Prediction of life of deep drawing die using artificial neural network

References

• Kusiak A. Intelligent manufacturing systems. Upper Saddle River, NJ: Prentice Hall; 1990.
   Google Scholar
• Fu W, Lu J, Chan WL. Die fatigue life improvement through the rational design of metal-forming system. J. Mater. Process. Technol. 2009;209:1074–1084.10.1016/j.jmatprotec.2008.03.016
   Web of Science ®Google Scholar
• Wang Q. Artificial neural networks as cost engineering methods in a collaborative manufacturing environment. Int. J. Prod. Econ. 2007;109:53–64.10.1016/j.ijpe.2006.11.006
   Web of Science ®Google Scholar
• Johnston AB, Maguireb LP, McGinnity TM. Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques. Rob. Comput. Integr. Manuf. 2007;25:513–521.
   Web of Science ®Google Scholar
• Veera K, Babu R. An expert system for predicting the deep drawing behavior of tailor welded blanks. Expert Syst. App. 2010;37:7802–7812.10.1016/j.eswa.2010.04.059
   Web of Science ®Google Scholar
• Tian Z. An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. J. Intell. Manuf. 2012;23:227–237.10.1007/s10845-009-0356-9
   Web of Science ®Google Scholar
• Radha Ramanan T, Sridharan R, Shashikant KS, et al. An artificial neural network based heuristic for flow shop scheduling problems. J. Intell. Manuf. 2011;22:279–288.
   Web of Science ®Google Scholar
• Lange K, Hettig A, Knoerr M. Increasing tool life in cold forging through advanced design and tool manufacturing techniques. J. Mater. Process. Technol. 2011;35:495–513.
   Web of Science ®Google Scholar
• Knoerr M, Lange K, Altan T. Fatigue failure of cold forging tooling: causes and possible solutions through fatigue analysis. J. Mater. Process. Technol. 1994;46:57–71.10.1016/0924-0136(94)90102-3
   Web of Science ®Google Scholar
• Lapovok R. Improvement of die life by minimisation of damage accumulation and optimisation of preform design. J. Mater. Process. Technol. 1998;80:608–612.10.1016/S0924-0136(98)00213-1
   Web of Science ®Google Scholar
• Skov-Hansen P, Bay N, Gronbaek J, et al. Fatigue in cold-forging dies: tool life analysis. J. Mater. Process. Technol. 1999;95:40–48.10.1016/S0924-0136(99)00319-2
   Web of Science ®Google Scholar
• Vazquez V, Hannan D, Altan T. Tool life in cold forging - an example of design improvement to increase service life. J. Mater. Process. Technol. 2000;98:90–96.10.1016/S0924-0136(99)00309-X
   Web of Science ®Google Scholar
• Jeong DJ, Kim DJ, Kim JH, et al. Effects of surface treatments and lubricants for warm forging die life. J. Mater. Process. Technol. 2001;113:544–550.10.1016/S0924-0136(01)00693-8
   Web of Science ®Google Scholar
• Arif AFM, Sheikh AK, Qamar SZ. A study of die failure mechanisms in aluminum extrusion. J. Mater. Process. Technol. 2003;134:318–328.10.1016/S0924-0136(02)01116-0
   Web of Science ®Google Scholar
• Kim DH, Kim BM, Kang CG. Die life considering the deviation of the preheating billet temperature in hot forging process. Finite Elem. Anal. Des. 2005;41:1255–1269.10.1016/j.finel.2004.11.005
   Web of Science ®Google Scholar
• Fu MW, Yong MS, Tong K, et al. A methodology for evaluation of metal forming system design and performance via CAE simulation. Int. J. Prod. Res. 2006;44:1075–1092.10.1080/00207540500337643
   Web of Science ®Google Scholar
• Saroosh MA, Lee HC, Im YT, et al. High cycle fatigue life prediction of cold forging tools based on workpiece material property. J. Mater. Process. Technol. 2007;191:178–181.10.1016/j.jmatprotec.2007.03.015
   Web of Science ®Google Scholar
• Jen YM, Chang LY, Fang CF. Assessing the fatigue life of butt-welded joints under oblique loading by using local approaches. Int. J. Fatigue. 2008;30:603–613.10.1016/j.ijfatigue.2007.05.011
   Web of Science ®Google Scholar
• Chen Z, Li G, Zhang H, et al. Fatigue life prediction of regulating valves on the intermediate-pressure section of a 400MW steam turbine. Eng. Fail. Anal. 2009;16:1483–1492.10.1016/j.engfailanal.2008.09.033
   Web of Science ®Google Scholar
• Tunga K, Sitaraman SK. Laser moiré interferometry for fatigue life prediction of lead-free solders. Microelectron. Reliab. 2010;50:2026–2036.10.1016/j.microrel.2010.05.005
   Web of Science ®Google Scholar
• Shinohara K, Yu O. Fatigue life evaluation accuracy of power devices using finite element method. Int. J. Fatigue. 2011;33:1221–1234.10.1016/j.ijfatigue.2011.03.009
   Web of Science ®Google Scholar
• Kang HT, Lee YL.(2011) Fatigue life prediction methods of seam-welded joints. In Y. L. Lee, M. E. Barkey, H.T. Kang (Ed.), Metal Fatigue Analysis Handbook: Practical problem-solving techniques for computer-aided engineering (383–423). Oxford: UK.
   Google Scholar
• Kannan S, Srinivasan SM. Influence of manufacturing processes and their sequence of execution on fatigue life of axle house tubes in automobiles. Fatigue Fail. Anal. 2013;34:79–92.10.1016/j.engfailanal.2013.07.013
   Web of Science ®Google Scholar
• Patel VK, Bhole SD, Chen DL. Fatigue life estimation of ultrasonic spot welded Mg alloy joints. Mater. Des. 2014;62:124–132.
   Web of Science ®Google Scholar
• Newman JA, Dowling NE. A crack growth approach to life prediction of spot-welded lap joints. Fatigue Fract. Eng. Mater. Struct. 1998;21:1123–1132.10.1046/j.1460-2695.1998.00099.x
   Web of Science ®Google Scholar
• Naranje V, Kumar S. An intelligent CAD system for automatic modelling of deep drawing die. Int. J. Comput. Appl. Technol. 2013;48:330–344.10.1504/IJCAT.2013.058355
   Google Scholar
• Bannantine J, Comer J, Handrock J. Fundamentals of metal fatigue analysis. Upper Saddle River, NJ: Prentice Hall; 1990.
   Google Scholar
• Swales GS, Yoon Y. Applying artificial neural networks to investment analysis. Financ. Anal. J. 1992;48:70–80.
   Google Scholar