Prediction of mechanical properties of Ti-6Al-4V using neural network

Detak Yan Pratama, Syarif Junaidi, Rizauddin Ramli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Objective of this study is to develop simulation for predicting mechanical properties of Ti-6Al-4V alloy. Rockwell Hardness (HRC), Ultimate tensile strength (UTS) and elongation (ε) are predicted by using Neural Network (NN) with multilayer feedforward architecture. The input of simulations are chemical compositions of Ti-alloy at room temperature. The data of the mechanical properties which are reported by other researchers are used for the NN training and Gradient Descent (GD) and Lavenberg Marquardt (LM) are applied as methods of learning algorithms. The results of training by both methods are compared in order to obtain high performance of output criteria which are determined by a Normalized Root Mean Square Error (NRMSE). is used to determine the performance of output criteria. In training, the NRMSE output calculated by GD algorithm show that HRC, UTS and ε are 0.024, 0.0717 and 0.1375 respectively, while LM algorithm for HRC, UTS and ε are 0.0207, 0.0689 and 0.1150, respectively. The NRMSE predicted output of GD algorithm for HRC, UTS, and ε are 0.0658, 0.0338 and 0.2994, while LM algorithm for HRC, UTS and ε are 0.0371, 0.1192 and 0.5487 respectively. In training, values of NRMSE calculated by LM algorithm is smaller than GD algorithm. These results suggest that LM algorithm shows excellent ability for training, however the GD method is more appropriate for the training algorithm in order to obtain a high performance of output criteria. It can be concluded that the NN can be applied for predicting mechanical properties of Ti-6Al-4V alloys.

Original languageEnglish
Title of host publicationAdvanced Materials Research
Pages443-448
Number of pages6
Volume89-91
DOIs
Publication statusPublished - 2010
Event6th International Conference on Processing and Manufacturing of Advanced Materials - THERMEC'2009 - Berlin
Duration: 25 Aug 200929 Aug 2009

Publication series

NameAdvanced Materials Research
Volume89-91
ISSN (Print)10226680

Other

Other6th International Conference on Processing and Manufacturing of Advanced Materials - THERMEC'2009
CityBerlin
Period25/8/0929/8/09

Fingerprint

Neural networks
Mechanical properties
Tensile strength
Mean square error
Learning algorithms
Elongation
Multilayers
Hardness
Chemical analysis
Temperature

Keywords

  • Learning algortihm
  • Mechanical properties
  • Neural network
  • Prediction
  • Ti-6Al-4V

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Pratama, D. Y., Junaidi, S., & Ramli, R. (2010). Prediction of mechanical properties of Ti-6Al-4V using neural network. In Advanced Materials Research (Vol. 89-91, pp. 443-448). (Advanced Materials Research; Vol. 89-91). https://doi.org/10.4028/www.scientific.net/AMR.89-91.443

Prediction of mechanical properties of Ti-6Al-4V using neural network. / Pratama, Detak Yan; Junaidi, Syarif; Ramli, Rizauddin.

Advanced Materials Research. Vol. 89-91 2010. p. 443-448 (Advanced Materials Research; Vol. 89-91).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pratama, DY, Junaidi, S & Ramli, R 2010, Prediction of mechanical properties of Ti-6Al-4V using neural network. in Advanced Materials Research. vol. 89-91, Advanced Materials Research, vol. 89-91, pp. 443-448, 6th International Conference on Processing and Manufacturing of Advanced Materials - THERMEC'2009, Berlin, 25/8/09. https://doi.org/10.4028/www.scientific.net/AMR.89-91.443
Pratama DY, Junaidi S, Ramli R. Prediction of mechanical properties of Ti-6Al-4V using neural network. In Advanced Materials Research. Vol. 89-91. 2010. p. 443-448. (Advanced Materials Research). https://doi.org/10.4028/www.scientific.net/AMR.89-91.443
Pratama, Detak Yan ; Junaidi, Syarif ; Ramli, Rizauddin. / Prediction of mechanical properties of Ti-6Al-4V using neural network. Advanced Materials Research. Vol. 89-91 2010. pp. 443-448 (Advanced Materials Research).
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