Open Access   Article

Automatic die design and fatigue life prediction of forming die using AI technique: Expert System

M.R Bhatt1 , H Mehta2 , S.H Buch3

1 School of Engineering, RK University, Rajkot, India.
2 School of Engineering, RK University, Rajkot, India.
3 School of Engineering, RK University, Rajkot, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 20-30, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.2030

Online published on Apr 30, 2018

Copyright © M.R Bhatt, H Mehta, S.H Buch . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

Citation

IEEE Style Citation: M.R Bhatt, H Mehta, S.H Buch, “Automatic die design and fatigue life prediction of forming die using AI technique: Expert System”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.20-30, 2018.

MLA Style Citation: M.R Bhatt, H Mehta, S.H Buch "Automatic die design and fatigue life prediction of forming die using AI technique: Expert System." International Journal of Computer Sciences and Engineering 6.4 (2018): 20-30.

APA Style Citation: M.R Bhatt, H Mehta, S.H Buch, (2018). Automatic die design and fatigue life prediction of forming die using AI technique: Expert System. International Journal of Computer Sciences and Engineering, 6(4), 20-30.

VIEWS PDF XML
160 226 downloads 32 downloads
  
  
           

Abstract

Sheet metal forming is an important process which causes some changes in the shape of solid metal parts via plastic (permanent) deformation. When deliberating with sheet metal forming process in this scenario, die and punch cost plays a vital role, making the processes costlier in whole production cycle. It is required to estimate the die life because it is repeatedly used in manufacturing process. Approximate calculation of fatigue life of axisymmetric forming dies helps in planning for the production. This is calculated using AI technique Expert system. In present research work, the development of expert system has been done using VB, python and AutoCAD environment. The developed ES is enabling to generate manufacturing drawing of the designed die which requires few input parameters. Based on few input parameters, ES predict the fatigue life of these dies during deep drawing forming operations.

Key-Words / Index Term

Deep Drawing, Die Design, Fatigue, Expert System(ES), AI

References

[1] S. Q. Xie, and J. Q. Liu, “Integrated concurrent approach for compound sheet metal cutting and punching”, International Journal of Production Research, Vol.39, Issue.6, pp.1095–1112, 2001
[2] K. V. Ramana, and P. V. M. Rao, “Automated manufacturability evaluation system for sheet metal components in mass production”, International Journal of Production Research, Vol.43, Issue.B, pp.3889-3913, 2005
[3] M. Ghatrehnaby, and B. Arezoo, “A fully automated nest-ing and piloting system for progressive dies”, Journal of Materials Processing Technology, Vol.209, Issue.1, pp.525–535, 2009
[4] D. Y. Kim, and J. J. Park, “Development of an expert sys-tem for the process design of axisymmetric hot steel forging”, Journal of Materials Processing Technology, Vol-01, Issue.1-3, pp.223-230, 2000
[5] M. S. Kim, J. C. Choi, Y. H. Kim, G. J. Huh, and C. Kim, “An Automated Process Planning and Die Design System for Quasi-Axisymmetric Cold Forging Products”, International Journal of Advanced Manufacturing Technology, Vol.20 Issue.3, pp. 201–213, 2002
[6] R. Ravi, Y. V. R. K. Prasad, and V. V. S. Sarma, “Development of Expert Systems for the Design of a Hot-Forging Process Based on Material Workability”, Journal of Materials Engineering and Performance, Vol.12, pp. 646-652, 2003
[7] T. Ohashi, S. Imamura, T. Shimizu, M. Motomur, “Computer-aided die design for axis-symmetric cold forging products by feature elimination”, Journal of Materials Processing Technology, Vol.137, issue.1-3, ,pp. 138–144, 2003
[8] S. J. Lee, T. S. Kim, S. S. Lee, and K. S. Park, “Development of an Expert System for the Trim Die Design in Au-tomotive Industry”, In Tenth Computer Supported Cooperative Work in Design Conference (CSCWD), IEEE pp.1-6, 2006
[9] X. Zhang, M. Lu, P. Su, G. Xu, and H. Zhao, “Research on Neural Network Integration Fusion Method and Application on the Fault diagnosis of Automotive Engine”, In the Proceedings of the 2007 IEEE Second Industrial Electronics and Applications Conference (ICIEA 2007), IEEE. pp.48, 2007
[10] T. Giannakakis, and G. C. Vosniakos, “Sheet metal cutting and piercing operations planning and tools configuration by an expert system”, International Journal of Advanced Manufacturing Technology, Vol.36 Issue.7-8,, pp. 658–670, 2008
[11] X. Zhang, C. Y. Han, Y. Cui, Y. P. Lu, E.F. Liu, and H. B.Cui, “An Active Knowledge Support System for Design of Automobile Ball”, in International Workshop on Intelli-gent Systems and Applications (ISA 2009), IEEE, pp. 1-4, 2009
[12] A. B. Johnston, L. P. Maguire, and T. M. McGinnity, “Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques”, Robotics and Computer-Integrated Manufacturing, vol.25, Issue.3, pp. 513– 521, 2009
[13] T. M. Dale, W. A. Young, and R. P. Judd, “A rule-based approach to predict forging volume for cost estimation during product design”, International Journal of Advanced Manufacturing Technology, Vol.46 Issue.1-4, pp.31–41,2010
[14] K. VeeraBabu, R. Ganesh Narayanan, and G. Saravana Kumar, “An expert system for predicting the deep drawing behavior of tailor welded blanks”, Expert Systems with Applications, Vol.37, pp. 7802–7812, 2010
[15] M.V.Jagannatha Reddy , B.Kavitha, “Expert System to Predict the Type of Fever Using Data Mining Techniques on Medical Databases”, International Journal of Computer Science and Engineering, Vol.3 , Issue.9 , pp. 165-171, 2015
[16] B. Falk, U. Engel, and M. Gieger, “Fundamental aspects for the evaluation of the fatigue behavior of cold forging tools”, Journal of Materials Processing Technology, Vol.119, pp.158-164, 2001
[17] P. Skov-Hansen, N. Bay, J. Grùnbñk, and P. Brùndsted, “Fatigue in cold-forging dies: tool life analysis”, Journal of Materials Processing Technology, Vol.95, pp. 40-48, 1999
[18] ABAQUS user guide, 2013
[19]S. Kashid, and S. Kumar, “prediction of life of punches of compound die using artificial neural network”, in 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014), vol.62, pp. 1-6, 2014
[20]V. H. Shankar, V. R. Kumar, and N. V. S. Shankar, “fatigue analysis of polymer composite deep drawing die”, International Journal of Mechanical Engineering and Robotics Research, Vol.4, Issue.2, pp. 22-29,2015
[21] A. V. Valsov, Thermomechanical Fatigue of Dies for Hot Stamping, in Steel In Translation, Vol.46, Isuue.5 pp. 356-360,2016
[22] V. B. Bhandari, Design of Machine Elements, Third Ed., Tata McGrow Hill Edu. Pvt. Ltd., 2013.
[23] AUTOCAD user manual, 2010
[24]ASM International, Fatigue, Elements of Metallurgy and Engineering Alloys, 2008.