Simulated Annealing Algorithm Method Research Articles

Tool wear deceleration in turning of Inconel 625

Rapid tool wear is one of the most important problems in rough turning of Inconel 625 nickel-based super alloys which is less considered as a case study in wear analysis researches. Therefore, in this study, high material removal rate and low tool wear of PVD-TiAlN coated carbide were considered as objective factors in rough turning of Inconel 625 super-alloy. By using an adaptive network-based fuzzy inference system; an input-output relationship model was developed to find the effective parameters. Furthermore, simulated annealing algorithm method was used to define an optimum cutting condition considering low flank wear and high material removal rate. The results indicate that cutting speed and depth of cut with their interaction are the most effective factors on tool wear. While, feed rate is almost ineffective on tool wear propagation which can be increased to obtain higher material removal rate.

Tool wear deceleration in turning of Inconel 625

Rapid tool wear is one of the most important problems in rough turning of Inconel 625 nickel-based super alloys which is less considered as a case study in wear analysis researches. Therefore, in this study, high material removal rate and low tool wear of PVD-TiAlN coated carbide were considered as objective factors in rough turning of Inconel 625 super-alloy. By using an adaptive network-based fuzzy inference system; an input-output relationship model was developed to find the effective parameters. Furthermore, simulated annealing algorithm method was used to define an optimum cutting condition considering low flank wear and high material removal rate. The results indicate that cutting speed and depth of cut with their interaction are the most effective factors on tool wear. While, feed rate is almost ineffective on tool wear propagation which can be increased to obtain higher material removal rate.

A Heuristic Method for Solving Bi-Objective Two-Stage Hybrid Flow Shop Scheduling Problem

Hybrid flow shop problems are the most widely used in scheduling problems. A two-stage hybrid flow shop can be considered as part of a larger problem we have studied the scheduling of a two-stage hybrid flow shop problem which contains one machine at the first stage and two machines at the second stage with two objectives, the average flow time ( F ) and the average tardiness ( T ). In this paper a heuristic method has been introduced and it has been compared to a multi-objective decision making (lexicography) using simulated annealing (SA) Algorithm on both objective. Eventually, obtained solutions have been solved by simple additive weighting (SAW) method. The weight of criteria have been obtained based on the Shannon Entropy. The result indicated that the proposed heuristic method solution in 90.4% problems has led to better than solutions based on SA Algorithm. The solutions obtained out of 500 problems in heuristic method is 15.04% better than the solutions obtained according to SA Algorithm. And also in heuristic method, the obtained solutions were 58 times faster than SA Algorithm method.

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

An improved particle swarm optimization (IPSO) was proposed and the intelligent algorithms such as IPSO, genetic algorithm (GA), and simulated annealing algorithm (SA) were introduced to determine parameters of Van Genuchten (VG) model for soil water retention curve (SWRC) of four typical agricultural soil te xtures (clay, clay loam, silt loam and sand loam) in China. For comparison, the SWRC in term of VG model was also fitted by a computer program RETC and pedotransfer function Rosetta, respectively. For four soil textures, the value of determination coeffic ient (R 2 ) and root mean square error (RMSE) in the estimation of VG equation parameters by IPSO are the highest and lowest in the above three intelligent algorithms, respectively. The simulated values of water content by IPSO are much closed to the measured values (R 2 = 0.990). It was found that the Rosetta is unable to estimate the SWRC adequately and the highest RMSE value is up to 1.096E 01cm 3 cm 3 . The predicted values of moisture content by Rosetta are far away from the measured values (R 2 = 0.585). The RETC provided good simulation results of water content (R 2 = 0.974). However, the soil residual water content (θr), of VG equation can not be obtained. It was concluded that the IPSO presented here is more reasonable and reliable to estimate the SWRC in term of VG model than the method of GA, SA, RETC and Rosetta.

Using a d-optimal mixture design to study the thermal properties of short glass fiber- and polytetrafluoroethylene-reinforced polycarbonate composites

This study analyzed variations of thermal properties that depend on the injection molding techniques during the blending of short glass fiber (SGF)- and polytetrafluoroethylene (PTFE)-reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is using a d-optimal mixture design (DMD). The thermal conductivity and thermal expansion coefficient were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back-propagation neural network (BPNN), and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, analysis of variance and response surface graphs were applied to identify the effect of mixture ratio of SGF- and PTFE-reinforced PC composites for the thermal conductivity and thermal expansion coefficient.

Optimization of Warpage in Injection Molding Based on Simulated Annealing Algorithm

To make a simulated annealing algorithm(SA) to optimize the method of warping deformation of the purpose is to enable the optimal value of the warpage. Using ActiveX technology and VB programming tools will be SA and Moldflow combination. The optimal injection molding process parameters, such as mold temperature, melt temperature, gate location were determined by according to Metropolis criterion and following a route of Monte Carlo(MC) heuristic random search determined by SA. The results of the typical model show that the wargae is accurate and reliable, and the optimization efficiency is effectively improved by applying the SA method, therefore the combination of Moldflow and SA proposed in this paper is useful for the optimization of injection molding process parameters.

Study on tribological behaviors of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites via a d-optimal mixture design

This study analyzed variations of tribological behaviors that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is to use a D-optimal mixture design (DMD). The tribological behaviors of friction coefficient and wear mass loss were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back-propagation neural network (BPNN) and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, the scanning electron microscope (SEM) images show that the abundant debris are peeled off from the matrix materials and predominant delamination mechanisms and plastic deformation are shown on the worn surface after tribological behavior tests. Copyright © 2010 John Wiley & Sons, Ltd.

A Clustering Protocol Using Multiple Chain Strategy in WSNs

In W SNs, e nergy efficiency and low latency are considered as two key issues in designing routing protocol . This paper introduces two schemes to combine clustering strategy with chain routing algorithm in order to satisfy both energy and delay constraints in WSNs. Furthermore by one scheme, we propose a two layer hierarchical routing protocol called Chain Routing B ased on Coordinates-oriented Clustering Strategy ( CRBCC ), which gives a good compromise between energy consumption and delay . First, CRBCC makes balanced clustering according to y coordinates. That is to say, each cluster has approximately equal number of nodes . Second, CRBCC makes chain routing by simulated annealing algorithm (SA) inside the cluster and elects chain leader in the order of x coordinates. Third, CRBCC makes chain routing again by SA method among chain leaders. Simulation results show that CRBCC performs better than PEGASIS in terms of energy efficiency and network delay.

Traveling salesman problem and Tsallis statistics.

A generalization of the stochastic method of simulated annealing algorithm based on Tsallis statistics is proposed. This algorithm is considerably faster than the traditional ones in solving the traveling salesman problem. Acceptable solutions are found in fewer steps and higher temperatures than both the classical and the fast simulated annealings. Recent developments in solving NP-complete problems can be incorporated and improve the performance even more.