The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosi...Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.展开更多
Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design r...Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.展开更多
As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is ...As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.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
Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImpr...Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.展开更多
Parameter optimization integrating operation parameters and structure parameters for the purpose of high permeate flux,high productivity and low exergy consumption of direct contact membrane distillation (DCMD) proces...Parameter optimization integrating operation parameters and structure parameters for the purpose of high permeate flux,high productivity and low exergy consumption of direct contact membrane distillation (DCMD) process was conducted based on Taguchi experimental design. L16(45) orthogonal experiments were carried out with feed inlet temperature,permeate stream inlet temperature,flow rate,module packing density and length-diameter ratio as optimization parameters and with permeate flux,water productivity per unit volume of module and water production per unit exergy loss separately as optimization objectives. By using range analysis method,the dominance degree of the various influencing factors for the three objectives was analyzed and the optimum condition was obtained for the three objectives separately. Furthermore,the multi-objectives optimization was performed based on a weight grade method. The combined optimum conditions are feed inlet temperature 75℃,packing density 30% ,length-diameter ratio 10,permeate stream inlet temperature 30 ℃ and flow rate 25 L/h,which is in order of their dominance degree,and the validity of the optimization scheme was confirmed.展开更多
Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimize...Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.展开更多
With shrinking technology,the increase in variability of process,voltage,and temperature(PVT) parameters significantly impacts the yield analysis and optimization for chip designs.Previous yield estimation algorithms ...With shrinking technology,the increase in variability of process,voltage,and temperature(PVT) parameters significantly impacts the yield analysis and optimization for chip designs.Previous yield estimation algorithms have been limited to predicting either timing or power yield.However,neglecting the correlation between power and delay will result in significant yield loss.Most of these approaches also suffer from high computational complexity and long runtime.We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic(CAA) and the adaptive weighted sum(AWS) method.Both power and timing yield are set as objective functions in this framework.The two objectives are optimized simultaneously to maintain the correlation between them.The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations.Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function(CDF) bounds.Finally,the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions.Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.展开更多
为获取大跨度桥梁顶推施工临时墩间距、导梁长度及刚度的最优参数,优化主梁内力线形,节约材料成本,提出一种基于非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithmⅡ,NSGA-Ⅱ)遗传算法的主梁线形及材料成本优化方法。以临...为获取大跨度桥梁顶推施工临时墩间距、导梁长度及刚度的最优参数,优化主梁内力线形,节约材料成本,提出一种基于非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithmⅡ,NSGA-Ⅱ)遗传算法的主梁线形及材料成本优化方法。以临时墩最大支反力与主梁截面最大应力为约束条件,建立临时墩间距、导梁长度及刚度与顶推就位主梁线形及材料成本的多目标优化模型。该方法通过NSGA-Ⅱ遗传算法迭代求解ANSYS顶推施工全过程杆系模型,进行联合仿真优化。通过快速非支配排序与精英保留策略得到Pareto前沿优化解集,最后通过逼近理想解排序法(technique for order preference by similarity to an ideal solution, TOPSIS)选出最佳方案。通过具体工程分析,得到的Pareto优化解集收敛性好,可根据不同目标侧重从中选取设计参数,最终推荐临时墩间距59 m,导梁长度35 m,导梁刚度为主梁的0.15倍。研究结果可为相关工程施工方案设计提供参考。展开更多
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金financially supported by the National Key Research and Development Program of China(Grant No.2016YFB0701204)
文摘Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.
基金financially sponsored by National Natural Science Foundation of China(No.50975121)Changchun Science and Technology Plan Projects(No.10KZ03)the Plan for Scientific and Technology Development of Jilin Province(No.20150520106JH)
文摘Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2014AA041503)National Natural Science Foundation of China(Key Program,Grant No.51235003)
文摘As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.
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Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.
文摘Parameter optimization integrating operation parameters and structure parameters for the purpose of high permeate flux,high productivity and low exergy consumption of direct contact membrane distillation (DCMD) process was conducted based on Taguchi experimental design. L16(45) orthogonal experiments were carried out with feed inlet temperature,permeate stream inlet temperature,flow rate,module packing density and length-diameter ratio as optimization parameters and with permeate flux,water productivity per unit volume of module and water production per unit exergy loss separately as optimization objectives. By using range analysis method,the dominance degree of the various influencing factors for the three objectives was analyzed and the optimum condition was obtained for the three objectives separately. Furthermore,the multi-objectives optimization was performed based on a weight grade method. The combined optimum conditions are feed inlet temperature 75℃,packing density 30% ,length-diameter ratio 10,permeate stream inlet temperature 30 ℃ and flow rate 25 L/h,which is in order of their dominance degree,and the validity of the optimization scheme was confirmed.
基金supported by the National Natural Science Foundation of China(Grant Nos.51422603,51356001&51321002)the National Basic Research Program of China("973"Project)(Grant No.2013CB228301)
文摘Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.
文摘With shrinking technology,the increase in variability of process,voltage,and temperature(PVT) parameters significantly impacts the yield analysis and optimization for chip designs.Previous yield estimation algorithms have been limited to predicting either timing or power yield.However,neglecting the correlation between power and delay will result in significant yield loss.Most of these approaches also suffer from high computational complexity and long runtime.We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic(CAA) and the adaptive weighted sum(AWS) method.Both power and timing yield are set as objective functions in this framework.The two objectives are optimized simultaneously to maintain the correlation between them.The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations.Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function(CDF) bounds.Finally,the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions.Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.
文摘为获取大跨度桥梁顶推施工临时墩间距、导梁长度及刚度的最优参数,优化主梁内力线形,节约材料成本,提出一种基于非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithmⅡ,NSGA-Ⅱ)遗传算法的主梁线形及材料成本优化方法。以临时墩最大支反力与主梁截面最大应力为约束条件,建立临时墩间距、导梁长度及刚度与顶推就位主梁线形及材料成本的多目标优化模型。该方法通过NSGA-Ⅱ遗传算法迭代求解ANSYS顶推施工全过程杆系模型,进行联合仿真优化。通过快速非支配排序与精英保留策略得到Pareto前沿优化解集,最后通过逼近理想解排序法(technique for order preference by similarity to an ideal solution, TOPSIS)选出最佳方案。通过具体工程分析,得到的Pareto优化解集收敛性好,可根据不同目标侧重从中选取设计参数,最终推荐临时墩间距59 m,导梁长度35 m,导梁刚度为主梁的0.15倍。研究结果可为相关工程施工方案设计提供参考。
