Application of Neural Network in Predicting Forging Hardness

Yuan Ping Luh; Chang Hung Tu; Huang Li Wang; Chien Jung Chiu

doi:10.4028/www.scientific.net/SSP.305.169

Paper Titles

Implementation of Neural Network in Classifying the Product Quality for the ABS Metallization
p.139

Defect Reduction in Forming Process of Fired Clay Floor Tiles by Six Sigma Approach
p.147

Chipping Size Reduction on Ultra-Thin Wafers and Narrow Saw-Streets for Wafer Sawing Process
p.154

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network
p.163

Application of Neural Network in Predicting Forging Hardness
p.169

Simulation Based Mold Design Optimization of a Spring Flap Casting
p.178

Comparative Study of Optical Silicon Nanomachining Experimental Results and MD Simulation Outputs
p.185

Effect of Process Parameters on Surface Roughness in Surface Grinding of 90CrSi Tool Steel
p.191

Finite Element Stress Characterization of Stacked Die Package for Reliability Improvement and Architecture Optimization
p.198

HomeSolid State PhenomenaSolid State Phenomena Vol. 305Application of Neural Network in Predicting...

Application of Neural Network in Predicting Forging Hardness

Abstract:

This study used cold forging to produce car components with the desired hardness without using postprocessing methods such as heat treatment, surface blasting, and polishing. Material diameter, mold neck diameter, and hardness of the material after annealing were used as parameters, and nine sets of experimental parameters were obtained using the Taguchi method. Under the premise of high-quality forging, this study determined the optimal hardness and the relationship between formation parameters and forging hardness. By inputting the hardness data obtained from the Taguchi L9 orthogonal array into a Matlab-implemented neural network, this study determined a hardness formula. Finally, using the inbuilt graphical user interface software of Matlab, a simple program was written that can be used to predict hardness and that could extensively reduce the costs and time associated with forging development.

You might also be interested in these eBooks

Manufacturing Sciences and Technologies X

View Preview

Info:

Periodical:

Solid State Phenomena (Volume 305)

Pages:

169-177

DOI:

https://doi.org/10.4028/www.scientific.net/SSP.305.169

Citation:

Cite this paper

Online since:

June 2020

Authors:

Yuan Ping Luh*, Chang Hung Tu, Huang Li Wang, Chien Jung Chiu

Keywords:

CAE, Cold Forging, Hardness, Neural Network, Taguchi Method

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] F.T. Mahi, D. Schmoeckel: Metal Forming (Warm): Comparison with Hot and Cold Forming (Elsevier BV, Netherlands 2016).

DOI: 10.1016/b978-0-12-803581-8.03564-5

Google Scholar

[2] W.K. Shi, S.H. Li, W.C. Yao, Y.K. Fuh, S.C. Tsai, and T.K. Sue: Molding Technology Handbook Series—Technical Manual for Forging Mold (Metal Industries Research & Development Centre Publications, Taiwan 2002).

Google Scholar

[3] Y.P. Luh, H.L. Wang, J.R. Ciou and J.F. Jhang: IOP Conference Series: Materials Science and Engineering Vol. 538 (2019), pp.1-12.

Google Scholar

[4] H.H. Lee, in: Taguchi Methods: Principles and Practices of Quality Design, Gau Lih Book Publishing, New Taipei City, Taiwan (2013).

Google Scholar

[5] K.Y. Huang, in: Neural Networks, Chuan Hwa Publishing, Taipei, Taiwan (2017).

Google Scholar

[6] H. Tumer, F.O. Sonmez: Journal of materials processing technology Vol. 209 (2009), pp.1538-1549.

Google Scholar

[7] F.O. Sonmez, A. Demir: Journal of Materials Processing Technology Vol. 186 (2007), pp.163-173.

Google Scholar

[8] H. Kim, S.M. Lee, T. Altan: Journal of Materials Processing Technology Vol. 59 (1996) pp.113-121.

Google Scholar

[9] X. Zhang, D. Mu: Physical Testing and Chemical Analysis part A:Physical Testing Vol. 53(6) (2017), pp.410-412.

Google Scholar

[10] Information on http://www.yrmade.net/ydjtech.html.

Google Scholar

[11] S. Huang, D. Biao, F. Zhou, S. Hua: Physical Testing and Chemical Analysis part A:Physical Testing Vol. 50(2) (2014), pp.127-130.

Google Scholar

[12] R. Jang, in: Introduction to MATLAB Programming, GOTOP Information Publishing, Taipei, Taiwan (2015).

Google Scholar