machine. The tool locally deforms by this way the sheet with pure deformation stretching. In SPIF process, the research is concentrate on the development of predict models for estimate the product quality. Using simulated annealing algorithm (SAA), Surface quality in SPIF has been modeled. In the development of this predictive model, spindle speed, feed rate and step depth have been considered as model parameters. Maximum peak height (Rz) and Arithmetic mean surface roughness (Ra) are used as response parameter to assess the surface roughness of incremental forming parts along and across tool path direction. The data required has been generate, compare and evaluate to the proposed models that obtained from SPIF experiments. Simulated Annealing Algorithm (SAA) is utilized to develop an effective mathematical model to predict optimum level. In simulated algorithm (SA), an exponential cooling schedule depending on Newtonian cooling process is used and by choosing the number of iterations at each step on the experimental work is done. The SA algorithm is used to predict the forming parameters (speed, feed and step size) on surface quality in forming process of Al 1050 based on Taguchi‘s orthogonal array of L9 and (ANOVA) analysis of variance were used to find the best factors that effect on  the surface quality.


2013 ◽  
Vol 830 ◽  
pp. 37-40
Author(s):  
Jia Deng

Accurate identification of model parameters is to improve the giant magnetostrictive precision displacement control key, For single algorithm is difficult to achieve for giant magnetostrictive hysteresis nonlinear model parameters accurately identify problems, in this paper, the genetic algorithm and simulated annealing algorithm fusion, First, quick search ability of genetic algorithm are used to get a better community, recycle kick ability of simulated annealing algorithm to to adjust and optimize the whole group, Presented an improved genetic simulated annealing algorithm, And its application to the giant magnetostrictive actuator displacement hysteresis nonlinear model parameter identification. The algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, both the faster convergence speed, and improves the precision and quality of the optimal solution.


2021 ◽  
Vol 19 (3) ◽  
pp. 250-262
Author(s):  
A. Cruz ◽  
W. Vélez ◽  
P. Thomson

This work presents a novel technique for estimating the prestressing forces in simply supported beams with axial prestress force. The technique is based on the use of generic finite elements for modeling the beam and experimental time-domain response to simultaneously identify axial forces and generic parameters. Parameter updating is accomplished using a Simulated Annealing algorithm implemented for the solution of the prestress force identification problem. The effectiveness of the method was assessed in numerical simulations and was further verified on an experimental prestressed concrete beam. The results show that the inclusion of generic elements allows the identification of the force to be achieved even in the presence of errors in model parameters, thus eliminating the restraints of previous approaches.


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