Advances of physics-based precision modeling and simulation for manufacturing processes

  • Gang Wang 1 ,
  • Yi-Ming Rong 1 ,
  • 2
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  • 1.  Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, People’s Republic of China
    2. Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609-2280, USA

Received date: 2012-09-17

  Online published: 2012-03-01

Abstract

The development of manufacturing process concerns precision, comprehensiveness, agileness, high efficiency and low cost. The numerical simulation has
become an important method for process design and optimization. Physics-based modeling was proposed to promote simulations with a high accuracy. In this paper, three cases, on material properties, precise boundary conditions, and micro-scale physical models, have been discussed to demonstrate how physics-based modeling can improve manufacturing simulation. By using this method, manufacturing process can be modeled precisely and optimized
for getting better performance.

Cite this article

Gang Wang 1 , Yi-Ming Rong 1 , 2 . Advances of physics-based precision modeling and simulation for manufacturing processes[J]. Advances in Manufacturing, 2013 , 1(1) : 75 -81 . DOI: 10.1007/s40436-013-0005-6

References

1. Fleck NA, Muller GM, Ashby MF et al (1994) Strain gradient plasticity: theory and experiment. Acta Metall Mater 42(2):475–487

2. Stolken JS, Evans AG (1998) A microbend test method for measuring the plasticity length scale. Acta Mater 46(14):5109–5115

3. Vollertsen F, Biermann D, Hansen HN et al (2009) Size effects in manufacturing of metallic components. CIRP Ann-Manuf Technol 58(2):566–587

4. Hu CM, He HL, Hu SS (2003) A study on dynamic mechancial behaviors of 45 steel. Explos Shock Waves 2:188–192

5. Li GH, Wang MJ, Kang RK (2010) Dynamic mechanical properties and constitutive model of Fe-36Ni invar alloy at high temperature and high strain rate. Mater Sci Technol 6:824–828

6. Yu HQ, Cheng JD (1999) Metal forming principle. China Machine, Beijing

7. Johnson GR, Cook WH (1983) A constitutive model and data for metal subjected to large strains, high strain rates and high temperatures. In: Proceedings of the 7th international symposium on ballistics, The Hague, 19–21 April 1983

8. Totten GE, Bates C, Clinton N (1993) Handbook of quenchants and quenching technology. ASM International, Almere
9. Mackenzie DS, Totten GE (2000) Aluminum quenching technology: a review. Mater Sci Forum 331–337:589–594

10. Xiao B, Wang Q, Jadhav P et al (2010) An experimental study of heat transfer in aluminum castings during water quenching. J Mater Process Technol 210:2023–2028

11. Wu Z, Caliot C, Flamant G et al (2011) Numerical simulation of convective heat transfer between air flow and ceramic foams to optimise volumetric solar air receiver performances. Int J Heat  Mass Transf 54:1527–1537

12. Pan JS, Wang J, Gu JF (2012) One of progress in heat treatment numerical simulation: numerical model of heat treatment with expanded solution domain. Heat Treat Met 37(1):7–13

13. MacKenzie DS, Kumar A, Metwally H et al (2009) Prediction of distortion of automotive pinion gears during quenching using CFD and FEA. J ASTM Int 6(1):1–10

14. Xiao BW, Wang QG, Wang G et al (2010) Robust methodology for determination of heat transfer coefficient distribution in convection. Appl Therm Eng 30:2815–2821

15. Wang G, Rong YM (2012) Multiphase model on interfacial heat transfer for water quenching of cylindrical sample. In: TMS 2012 Annual Meeting & Exhibition, Orlando, 11–15 March 2012

16. Kurul N, Podowski MZ (1991) On the modeling of multidimensional effects in boiling channels. In: ANS processing of the 27th national heat transfer conference, Minneapolis

17. Wang G, Rong YM (2012) CFD-based modeling on interfacial heat transfer for water quenching. In: TMS 2012, Orlando, 11–15 March 2012

18. Schiller L, Naumann A (1933) A drag coefficient correlation. VDI Zeits 77:318–320
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