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Experiments and Computational Modeling of Metal Injection Molding for Forming Small Parts
Experiments and Computational Modeling of Metal Injection Molding for Forming Small Parts J.C. Gelin ( I ) , T. Barriere, M. Dutilly Laboratoire de Mecanique Appliquee - UMR CNRS, Universite de Franche-Conte, Besancon, France Received on January 10, 1999
Abstract The metal injection molding process is mainly used for manufacturing small metallic components with complex shapes. The process includes four stages and the present study is concentrated on the injection and the densification ones. A multiple cavities mold has been designed and realized that permits to characterize the effects of injection parameters on the final results in terms of shape and integrity. A mechanical model and a set of numerical simulations have been carried out that enhance the experimental results and that allow to access to the influence of the main process parameters. Finally it is shown that computational modeling could be used to help the process designer to produce accurate parts. Keywords: Powder injection molding, Finite element method, Simulation
1. INTRODUCTION
The metal injection molding (MIM) process allows the manufacturing of small and very complex metallic components. This process is used in several fields as automotive and aeronautics, data processing, electronics. The manufacturing of a component by MIM follows four stages: the mixture of an organic binder with a metal powder to produce the feedstock [l], the molding of the feedstock, the debinding to eliminate the primary binder and finally the densification stage by solid state sintering. The role of the organic binder is to facilitate the injection molding of the component. The molding parameters, such as the injection pressure, the mold temperature and the feedstock are of a primary importance to obtain a part free of defects. The debinding can be done by several methods: catalytic methods [2],immersion in a solvent [3],thermal debinding or by combination of these techniques. The densification is generally done by solid state sintering to obtain a density close to the theoretical density of powder material [4][5]. This high density is essential in order to have high ultimate material properties and a good corrosion resistance. In our paper the optimization of the component properties are carried out using SEM observations and Xray analyses to determine the microstructures and the temperature cycles well adapted [6], and also by using the numerical modeling of the MIM process by the finite element method. Other alternatives for the optimization of injection process parameters are possible through neural network optimizationtechniques [7]. The modeling of the MIM process requires to be able to know the final shape after injection and also the spatial powder volume fraction in order to be able to compute the final density and properties after sintering. In the approach developed (8][9], the fluid-particle flows has been considered using the multiphase flow approach. Other methods can be applied as the granular media mechanics [lo] that consist to calculate the motion of each particle in the fluid. In that case, powders size and distribution can be
Annals of the ClRP Vol. 48/1/1999
properly taken into account. But this method does not allow the modeling of complex industrial components. Other authors use the homogenization approach that consist to consider the binder-powder particles mixture as a fluid with an equivalent viscosity. In that case, the phase separation effects depending upon flowability of the mixture are neglected. In the multiphase flow approach [ l l ] , each phase is characterize by its own density, velocity field and volume fraction. The modeling of the interactions between both phases is done by a term of momentum exchange in the equations of motion of each phase. So the results of numerical simulations can predict both the evolution of the mold filling, the formation of dead zones and then the spatial distribution of the powder volume fraction after injection. 2. EXPERIMENTAL PROCEDURE 2.1 Mold design
In order to investigate the effects of process parameters, a multiple cavities mold has been designed and made in the laboratory. The mold cavities shown in Figure 1 are designed to obtain components as tensile (Types 2 and 3) and bending (Types 1 and 4) specimens with single or multiple input flows and also a component as a wheel. The wheel makes it possible to study the filling problem when the flow fronts weld. The choice of a segmented mold composed of hard metallic plates machined by EDM makes it is possible to easily change the shape or the thickness of the molded parts. Moreover the cylindrical runners (5 mm diameter) facilitates the filling of the mold, see Figure 2. The mold entering gates are rectangular ones with a section equal to 6 mm2, the location of the gates is important for obtaining an uniform filling without liquid or solid jetting [13][14][ 15][16]. To obtain the correct ejection of the parts, a clearance angle of 2" is necessary. Moreover in multiple cavities mold design the runners system must be designed in order to ensure that the mixture arrive at the same pressure and temperature for each parts.
179
Furthermore the segregation phenomena due to large shearing gradients, acceleration forces and to the difference between the densities of the binder and the powder can appear in the flow changes direction when the powder and the binder undergo different inertial and gravity forces.
I !
60
I
the components is given by the ratio weight over volume and the results in Table 2 show that the deviation is very small.
Figure 3: Photography of molding parts with 17-4PH powder
I
Figure 1: Des& and dimensions of the mold
Table 2: Weight evolution of molded parts (17-4PH) The SEM observations related in Figure 4 concern the distribution of the metallic particles and binder after injection molding. A spherical form characterizes the metallic particles with a diameter from 1.5 to 5 pm that allows a greater density during the sintering. Figure 2: A view of the open mold 2.2 Molding of the components
The molding is done in using an injection machine with a capacity equals 220 kN, an injection volume of 30.4 cm3 and a 22 mm diameter screw. The maximum melt pressure and injection speed d the machine correspond to 160 MPa and 160 mms-'. The feedstock (17-4PH steel with POM binder) is injected with an uniform injection speed during the filling stage, the packing stage being characterized by a constant hydraulic pressure. The mold temperature is maintained constant using a water circulation system with temperature controlled during filling, packing and cooling stages. A large number of defects occurring in the molding stage are due to a poor control of the cooling phase, This phase of the molding cycle has to compensate the shrinkage related to cooling, this one is largely influenced by the temperatures of the feedstock and the mold. The molding parameters are summarized in Table 1 and are in agreement with those given by the feedstock provider. rpararneters I Values 1 Injection time 1s Input flow 27.8 cm3s-' Injection specific pressure 1732 bars Injection temperature 1702185°C Mold temperature 140°C Packing pressure 40 bars 60 s Cooling time 362 g/lO mn (21.6 kg/l9Pc) Feedstock viscosity MFI
Figure 4: Observations of the component after injection molding by SEM The structural integrity of the molded parts was checked in using non destructive testing X-ray analyses making it possible to determine filling defects as inclusions and bubbles. The analyses realized on parts of each type show homogeneous distribution of the powder and binder. Small zones of internal porosity, characterized by a reduced density of powder, still reside on the welded joint of the bending test specimen, see Figure 5. From these observations one can conclude that the choice for injection molding parameters is adapted for this feedstock (Type 2).
~
Figure 5 Results from X-ray analysis of the components The injection of the components leads to parts with constant size and free of defects, Figure 3. The density of 180
2.3 Debinding stage The thermal debinding is made on the mold parts to
eliminate the primary binder. The binder composition and the thermal debinding employed imposes a very slow thermal processing (a ramp with 5"CIh) allowing a progressive departure of the organic components. Thermal processing with more important increasing speeds in temperature led to the appearance of significant defects, Table 3. Figure 6 shows a micrograph of the component microstructure after debinding that highlights the presence of a communicating regular porosity and absence of macroscopic defects, that should allow a suitable sintering of the parts.
3.1 Conservation laws
Mixture theory considers two interpenetrating continuum media and at each material point, the solid (s)and the liquid (f) phase are both present. Therefore the solid $s and the fluid
partial densities
as
ps = $tsps0
and
pf = 'pfpfo . Within domain f2 , consewation laws enable to write mass conservation and momentum conservation as:
3
7.2%
volume fraction are defined as field variables
related to
JP j
small cracks
Qf
+ vi. Vpi + pidivvi = 0
= mi + pibi + divo, Table 3: Influence of debinding speed on the defects
where the subscripts (f) or (s) stand for fluid (f) or solid (s) respectively and where mi are interaction forces which result from momentum exchange between both phases. In this paper, we consider the following form for the interaction equation: mf = -m, = k(v, where
- vf)
(3)
k = k($,, vs, vl)
is an interaction parameter.
When v, vanishes, powder is considered like an opened porous media and k tends to J ~ / K, a well-known result of
Figure 6: SEM after debinding 2.4 Sintering stage
The sintering is made in primary vacuum. The thermal sintering stage is the following: a first temperature increasing corresponding to 5"C/mn, a constant temperature of 600°C during 1 h to allow the remaining binder to be decompose, a second temperature increasing corresponding to S"C/mn, and finally a constant temperature of 1250°C during 1 h where the sintering rapidly increases. The observation of the sintered parts do not highlight the appearance of defects. A homothetic shrinkage of part dimensions is observed, see Figure 7. The density and the mechanical properties of the parts are in conformity with the expected values and the regular porosity, the absence of macroscopic defects have to be underlined.
.
.-,
I
I
Figure 7:Shape of the component (Type 2, Figure 1) after molding - debinding and sintering
3. MODELING AND FINITE ELEMENT SIMULATION The modeling and FE simulation are based on the biphasic flows theory that considers two interpenetratingcontinuum media at each material point, the solid and the liquid phase are both present (111. Since for high concentration, powder flows are dominated by particle collisions, the solid phase behaves like a non Newtonian viscous fluid.
flow through porous media [14]. We denote pf as unloaded fluid viscosity and K as the porous media permeability. When I$,vanishes, it results that the interaction forces are similar to the classical drag forces. As materials are considered as incompressible, and if the mixture is always saturated it results that 4s+t$l = 1 . By summing equations (1) for fluid and solid phases respectively and using the saturation relation, one can derive the following relation: div($,v, + Qtvf)= 0 (4) that can be interpretedas the global incompressibility of the mixture. 3.2 Finite element formulation The conservation equations are discretized using a Galerkin weighted residual method. A mixed velocitypressure formulation or reduced integration penalty method is used to treat the incompressibility constraint (4). The following equations set is obtained (in the case of the penalty method):
where Mi is the mass matrix of phase i (respectively f for fluid phase and s for solid phase), Kii
is the stiffness
matrix of phase, k is the interaction matrix and Kd" the penalty matrix for incompressibility Discretization of equation (5) leads to: + (Kadvec + Kdi,)cp
=
'I
is
constraint.
(6)
where M is the mass matrix for volume fraction equations, Kadvec is the stiffness matrix for advection phenomena 181
and Kdi,
is the stiffness matrix for diffusion phenomena.
An implicit backward scheme is used to discretize both systems [9]. 3.3 Injection molding simulation The simulation of injection molding with the developed model has been carried out using the geometry of the mold defined in Figure 1 and the injection and material parameters defined in Table 1. In Figure 8, interrupted molding experiments are compared with simulations results, The results indicate very good agreement in term of front position.
obtain accurate components free of macroscopic and microscopic defects depends directly on main injection, debinding and sintering parameters. The effects of these parameters has been studied through analysis at different levels including SEM and X-Ray analyses, as well as conventional measures. The effects of main process parameters has also been investigated by developing an original approach for the simulation of the processes. The results obtained prove that it is possible to obtain accurate results in terms of front positions, final shape and spatial distribution of powder volume fraction after injection. That result allows to help the process designer to produce accurate parts.
REFERENCES
[l] Hattwig, T., Veltl, G., Petzoldt, F., Scholl, R., Kieback, B., 1998, Powders for metal injection molding, J. of European Ceramic Society, 18,1211-1216. [2] Krueger, D.C., 1995, Powder injection molding catalytic debinding system, Pm2TEC, 6-16.
[3] Zu, Y.S., Lin, S.T., 1997, Optimizing the mechanical properties of injection molded W-4.9% Ni-2.1 %Fe in debinding, J. Mater. Processing Technology, 71, 337-342. [4] Piwill, I.E., Ediridinghe, M.J., Bevis, M.J., 1992, Development of temperature heating rate diagrams for the pyrolytic removal, J. Mater. Science, 27, 4381-4388. [5] Kim, K.T., Jeon, Y.C., 1998, Densification behavior of 31 6L stainless steel powder under high temperature, Mater. Science and Engineering, 242-250. Figure 8: Comparison between front positions for different mold filling stages Another important result from numerical simulations of the mixture flow is the powder volume fraction at the end of the molding stage. Figure 9 shows results obtained in simulation the injection of the wheel (Figure 1) considering the material defined in Table 1 and applying different values of the interaction coefficient defined in equation (3).The results clearly indicate that high values for the interaction coefficient lead to segregation effects in the internal part of the wheel whereas low values of the interaction coefficient lead to powder accumulation near the external boundaries of the wheel.
[6] Langer, E.M., Scharz, M., Wohlfromm, H., Blomacher, M., Weinand, D., Hanser. C., 1996, Metallographic investigations as part of the development of injection mold alloys and optimization of the metal injection moulding technique, Verlag, Munchen, PraM. Metallogr., 33, 222-234. [7] Choi, G.H., Lee, K.D., Chang, N., Kim, S.G., 1994, Optimisation of process parameters of injection molding with neural network application in a process simulation environment, Annals of the CIRP, 4311, 449-452. [8] Dutilly, M., 1998, PhD Thesis, Franche-ComteUniversity - France. [9] Dutilly, M., Ghouati, O., Gelin, J.C., 1998, Finite element analysis of the debinding and densification in the process of metal injection molding, J. Mater. Proc. Technology, 83, 170-175. [lo] Iwai, T., Aizawa, T., Kihara., J., 1994, Proc. of Powder Metallurgy 94, EPMA, 346-349. [ l l ] Bowen, R.M., Grot., R.A., Maugin, G.A., 1976, C. Eringen Ed., Academic Press, 831-834. [12] German, R.M, Bose, A., 1997, Injection molding of metals and ceramics, Princeton, New Jersey, USA.
[13] Picirillo, N., Lee, D., 1992, Jetting phenomenon in powder injection molding, The International J. of Powder Metallurgy, 30 /1, 221 -229. [14] Picirillo, N., and Lee, D., 1991, Jetting in powder injection molding, Advanced in Powder Metallurgy, 2, 119126. Figure 9: Effects of the interaction coefficient between binder and powder on the segregation 4. Conclusions The MIM process has been investigated both from experiments and from numerical modeling. The ability to
182
[15] Fox, R.T., Lee, D., 1991, Analysis of temperature effects during cooling in powder injection molding, The Int. J. of Powder Metallurgy, 30 / I , 221 -229. [16] Kang, M., Kim, S.G., 1990, CIM for mold factory automation, Annals of the CIRP, 3911, 467-470.
Abstract The metal injection molding process is mainly used for manufacturing small metallic components with complex shapes. The process includes four stages and the present study is concentrated on the injection and the densification ones. A multiple cavities mold has been designed and realized that permits to characterize the effects of injection parameters on the final results in terms of shape and integrity. A mechanical model and a set of numerical simulations have been carried out that enhance the experimental results and that allow to access to the influence of the main process parameters. Finally it is shown that computational modeling could be used to help the process designer to produce accurate parts. Keywords: Powder injection molding, Finite element method, Simulation
1. INTRODUCTION
The metal injection molding (MIM) process allows the manufacturing of small and very complex metallic components. This process is used in several fields as automotive and aeronautics, data processing, electronics. The manufacturing of a component by MIM follows four stages: the mixture of an organic binder with a metal powder to produce the feedstock [l], the molding of the feedstock, the debinding to eliminate the primary binder and finally the densification stage by solid state sintering. The role of the organic binder is to facilitate the injection molding of the component. The molding parameters, such as the injection pressure, the mold temperature and the feedstock are of a primary importance to obtain a part free of defects. The debinding can be done by several methods: catalytic methods [2],immersion in a solvent [3],thermal debinding or by combination of these techniques. The densification is generally done by solid state sintering to obtain a density close to the theoretical density of powder material [4][5]. This high density is essential in order to have high ultimate material properties and a good corrosion resistance. In our paper the optimization of the component properties are carried out using SEM observations and Xray analyses to determine the microstructures and the temperature cycles well adapted [6], and also by using the numerical modeling of the MIM process by the finite element method. Other alternatives for the optimization of injection process parameters are possible through neural network optimizationtechniques [7]. The modeling of the MIM process requires to be able to know the final shape after injection and also the spatial powder volume fraction in order to be able to compute the final density and properties after sintering. In the approach developed (8][9], the fluid-particle flows has been considered using the multiphase flow approach. Other methods can be applied as the granular media mechanics [lo] that consist to calculate the motion of each particle in the fluid. In that case, powders size and distribution can be
Annals of the ClRP Vol. 48/1/1999
properly taken into account. But this method does not allow the modeling of complex industrial components. Other authors use the homogenization approach that consist to consider the binder-powder particles mixture as a fluid with an equivalent viscosity. In that case, the phase separation effects depending upon flowability of the mixture are neglected. In the multiphase flow approach [ l l ] , each phase is characterize by its own density, velocity field and volume fraction. The modeling of the interactions between both phases is done by a term of momentum exchange in the equations of motion of each phase. So the results of numerical simulations can predict both the evolution of the mold filling, the formation of dead zones and then the spatial distribution of the powder volume fraction after injection. 2. EXPERIMENTAL PROCEDURE 2.1 Mold design
In order to investigate the effects of process parameters, a multiple cavities mold has been designed and made in the laboratory. The mold cavities shown in Figure 1 are designed to obtain components as tensile (Types 2 and 3) and bending (Types 1 and 4) specimens with single or multiple input flows and also a component as a wheel. The wheel makes it possible to study the filling problem when the flow fronts weld. The choice of a segmented mold composed of hard metallic plates machined by EDM makes it is possible to easily change the shape or the thickness of the molded parts. Moreover the cylindrical runners (5 mm diameter) facilitates the filling of the mold, see Figure 2. The mold entering gates are rectangular ones with a section equal to 6 mm2, the location of the gates is important for obtaining an uniform filling without liquid or solid jetting [13][14][ 15][16]. To obtain the correct ejection of the parts, a clearance angle of 2" is necessary. Moreover in multiple cavities mold design the runners system must be designed in order to ensure that the mixture arrive at the same pressure and temperature for each parts.
179
Furthermore the segregation phenomena due to large shearing gradients, acceleration forces and to the difference between the densities of the binder and the powder can appear in the flow changes direction when the powder and the binder undergo different inertial and gravity forces.
I !
60
I
the components is given by the ratio weight over volume and the results in Table 2 show that the deviation is very small.
Figure 3: Photography of molding parts with 17-4PH powder
I
Figure 1: Des& and dimensions of the mold
Table 2: Weight evolution of molded parts (17-4PH) The SEM observations related in Figure 4 concern the distribution of the metallic particles and binder after injection molding. A spherical form characterizes the metallic particles with a diameter from 1.5 to 5 pm that allows a greater density during the sintering. Figure 2: A view of the open mold 2.2 Molding of the components
The molding is done in using an injection machine with a capacity equals 220 kN, an injection volume of 30.4 cm3 and a 22 mm diameter screw. The maximum melt pressure and injection speed d the machine correspond to 160 MPa and 160 mms-'. The feedstock (17-4PH steel with POM binder) is injected with an uniform injection speed during the filling stage, the packing stage being characterized by a constant hydraulic pressure. The mold temperature is maintained constant using a water circulation system with temperature controlled during filling, packing and cooling stages. A large number of defects occurring in the molding stage are due to a poor control of the cooling phase, This phase of the molding cycle has to compensate the shrinkage related to cooling, this one is largely influenced by the temperatures of the feedstock and the mold. The molding parameters are summarized in Table 1 and are in agreement with those given by the feedstock provider. rpararneters I Values 1 Injection time 1s Input flow 27.8 cm3s-' Injection specific pressure 1732 bars Injection temperature 1702185°C Mold temperature 140°C Packing pressure 40 bars 60 s Cooling time 362 g/lO mn (21.6 kg/l9Pc) Feedstock viscosity MFI
Figure 4: Observations of the component after injection molding by SEM The structural integrity of the molded parts was checked in using non destructive testing X-ray analyses making it possible to determine filling defects as inclusions and bubbles. The analyses realized on parts of each type show homogeneous distribution of the powder and binder. Small zones of internal porosity, characterized by a reduced density of powder, still reside on the welded joint of the bending test specimen, see Figure 5. From these observations one can conclude that the choice for injection molding parameters is adapted for this feedstock (Type 2).
~
Figure 5 Results from X-ray analysis of the components The injection of the components leads to parts with constant size and free of defects, Figure 3. The density of 180
2.3 Debinding stage The thermal debinding is made on the mold parts to
eliminate the primary binder. The binder composition and the thermal debinding employed imposes a very slow thermal processing (a ramp with 5"CIh) allowing a progressive departure of the organic components. Thermal processing with more important increasing speeds in temperature led to the appearance of significant defects, Table 3. Figure 6 shows a micrograph of the component microstructure after debinding that highlights the presence of a communicating regular porosity and absence of macroscopic defects, that should allow a suitable sintering of the parts.
3.1 Conservation laws
Mixture theory considers two interpenetrating continuum media and at each material point, the solid (s)and the liquid (f) phase are both present. Therefore the solid $s and the fluid
partial densities
as
ps = $tsps0
and
pf = 'pfpfo . Within domain f2 , consewation laws enable to write mass conservation and momentum conservation as:
3
7.2%
volume fraction are defined as field variables
related to
JP j
small cracks
Qf
+ vi. Vpi + pidivvi = 0
= mi + pibi + divo, Table 3: Influence of debinding speed on the defects
where the subscripts (f) or (s) stand for fluid (f) or solid (s) respectively and where mi are interaction forces which result from momentum exchange between both phases. In this paper, we consider the following form for the interaction equation: mf = -m, = k(v, where
- vf)
(3)
k = k($,, vs, vl)
is an interaction parameter.
When v, vanishes, powder is considered like an opened porous media and k tends to J ~ / K, a well-known result of
Figure 6: SEM after debinding 2.4 Sintering stage
The sintering is made in primary vacuum. The thermal sintering stage is the following: a first temperature increasing corresponding to 5"C/mn, a constant temperature of 600°C during 1 h to allow the remaining binder to be decompose, a second temperature increasing corresponding to S"C/mn, and finally a constant temperature of 1250°C during 1 h where the sintering rapidly increases. The observation of the sintered parts do not highlight the appearance of defects. A homothetic shrinkage of part dimensions is observed, see Figure 7. The density and the mechanical properties of the parts are in conformity with the expected values and the regular porosity, the absence of macroscopic defects have to be underlined.
.
.-,
I
I
Figure 7:Shape of the component (Type 2, Figure 1) after molding - debinding and sintering
3. MODELING AND FINITE ELEMENT SIMULATION The modeling and FE simulation are based on the biphasic flows theory that considers two interpenetratingcontinuum media at each material point, the solid and the liquid phase are both present (111. Since for high concentration, powder flows are dominated by particle collisions, the solid phase behaves like a non Newtonian viscous fluid.
flow through porous media [14]. We denote pf as unloaded fluid viscosity and K as the porous media permeability. When I$,vanishes, it results that the interaction forces are similar to the classical drag forces. As materials are considered as incompressible, and if the mixture is always saturated it results that 4s+t$l = 1 . By summing equations (1) for fluid and solid phases respectively and using the saturation relation, one can derive the following relation: div($,v, + Qtvf)= 0 (4) that can be interpretedas the global incompressibility of the mixture. 3.2 Finite element formulation The conservation equations are discretized using a Galerkin weighted residual method. A mixed velocitypressure formulation or reduced integration penalty method is used to treat the incompressibility constraint (4). The following equations set is obtained (in the case of the penalty method):
where Mi is the mass matrix of phase i (respectively f for fluid phase and s for solid phase), Kii
is the stiffness
matrix of phase, k is the interaction matrix and Kd" the penalty matrix for incompressibility Discretization of equation (5) leads to: + (Kadvec + Kdi,)cp
=
'I
is
constraint.
(6)
where M is the mass matrix for volume fraction equations, Kadvec is the stiffness matrix for advection phenomena 181
and Kdi,
is the stiffness matrix for diffusion phenomena.
An implicit backward scheme is used to discretize both systems [9]. 3.3 Injection molding simulation The simulation of injection molding with the developed model has been carried out using the geometry of the mold defined in Figure 1 and the injection and material parameters defined in Table 1. In Figure 8, interrupted molding experiments are compared with simulations results, The results indicate very good agreement in term of front position.
obtain accurate components free of macroscopic and microscopic defects depends directly on main injection, debinding and sintering parameters. The effects of these parameters has been studied through analysis at different levels including SEM and X-Ray analyses, as well as conventional measures. The effects of main process parameters has also been investigated by developing an original approach for the simulation of the processes. The results obtained prove that it is possible to obtain accurate results in terms of front positions, final shape and spatial distribution of powder volume fraction after injection. That result allows to help the process designer to produce accurate parts.
REFERENCES
[l] Hattwig, T., Veltl, G., Petzoldt, F., Scholl, R., Kieback, B., 1998, Powders for metal injection molding, J. of European Ceramic Society, 18,1211-1216. [2] Krueger, D.C., 1995, Powder injection molding catalytic debinding system, Pm2TEC, 6-16.
[3] Zu, Y.S., Lin, S.T., 1997, Optimizing the mechanical properties of injection molded W-4.9% Ni-2.1 %Fe in debinding, J. Mater. Processing Technology, 71, 337-342. [4] Piwill, I.E., Ediridinghe, M.J., Bevis, M.J., 1992, Development of temperature heating rate diagrams for the pyrolytic removal, J. Mater. Science, 27, 4381-4388. [5] Kim, K.T., Jeon, Y.C., 1998, Densification behavior of 31 6L stainless steel powder under high temperature, Mater. Science and Engineering, 242-250. Figure 8: Comparison between front positions for different mold filling stages Another important result from numerical simulations of the mixture flow is the powder volume fraction at the end of the molding stage. Figure 9 shows results obtained in simulation the injection of the wheel (Figure 1) considering the material defined in Table 1 and applying different values of the interaction coefficient defined in equation (3).The results clearly indicate that high values for the interaction coefficient lead to segregation effects in the internal part of the wheel whereas low values of the interaction coefficient lead to powder accumulation near the external boundaries of the wheel.
[6] Langer, E.M., Scharz, M., Wohlfromm, H., Blomacher, M., Weinand, D., Hanser. C., 1996, Metallographic investigations as part of the development of injection mold alloys and optimization of the metal injection moulding technique, Verlag, Munchen, PraM. Metallogr., 33, 222-234. [7] Choi, G.H., Lee, K.D., Chang, N., Kim, S.G., 1994, Optimisation of process parameters of injection molding with neural network application in a process simulation environment, Annals of the CIRP, 4311, 449-452. [8] Dutilly, M., 1998, PhD Thesis, Franche-ComteUniversity - France. [9] Dutilly, M., Ghouati, O., Gelin, J.C., 1998, Finite element analysis of the debinding and densification in the process of metal injection molding, J. Mater. Proc. Technology, 83, 170-175. [lo] Iwai, T., Aizawa, T., Kihara., J., 1994, Proc. of Powder Metallurgy 94, EPMA, 346-349. [ l l ] Bowen, R.M., Grot., R.A., Maugin, G.A., 1976, C. Eringen Ed., Academic Press, 831-834. [12] German, R.M, Bose, A., 1997, Injection molding of metals and ceramics, Princeton, New Jersey, USA.
[13] Picirillo, N., Lee, D., 1992, Jetting phenomenon in powder injection molding, The International J. of Powder Metallurgy, 30 /1, 221 -229. [14] Picirillo, N., and Lee, D., 1991, Jetting in powder injection molding, Advanced in Powder Metallurgy, 2, 119126. Figure 9: Effects of the interaction coefficient between binder and powder on the segregation 4. Conclusions The MIM process has been investigated both from experiments and from numerical modeling. The ability to
182
[15] Fox, R.T., Lee, D., 1991, Analysis of temperature effects during cooling in powder injection molding, The Int. J. of Powder Metallurgy, 30 / I , 221 -229. [16] Kang, M., Kim, S.G., 1990, CIM for mold factory automation, Annals of the CIRP, 3911, 467-470.