Cutting temperature is an important factor which directly affects cutting tool wear, cutting tool life, machined surface quality, and accuracy in the high-speed machining (HSM) process. It is very important to study t...
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Cutting temperature is an important factor which directly affects cutting tool wear, cutting tool life, machined surface quality, and accuracy in the high-speed machining (HSM) process. It is very important to study the distribution law of cutting temperature for the HSM process. In this paper, the self-developed embedded temperature measuring tool holder system (ETMTHS) is employed to measure the continuous temperature of carbide end milling tool tip. The dynamic temperature field model of solid cemented carbide milling cutter is established by using heat source method, and the heat flux in power series form is solved by using the particleswarmoptimization (PSO) algorithm. At the same time, an optimizationalgorithm to solve the inverse problem of heat conduction is given. The solution of heat flux is converted into the solution of the optimal value problem. Using optimizationalgorithm, the inverse heat conduction problem can be solved successfully. The temperature and its gradient distribution of solid cemented carbide milling cutter are obtained by analyzing the continuous milling temperature of ANSYS simulation. The comparison results show a good agreement between the simulation temperature and measuring temperature.
Carbon dioxide (CO2) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO2-EOR process is highly dependent...
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Carbon dioxide (CO2) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO2-EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO2 injection process. Traditional empirical methods to test the CO2-oil MMP are time consuming and expensive;derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. In this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO2 injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature;(2) average critical temperature;(3) molecular weight of pentane plus (C5+) fraction of crude oil, and;(4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work. (C) 2016 Elsevier Ltd. All rights reserved.
Cloud manufacturing (CMfg) is a kind of advanced service-oriented manufacturing model with on-demand use of various lifecycle-resources. Resource service selection (RSS) is one of the critical techniques for implement...
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Cloud manufacturing (CMfg) is a kind of advanced service-oriented manufacturing model with on-demand use of various lifecycle-resources. Resource service selection (RSS) is one of the critical techniques for implementing CMfg, which is applied for building flexible and loosely coupled service application to requestors. With lots of resource services owning similar functionality in RSS, quality of service (QoS) which can reflect user experience of service is often considered as a key technology to distinguish resource services for RSS. However, because of the heterogeneous QoS values, vast amounts of homogeneous resources and dynamic customer requirements in CMfg, the issue of how to measure fuzzy QoS and select the best services considering design preference, are rarely studied in CMfg. In this paper, we propose an integrated resource service selection approach to assist requesters to obtain optimal manufacturing services. Firstly, the problem description on resource service selection in CMfg is summarized. Then, a design preference-based QoS description model of CMfg is proposed and a QoS computation model based on fuzzy theory is presented for QoS measurement. Based on the above model, particleswarmoptimization (PSO) algorithm is adopted to select the optimal service composition. Finally, a numerical example is given to validate the effectiveness and efficiency of the proposed approach.
In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of R...
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In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.
In a large common place, a huge number of pedestrians may flood into the surrounding region and mix with the vehicles which originally existed on the roads when emergent events occur. The mutual restriction between pe...
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In a large common place, a huge number of pedestrians may flood into the surrounding region and mix with the vehicles which originally existed on the roads when emergent events occur. The mutual restriction between pedestrians and vehicles as well as the mutual effect between evacuation individuals and the environment which evacuees are situated in, will have an important impact on evacuation effects. This paper presents a pedestrian-vehicle mixed evacuation model to produce optimal evacuation plans considering both evacuation time and density degree. A co-evolutionary multi-particleswarms optimization approach is proposed to simulate the evacuation process of pedestrians and vehicles separately and the interaction between these two kinds of traffc modes. The proposed model and algorithm are effective for mixed evacuation problems. An illustrating example of a study region around a large stadium has been presented. The experimental results indicate the effective performances for evacuation problems which involve complex environments and various types of traffic modes.
Nowadays, using model checking techniques is one of the best solutions for software (and hardware) verification. The problem while using model checking techniques is state space explosion in which all the available me...
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Nowadays, using model checking techniques is one of the best solutions for software (and hardware) verification. The problem while using model checking techniques is state space explosion in which all the available memory is consumed by the model checker to generate all the reachable states. Among different approaches to cope with the state space explosion problem, using heuristic and meta-heuristic algorithms seems a proper solution. Although in all of these approaches it is not possible to solve the problem totally, however, it is possible to use them as refutation techniques. In the meta-heuristic techniques it is tried to generate only a portion of the state space with the highest probability to reach a faulty state. In this paper, we propose two new algorithms to deadlock detection in complex software systems specified through graph transformation systems. The first approach is a hybrid algorithm using PSO and BAT (BAPSO) and the second one is a greedy algorithm to find deadlocks. The experimental results show that the hybrid approach (BAPSO) is more accurate than PSO, BAT and other existing approaches like Genetic algorithm (GA). In addition, in most of the case studies, the proposed greedy algorithm can compete with the meta-heuristic algorithms in terms of speed and accuracy.
In this paper, a new comprehensive model has been presented for the measurement, evaluation and minimization of CO2, NOx and CO as three important emissions (emitted from vehicles) in the open time dependent vehicle r...
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In this paper, a new comprehensive model has been presented for the measurement, evaluation and minimization of CO2, NOx and CO as three important emissions (emitted from vehicles) in the open time dependent vehicle routing problem (OTDVRP). In the OTDVRP, traffic properties of congested regions like city centers are considered. Travel time between two points depends on the time of departure, and the vehicles do not come back to the depot. In some distribution companies, vehicles are rental;therefore, they do not come back to the depot from the last customer. To solve the proposed problem, an improved particle swarm optimization algorithm is developed. The results show good performance in computation experiments compared to original PSO algorithm. The results of the experiments show that considering minimization, the pollutants can reduce emissions by 16% on the average compared to the classical open TDVRP. Factors causing the variation in emissions are also identified and discussed in this study. (C) 2016 Elsevier Ltd. All rights reserved.
In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used a...
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In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used as the utility function rather than using a piecewise linear function as an approximation of the utility function of each attribute. We called this method as PUTADIS. The objective is calculating the coefficients of the polynomial and the threshold limit of classes and weight of attributes such that it minimizes the number of misclassification error. Estimation of unknown parameters of the problem is calculated by using a hybrid algorithm which is a combination of particle swarm optimization algorithm (PSO) and Genetic algorithm (GA). The results obtained by implementing the model on different datasets and comparing its performance with other previous methods show the high efficiency of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.
This paper carries out in-depth and meticulous analysis of the DV-Hop localization algorithm for wireless sensor network. It improves the DV-Hop algorithm into a node localization algorithm based on one-hop range, and...
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This paper carries out in-depth and meticulous analysis of the DV-Hop localization algorithm for wireless sensor network. It improves the DV-Hop algorithm into a node localization algorithm based on one-hop range, and proposes the centroid particleswarmoptimization localization algorithm based on RSSI by adding the RSSI and particle swarm optimization algorithm to the traditional centroid localization algorithm. Simulation experiment proves that the two algorithms have excellent effect.
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