Application of Genetic Algorithm in Cutting Parameter Optimization

Process parameters optimization is an important problem in numerical control machining, through the analysis of various factors affecting the cutting effect in cutting process, a mathematical model of cutting parameter optimization in NC machining is established and the constraint conditions are also determined in the paper. The article puts forward using genetic algorithm to realize the optimization of mathematical model, and the optimization analysis results are verified in practical processing. The experimental results show that the optimized cutting parameters can satisfy machining requests and improve the cutting efficiency.

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Research on High Speed Cutting Parameter Optimization and Fault Diagnosis Technology

Advances in Mechanical Engineering ◽

10.1155/2014/281216 ◽

2014 ◽

Vol 6 ◽

pp. 281216 ◽

Cited By ~ 2

Author(s):

Honggen Zhou ◽

Xuwen Jing ◽

Lei Wang ◽

Kaiyun Dai ◽

Jia Yongpeng

Keyword(s):

Mathematical Model ◽

Fault Diagnosis ◽

Parameter Optimization ◽

High Speed ◽

Cutting Parameters ◽

Cutting Process ◽

High Speed Cutting ◽

Cutting Parameter ◽

Cutting Parameter Optimization ◽

The Mathematical Model

High speed cutting process is a very complicated process; cutting parameters have a significant effect on cutting process and play a key role in the process of product manufacturing. The overall scheme of high speed cutting parameter optimization and its fault diagnosis have been introduced. The mathematical model of the selected cutting parameters was established and the optimized parameters were obtained by combining the experimental design with the technology of data processing. The statistical description of high speed cutting process control was introduced and the fault diagnosis model of cutting parameter optimization by using the neural network was proposed. Finally, the mathematical model in the present study is validated with a numerical example. The results show that the present method solved the problem of poor universality of high speed cutting data effectively and avoided the inaccuracy of physical and chemical mechanism research. Meanwhile, the present study prevents the passive checks of the cutting and gets better diagnosis of the complicated cutting fault type.

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Energy efficient cutting parameter optimization

Frontiers of Mechanical Engineering ◽

10.1007/s11465-020-0627-x ◽

2021 ◽

Author(s):

Xingzheng Chen ◽

Congbo Li ◽

Ying Tang ◽

Li Li ◽

Hongcheng Li

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Parameter Optimization ◽

Energy Efficient ◽

Cutting Parameters ◽

Machining Process ◽

Cutting Fluid ◽

Current Status ◽

Cutting Parameter ◽

Cutting Parameter Optimization

AbstractMechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

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Cutting Parameter Optimization Technique for High Efficiency NC Machining

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.211-212.167 ◽

2011 ◽

Vol 211-212 ◽

pp. 167-171

Author(s):

An Jiang Cai ◽

Shi Hong Guo ◽

Zhao Yang Dong ◽

Hong Wei Guo

Keyword(s):

Neural Network ◽

Parameter Optimization ◽

Bp Neural Network ◽

High Efficiency ◽

Cutting Parameters ◽

Nc Machining ◽

Processing Efficiency ◽

Cutting Parameter ◽

Cutting Parameter Optimization ◽

Bp Neural Network Model

High efficient cutting process technique is one of the main development directions of cutting process technology in the future, a reasonable choice of NC machining cutting parameter is an important way to realize high efficiency NC machining. NC machining cutting parameter optimization techniques were studied, using BP neural network, milling parameters optimization model of aluminum alloy shell structure was built, and the structure of BP neural network was analysed, realizing the optimizing of the BP neural network model, the improving of the convergence accuracy, convergence speed, prediction accuracy, generalization ability of BP neural network model, which optimized the cutting parameters selection and predicted the processing efficiency to provide a theoretical basis for the selection of high efficiency NC machining cutting parameter. Production practice showed: the application of the optimized cutting parameters of BP neural network for processing could improve processing efficiency, reduce costs notablely while guaranteeing the processing quality, and achieve the optimization of integrated application efficiency for high efficiency NC machining and NC machine, so it has a higher promotional value.

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