NC Ultrasonic Machining Efficiency: Neural Network-Based Modeling and Simulation

Gao Yan Zhong; Yan Qing Wang

doi:10.4028/www.scientific.net/AMR.291-294.406

Paper Titles

Numerical and Experimental Pore Network Study on Drying of Grain Materials
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Finite Element Simulation Analysis of Thin-Wall Parts Ultrasonic Vibration Turning Device
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Numerical Analysis of RPC-Filled Circular Steel Tube Columns
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Simulation Study on Crash Performance of Vehicle’s Front Longitudinal Rails Based on Material Strain Rate
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NC Ultrasonic Machining Efficiency: Neural Network-Based Modeling and Simulation
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Original Industrial Application of two Numerical Models in Concasting Technology
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Finite Element Design and Numerical Simulation on Non-Pneumatic Beehive Damping Tire
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Effects of U-Shape Inner Cooler on Flow and Solidification of Molten Steel in Slab Continuous Casting Mold
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Tool Errors Compensation in Precision and Ultra-Precision Milling Based on the Least-Square Method
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 291-294NC Ultrasonic Machining Efficiency: Neural...

NC Ultrasonic Machining Efficiency: Neural Network-Based Modeling and Simulation

Abstract:

To explore the impact of abrasive granularity, feed pressure and cutting feed speed on NC ultrasonic machining efficiency, a technological test was carried out, and based on the test results, back propagation (BP) neural network model was established and validated by simulation. The validation process showed that when relative error is less than ±10%, only two samples among 18 tested have larger errors. By the utilization of the BP network for training, correct fitting rate of machining efficiency target can be reached up to 88.9%. Our study indicates that (i) the output of the network is well fitted with the test data, (ii) the established model has good generalization ability to reflect the laws of NC ultrasonic machining process, and (iii) the model is suitable as a prediction tool for NC ultrasonic machining efficiency.

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Periodical:

Advanced Materials Research (Volumes 291-294)

Pages:

406-410

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.291-294.406

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Online since:

July 2011

Authors:

Gao Yan Zhong, Yan Qing Wang

Keywords:

Modeling, NC Ultrasonic Machining Efficiency, Neural Network (NN), Simulation

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