Fig. 6. Comparison of the distortion variation in the Y direction for different thermal conductivities

Fig. 7. Comparison of the distortion variation in the Z direction for different thermal conductivities

Fig. 8. Comparison of the twist variation for different thermal conductivities

For this part, distortion includes the displacements in three directions and the twist (the difference in angle between two initially parallel edges). The validation results are shown in Fig. 5 to Fig. 8. These figures also include the distortion values predicted by conventional injection molding simulation and the three-step model reported in [3].

4 Conclusions

In this paper, an integrated model to accomplish the numerical simulation of injection molding into rapid-prototyped molds is established and a corresponding simulation system is developed. For verification, an experiment is also carried out with an RPfabricated SL mold.

It is seen that a conventional simulation using current injection molding software breaks down for a photopolymer mold. It is assumed that this is due to the distortion in the mold caused by the temperature and load conditions of injection. The three-step approach also has much deviation. The developed model gives results closer to experimental.

Improvement in thermal conductivity of the photopolymer significantly increases part quality. Since the effect of temperature seems to be more dominant than that of pressure (load), an improvement in the thermal conductivity of the photopolymer can improve the part quality significantly.

Rapid Prototyping (RP) is a technology makes it possible to manufacture prototypes quickly and inexpensively, regardless of their complexity. Rapid Tooling (RT) is the next step in RP’s steady progress and much work is being done to obtain more accurate tools to define the parameters of the process. Existing simulation tools can not provide the researcher with a useful means of studying relative changes. An integrated model, such as the one presented in this paper, is necessary to obtain accurate predictions of the actual quality of final parts. In the future, we expect to see this work expanded to develop simulations program for injection into RP molds manufactured by other RT processes.

References

1. Wang KK (1980) System approach to injection molding process. Polym-Plast Technol Eng 14(1):75–93.

2. Shelesh-Nezhad K, Siores E (1997) Intelligent system for plastic injection molding process design. J Mater Process Technol 63(1–3):458–462.

3. Aluru R, Keefe M, Advani S (2001) Simulation of injection molding into rapid-prototyped molds. Rapid Prototyping J 7(1):42–51.

4. Shen SF (1984) Simulation of polymeric flows in the injection molding process. Int J Numer Methods Fluids 4(2):171–184.

5. Agassant JF, Alles H, Philipon S, Vincent M (1988) Experimental and theoretical study of the injection molding of thermoplastic materials. Polym Eng Sci 28(7):460–468.

6. Chiang HH, Hieber CA, Wang KK (1991) A unified simulation of the filling and post-filling stages in injection molding. Part I: formulation. Polym Eng Sci 31(2):116–124.

7. Zhou H, Li D (2001) A numerical simulation of the filling stage in injection molding based on a surface model. Adv Polym Technol 20(2):125–131.

8. Himasekhar K, Lottey J, Wang KK (1992) CAE of mold cooling in injection molding using a three-dimensional numerical simulation. J EngInd Trans ASME 114(2):213–221.

9. Tang LQ, Pochiraju K, Chassapis C, Manoochehri S (1998) Computeraided optimization approach for the design of injection mold cooling systems. J Mech Des, Trans ASME 120(2):165–174.

10. Rizzo FJ, Shippy DJ (1977) An advanced boundary integral equation method for three-dimensional thermoelasticity. Int J Numer Methods Eng 11:1753–1768.

11. Hartmann F (1980) Computing the C-matrix in non-smooth boundary points. In: New developments in boundary element methods, CML Publications, Southampton, pp 367–379.