Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital d...To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.展开更多
To solve the problem of difficulty in selecting NC cutting parameters by the redundancy technique, a method is put forward to optimize cutting parameters based on a revolutionary mathematical model and a revolutionary...To solve the problem of difficulty in selecting NC cutting parameters by the redundancy technique, a method is put forward to optimize cutting parameters based on a revolutionary mathematical model and a revolutionary cutting parameters database. By use of fuzzy inference rules, it can not only make the method itself evolved and updated, but also ensure data to be correct and feasible from the two optimization routes. Practical running and testing proved that this method can facilitate for the user to select parameters and greatly improve the processing efficiency.展开更多
To investigate cutting performance in the helical milling of carbon fiber reinforced polymer(CFRP),experiments were conducted with unidirectional laminates.The results show that the influence of cutting parameters is ...To investigate cutting performance in the helical milling of carbon fiber reinforced polymer(CFRP),experiments were conducted with unidirectional laminates.The results show that the influence of cutting parameters is very significant in the helical milling process. The axial force increases with the increase of cutting speed, which is below 95 m/min; otherwise, the axial force decreases with the increase of cutting speed. The resultant force always increases when cutting speed increases; with the increase of tangential and axial feed rates, cutting forces increase gradually. In addition, damage rings can appear in certain regions of the entry edges; therefore, the relationship between machining performance(cutting forces and holemaking quality) and cutting parameters is established using the nonlinear fitting methodology. Thus, three cutting parameters in the helical milling of CFRP, under the steady state, are optimized based on the multi-objective genetic algorithm, including material removal rate and machining performance. Finally, experiments were carried out to prove the validity of optimized cutting parameters.展开更多
The work done in this work deals with the efficacy of cutting parameters on surface of EN-8 alloy steel.For knowing the optimal effects of cutting parameters response surface methodology was practiced subjected to cen...The work done in this work deals with the efficacy of cutting parameters on surface of EN-8 alloy steel.For knowing the optimal effects of cutting parameters response surface methodology was practiced subjected to central composite design matrix.The motive was to introduce an interaction among input parameters,i.e.,cutting speed,feed and depth of cut and output parameter,surface roughness.For this,second order response surface model was modeled.The foreseen values obtained were found to be fairly close to observed values,showed that the model could be practiced to forecast the surface roughness on EN-8 within the range of parameter studied.Contours and 3-D plots are generated to forecast the value of surface roughness.It was revealed that surface roughness decreases with increases in cutting speed and it increases with feed.However,there were found negligible or almost no implication of depth of cut on surface roughness whereas feed rate affected the surface roughness most.For lower surface roughness,the optimum values of each one were also evaluated.展开更多
In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must...In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. This paper proposes the Particle Swarm Optimization (PSO) algorithm for estimating the parameters such a curve. The PSO algorithm is an evolutional method based on a very simple concept. Comparison of PSO results with those of GA and LS methods showed that the PSO algorithm is more effective for estimating the parameters of the above curve.展开更多
Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tuna...Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.展开更多
The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort.In this paper,an effective optimization method is proposed to reduce the water contamin...The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort.In this paper,an effective optimization method is proposed to reduce the water contamination on the side windows of automobiles.The accuracy and the efficiency of the numerical simulation are improved by using the lattice Boltzmann method,and the Lagrangian particle tracking method.Optimized parameters are constructed on the basis of the occurrence of the water deposition on a vehicle’s side window.The water contamination area of the side window and the aerodynamic drag are considered simultaneously in the design process;these two factors are used to form the multi-objective optimization function in the genetic algorithm(GA)method.The approximate model,the boundary-seeded domain method,and the GA method are combined in this study to enhance the optimization efficiency.After optimization,the optimal parameters for the A-pillar section are determined by setting the boundary to an area of W=7.77 mm,L=1.27 mm and H=11.22 mm.The side window’s soiling area in the optimized model is reduced by 66.93%,and the aerodynamic drag is increased by 0.41%only,as compared with the original model.It is shown that the optimization method can effectively solve the water contamination problem of side windows.展开更多
Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One s...Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi- objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling （multivariate adaptive regression splines, MARS） for a conceptual rainfall-runoff model （Xin＇anjiang model, XAJ） was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume （REPF and RERV）. The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate model- ing. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金Supported by the National Natural Science Foundation of China(50975141)the Aviation Science Fund(20091652018,2010352005)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2012ZX04003031-4)
文摘To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.
文摘To solve the problem of difficulty in selecting NC cutting parameters by the redundancy technique, a method is put forward to optimize cutting parameters based on a revolutionary mathematical model and a revolutionary cutting parameters database. By use of fuzzy inference rules, it can not only make the method itself evolved and updated, but also ensure data to be correct and feasible from the two optimization routes. Practical running and testing proved that this method can facilitate for the user to select parameters and greatly improve the processing efficiency.
基金supported by the Natural Science Foundation of Hebei Province,China (No.E2014501077)Natural Science Foundation of China (No.51275345)
文摘To investigate cutting performance in the helical milling of carbon fiber reinforced polymer(CFRP),experiments were conducted with unidirectional laminates.The results show that the influence of cutting parameters is very significant in the helical milling process. The axial force increases with the increase of cutting speed, which is below 95 m/min; otherwise, the axial force decreases with the increase of cutting speed. The resultant force always increases when cutting speed increases; with the increase of tangential and axial feed rates, cutting forces increase gradually. In addition, damage rings can appear in certain regions of the entry edges; therefore, the relationship between machining performance(cutting forces and holemaking quality) and cutting parameters is established using the nonlinear fitting methodology. Thus, three cutting parameters in the helical milling of CFRP, under the steady state, are optimized based on the multi-objective genetic algorithm, including material removal rate and machining performance. Finally, experiments were carried out to prove the validity of optimized cutting parameters.
文摘The work done in this work deals with the efficacy of cutting parameters on surface of EN-8 alloy steel.For knowing the optimal effects of cutting parameters response surface methodology was practiced subjected to central composite design matrix.The motive was to introduce an interaction among input parameters,i.e.,cutting speed,feed and depth of cut and output parameter,surface roughness.For this,second order response surface model was modeled.The foreseen values obtained were found to be fairly close to observed values,showed that the model could be practiced to forecast the surface roughness on EN-8 within the range of parameter studied.Contours and 3-D plots are generated to forecast the value of surface roughness.It was revealed that surface roughness decreases with increases in cutting speed and it increases with feed.However,there were found negligible or almost no implication of depth of cut on surface roughness whereas feed rate affected the surface roughness most.For lower surface roughness,the optimum values of each one were also evaluated.
文摘In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. This paper proposes the Particle Swarm Optimization (PSO) algorithm for estimating the parameters such a curve. The PSO algorithm is an evolutional method based on a very simple concept. Comparison of PSO results with those of GA and LS methods showed that the PSO algorithm is more effective for estimating the parameters of the above curve.
基金supported by the Hunan Provincial Natural Science Foundation of China(No.2023JJ40686).
文摘Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.
基金Project supported by the National Science Foundation of China(Grant No.51875238).
文摘The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort.In this paper,an effective optimization method is proposed to reduce the water contamination on the side windows of automobiles.The accuracy and the efficiency of the numerical simulation are improved by using the lattice Boltzmann method,and the Lagrangian particle tracking method.Optimized parameters are constructed on the basis of the occurrence of the water deposition on a vehicle’s side window.The water contamination area of the side window and the aerodynamic drag are considered simultaneously in the design process;these two factors are used to form the multi-objective optimization function in the genetic algorithm(GA)method.The approximate model,the boundary-seeded domain method,and the GA method are combined in this study to enhance the optimization efficiency.After optimization,the optimal parameters for the A-pillar section are determined by setting the boundary to an area of W=7.77 mm,L=1.27 mm and H=11.22 mm.The side window’s soiling area in the optimized model is reduced by 66.93%,and the aerodynamic drag is increased by 0.41%only,as compared with the original model.It is shown that the optimization method can effectively solve the water contamination problem of side windows.
基金Acknowledgements The work was supported by the National Basic Research Program of China （No. 2010CB951103）, the National Natural Science Foundation of China （Grant Nos. 41330854, 41371063 and 51309155） and the National Science ＆ Technology Pillar Program during the 12th Five-year Plan Period （2012BAC21B01 and 2012BAC19B03）. We are also thankful to anonymous reviewers and editors for their helpful comments and suggestions.
文摘Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi- objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling （multivariate adaptive regression splines, MARS） for a conceptual rainfall-runoff model （Xin＇anjiang model, XAJ） was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume （REPF and RERV）. The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate model- ing. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.
