Research on Parameters Optimization of Virtual NC Milling Based on Parallelism Selection Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3498-3503
Author(s):  
Xiu Lin Sui ◽  
Zheng Wei Kong ◽  
Jiang Hua Ge ◽  
Jia Tai Zhang ◽  
Li Juan Jin
Keyword(s):  
Genetic Algorithm ◽  
Physical Simulation ◽  
Parameters Optimization ◽  
Technology Support ◽  
Non Linear Programming ◽  
Milling Parameters ◽  
System A ◽  
Nc Milling ◽  
Optimized Model ◽  
Set Up

To resolve key technology of parameter optimization of virtual NC milling physical simulation system, a multi-objectives optimized model was set up by non-linear programming theory and resolved by improved parallelism selection genetic algorithm. It was optimized when milling characters are sufficiently considered and machine quality was guaranteed. Sub-population size is decided by weighting each optimized object and calculating adaptation to assure importance of optimized objects and fasten convergence velocity. A kind of software that can optimize milling parameters of virtual NC was developed, and optimized strategy and validity of algorithm are verified which provides technology support for realizing virtual NC milling physical system.

Application of Genetic Algorithm for Nozzle Parameters Optimization of Waterjet Propulsion System

2012 ◽  
Vol 157-158 ◽  
pp. 604-607
Author(s):  
Xuan Ling ◽  
Xu Dong Wang
Keyword(s):  
Genetic Algorithm ◽  
Cfd Simulation ◽  
Simulation Analysis ◽  
Optimization Technique ◽  
Propulsion System ◽  
Parameters Optimization ◽  
Integrated Method ◽  
System A ◽  
Design Cycle ◽  
Computer Based

Waterjet propulsion system have been increasingly used in the world due to its advantage of good maneuverability, operability, less vibration etc. The full understanding of waterjet reaction thrust is the preliminary step for the design of waterjet system. A recent research in this area is optimizing the nozzle structure of waterjet propulsion system to increase the waterjet reaction thrust as much as possible. In order to obtain the optimal parameters of nozzle, a new integrated method combining genetic algorithm with CFD simulation analysis is put forward in this paper. The integrated method will not only shorten the system design cycle, it will also develop optimization technique to realize the potential of computer based design automation. Finally, the optimal results are presented and discuss.

Genetic Algorithm-Based Parameters Optimization for the PID Controller Applied in Heave Compensation System

2014 ◽  
Vol 556-562 ◽  
pp. 2462-2465
Author(s):  
You Gang Sun ◽  
Hai Yan Qiang ◽  
Xiao Ming Sheng
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Negative Impact ◽  
Dynamic Performance ◽  
Compensation System ◽  
Integrated System ◽  
Parameters Optimization ◽  
Control Effect ◽  
System A ◽  
The Stability

To reduce the negative impact of the unexpected motion in the heave direction when hoisting on the sea, a heave compensation system is indispensable for a floating crane. In this paper, a genetic algorithm-based PID controller is presented to improve the dynamic performance of the heave compensation system. A simulation model of electro-mechanical and hydraulic integrated system of the heave compensation system is built. The simulation results show that the genetic algorithm-based PID control system improves the dynamic characteristics, strengthens the stability and rapidity of the system and even ensures the control effect of the heave compensation system.

Reliability-based NC milling parameters optimization using ensemble metamodel

2018 ◽  
Vol 97 (9-12) ◽  
pp. 3359-3369 ◽  
Author(s):  
Xiaoke Li ◽  
Jinguang Du ◽  
Zhenzhong Chen ◽  
Wuyi Ming ◽  
Yang Cao ◽  
...  
Keyword(s):  
Parameters Optimization ◽  
Milling Parameters ◽  
Nc Milling

New hybrid NSGA-III&SPEA/R to multi-object optimization in a half-car dynamic model

2019 ◽  
Vol 234 (6) ◽  
pp. 1660-1671
Author(s):  
Dinh-Nam Dao ◽  
Li-Xin Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Mean Square Displacement ◽  
Parameters Optimization ◽  
Simultaneous Optimization ◽  
Mean Square ◽  
Multi Objective ◽  
System A ◽  
Strength Pareto Evolutionary Algorithm ◽  
Optimal Values

In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic Algorithm II (NSGA-III) and SPEA/R (HNSGA-III&SPEA/R). This method is implemented to find the optimal values of the powertrain mount system stiffness parameters. This is the task of finding multi-objective optimization involving six simultaneous optimization goals: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R has proposed with the integration of Strength Pareto evolutionary algorithm-based reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R and NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.

Research and Application of Milling Parameters Optimization Based on Genetic Algorithm

2015 ◽  
Vol 1095 ◽  
pp. 820-823
Author(s):  
Min Min Hu ◽  
Yu Zhang ◽  
Sheng Wan Yuan
Keyword(s):  
Genetic Algorithm ◽  
Surface Quality ◽  
Production Cost ◽  
Production Efficiency ◽  
Cutting Parameters ◽  
Parameters Optimization ◽  
Milling Parameters ◽  
Cutting Test ◽  
High Production Efficiency ◽  
High Production

At present a number of machinery factories select cutting parameters mainly rely on the experience in China. In order to get the reasonable cutting parameters and then have a optimal machining surface quality, high production efficiency and low production cost. In this paper, using genetic algorithm and Matlab software [1] to optimize cutting parameters of milling and combining with the actual cutting test in factory to verify the optimized data. And then provide theory basis and feasible measure for milling.

Optimization of the OOP Children Restraint System

2012 ◽  
Vol 155-156 ◽  
pp. 386-390
Author(s):  
Zhong Hao Bai ◽  
Jing Fei ◽  
Wei Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Optimization Method ◽  
Approximate Model ◽  
Parameters Optimization ◽  
Fe Model ◽  
Restraint System ◽  
Effective Protection ◽  
Multi Body

Based on the study of SAE J1980-2008 and FMVSS 208, MADYMO7.1 is used to establish a Multi-body and FE model for two OOP children, and the statistic test is implemented to verify the accuracy of the model. The airbag parameters impacting OOP children greatly and their ranges are selected to determine the objective function. With the Latin Hypercube Sampling method, the Kring approximate model is constructed, and multi-island genetic algorithm is used in subsequently parameters optimization. The results show that the proposed optimization method can provide effective protection for 6-year-old OOP children.

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