DOI: 10.14704/nq.2018.16.6.1620

Forming Quality Forecasting for Inner Threads Copper Tubes Based on Neural Network

Chundi Jiang

Abstract


Aims at the defects such as folding and gaps, this paper analyze the relationship between the main processing parameters and inner thread forming quality. Using the fitness function of genetic algorithm to calculate individual fitness value, then the initial weights and thresholds of the Neural Network are assigned, BP Neural Network prediction model based on genetic algorithm is established. The results show that the Neural Networks model has high convergence speed and forecast accuracy, can realize the inner thread forming prediction accurately and improve effectively the forming quality of inner thread.

Keywords


Forming Quality, Neural Network, Forecasting, Inner Threads Copper Tube

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References


Atia TA, Altimari P, Moscardini E, Pettiti I, Toro L, Pagnanelli F. Synthesis and Characterization of Copper Ferrite Magnetic Nanoparticles by Hydrothermal Route. Chemical Engineering Transactions 2016; 47: 151-56.

Chauhan N, Narang J, Rawal R, Pundir CS, A Highly sensitive non-enzymatic ascorbate multi-sensor based on copper nanoparticles bound to multi-walled carbon nanotubes and polyaniline composite. Synthetic Metals 2011; 161(21-22): 2427-33.

Fichera A, Pagano A. A neural tool for the prediction of the experimental dynamics of two-phase flows. International Journal of Heat and Technology 2017; 35(2): 235-42.

Ghritlahre HK, Prasad RK. Investigation on heat transfer characteristics of roughened solar air heater using ANN technique. International Journal of Heat and Technology 2018; 36(1): 102-10.

Gonçalves CP. Quantum neural machine learning: backpropagation and dynamics. NeuroQuantology 2017; 15(1): 22-41.

Jiang CD. Copper casting variable frequency speed control of digital control system. Electronic Mechanical Engineering and Information Technology 2012; 12: 2098-101.

Jiang SY, Ren ZY, Wu B, Wu GX. General issues of FEM in backward ball spinning of thin-walled tubular part with longitudinal inner ribs. Transactions of Nonferrous Metals Society of China 2007; 17(4):793-98.

Lauret P, Heymes F, Aprin L, Johannet A, Slangen P. 2D Modelling of Turbulent Flow Around a Cylindrical Storage Tank by Artificial Neural Networks. Chemical Engineering Transactions 2015; 43(27): 1621-26.

Poletti A, Treville A. Nano and Microsensors: Real time monitoring for the smart and sustainable city. Chemical Engineering Transactions 2016; 47(1): 1-6.

Song XF, Zhan M, Jiang HB, Li T, Yang H. Forming Mechanism of Defects in Spinning of Large Complicated Thin-wall Aluminum Alloy Shells. Journal of Plasticity Engineering 2013; 20(1): 31-36.

Wang TC, Yan H, Zhong SS, Zhang Y. Research of Fire Alarm System Based on Extension Neural Network. Review of Computer Engineering Studies 2015; 2(1): 9-16.

Wu P, Ma QH, Zhu J, Liang HY. The Review of The Application of Magneto-Rheological Fluid and Engineering. Mathematical Modeling of Engineering Problems 2016; 3(2): 7-10.

Zeng Z, Chen JX. Algorithm for Finite Element Mesh Generation Based on Sweeping. Computer Engineering and Application 2013; 49(6): 219-21.

Zhang ZZ, Zhao ZZ, Wang JA, Zhao MZ. Analysis and study on the shaping process of inner grooved copper tube tooth. Forging and Stamping Technology 2005; 30(2): 39-40.

Zhou JF, Wang YL. The Introduction to Horizontal Continuous Casting of TP2 Copper Tube Process System. Copper Fabrication 2011; 3(1): 45-49.


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