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1、Shang jike of the injection molding process with stereolithography molds Abstract Functional parts are needed for design veri?cation testing, ?eld trials, customer evaluation, and production planning. By eliminating multiple steps, the creation of the injection mold directly by a rapid prototypin

2、g (RP) process holds the best promise of reducing the time and cost needed to mold low-volume quantities of parts. The potential of this integration of injection molding with RP has been demonstrated many times. What is missing is the fundamental understanding of how the modi?cations to the mold ma

3、terial and RP manufacturing process impact both the mold design and the injection molding process. In addition, numerical simulation techniques have now become helpful tools of mold designers and process engineers for traditional injection molding. But all current simulation packages for conventio

4、nal injection molding are no longer applicable to this new type of injection molds, mainly because the property of the mold material changes greatly. In this paper, an integrated approach to accomplish a numerical simulation of injection molding into rapid-prototyped molds is established and a cor

5、responding simulation system is developed. Comparisons with experimental results are employed for veri?cation, which show that the present scheme is well suited to handle RP fabricated stereolithography (SL) molds. Keywords Injection molding Numerical simulation Rapid prototyping 1 Introduct

6、ion In injection molding, the polymer melt at high temperature is injected into the mold under high pressure [1]. Thus, the mold material needs to have thermal and mechanical properties capable of withstanding the temperatures and pressures of the molding cycle. The focus of many studies has been

7、 to create the injection mold directly by a rapid prototyping (RP) process. By eliminating multiple steps, this method of tooling holds the best promise of reducing the time and cost needed to create low-volume quantities of parts in a production material. The potential of integrating injection mo

8、lding with RP technologies has been demonstrated many times. The properties of RP molds are very different from those of traditional metal molds. The key differences are the properties of thermal conductivity and elastic modulus (rigidity). For example, the polymers used in RP-fabricated stereolit

9、hography (SL) molds have a thermal conductivity that is less than one thousandth that of an aluminum tool. In using RP technologies to create molds, the entire mold design and injection-molding process parameters need to be modi?ed and optimized from traditional methodologies due to the completely d

10、ifferent tool material. However, there is still not a fundamental understanding of how the modi?cations to the mold tooling method and material impact both the mold design and the injection molding process parameters. One cannot obtain reasonable results by simply changing a few material propertie

11、s in current models. Also, using traditional approaches when making actual parts may be generating sub-optimal results. So there is a dire need to study the interaction between the rapid tooling (RT) process and material and injection molding, so as to establish the mold design criteria and techniq

12、ues for an RT-oriented injection molding process. In addition, computer simulation is an effective approach for predicting the quality of molded parts. Commercially available simulation packages of the traditional injection molding process have now become routine tools of the mold designer and pro

13、cess engineer [2]. Unfortunately, current simulation programs for conventional injection molding are no longer applicable to RP molds, because of the dramatically dissimilar tool material. For instance, in using the existing simulation software with aluminum and SL molds and comparing with experim

14、ental results, though the simulation values of part distortion are reasonable for the aluminum mold, results are unacceptable, with the error exceeding 50%. The distortion during injection molding is due to shrinkage and warpage of the plastic part, as well as the mold. For ordinarily molds, the mai

15、n factor is the shrinkage and warpage of the plastic part, which is modeled accurately in current simulations. But for RP molds, the distortion of the mold has potentially more in?uence, which have been neglected in current models. For instance, [3] used a simple three-step simulation process to co

16、nsider the mold distortion, which had too much deviation. In this paper, based on the above analysis, a new simulation system for RP molds is developed. The proposed system focuses on predicting part distortion, which is dominating defect in RP-molded parts. The developed simulation can be applie

17、d as an evaluation tool for RP mold design and process optimization. Our simulation system is veri?ed by an experimental example. Although many materials are available for use in RP technologies, we concentrate on using stereolithography (SL), the original RP technology, to create polymer molds.

18、The SL process uses photopolymer and laser energy to build a part layer by layer. Using SL takes advantage of both the commercial dominance of SL in the RP industry and the subsequent expertise base that has been developed for creating accurate, high-quality parts. Until recently, SL was primarily

19、 used to create physical models for visual inspection and form-?t studies with very limited functional applications. However, the newer generation stereolithographic photopolymers have improved dimensional, mechanical and thermal properties making it possible to use them for actual functional mold

20、s. 2 Integrated simulation of the molding process 2.1 Methodology In order to simulate the use of an SL mold in the injection molding process, an iterative method is proposed. Different software modules have been developed and used to accomplish this task. The main assumption is that temperat

21、ure and load boundary conditions cause signi?cant distortions in the SL mold. The simulation steps are as follows: 1 The part geometry is modeled as a solid model, which is translated to a ?le readable by the ?ow analysis package. 2 Simulate the mold-?lling process of the melt into a photopo

22、lymer mold, which will output the resulting temperature and pressure pro?les. 3 Structural analysis is then performed on the photopolymer mold model using the thermal and load boundary conditions obtained from the previous step, which calculates the distortion that the mold undergo during the in

23、jection process. 4 If the distortion of the mold converges, move to the next step. Otherwise, the distorted mold cavity is then modeled (changes in the dimensions of the cavity after distortion), and returns to the second step to simulate the melt injection into the distorted mold. 5 The shrin

24、kage and warpage simulation of the injection molded part is then applied, which calculates the ?nal distortions of the molded part. In above simulation ?ow, there are three basic simulation modules. 2. 2 Filling simulation of the melt 2.2.1 Mathematical modeling In order to simulate the us

25、e of an SL mold in the injection molding process, an iterative method is proposed. Different software modules have been developed and used to accomplish this task. The main assumption is that temperature and load boundary conditions cause significant distortions in the SL mold. The simulation steps

26、are as follows: 1. The part geometry is modeled as a solid model, which is translated to a file readable by the flow analysis package. 2. Simulate the mold-filling process of the melt into a photopolymer mold, which will output the resulting temperature and pressure profiles. 3. Structural analys

27、is is then performed on the photopolymer mold model using the thermal and load boundary conditions obtained from the previous step, which calculates the distortion that the mold undergo during the injection process. 4. If the distortion of the mold converges, move to the next step. Otherwise, the d

28、istorted mold cavity is then modeled (changes in the dimensions of the cavity after distortion), and returns to the second step to simulate the melt injection into the distorted mold. 5. The shrinkage and warpage simulation of the injection molded part is then applied, which calculates the final di

29、stortions of the molded part. In above simulation flow, there are three basic simulation modules. 2.2 Filling simulation of the melt 2.2.1 Mathematical modeling Computer simulation techniques have had success in predicting filling behavior in extremely complicated geometries. However, most of th

30、e current numerical implementation is based on a hybrid finite-element/finite-difference solution with the middleplane model. The application process of simulation packages based on this model is illustrated in Fig. 2-1. However, unlike the surface/solid model in mold-design CAD systems, the so-call

31、ed middle-plane (as shown in Fig. 2-1b) is an imaginary arbitrary planar geometry at the middle of the cavity in the gap-wise direction, which should bring about great inconvenience in applications. For example, surface models are commonly used in current RP systems (generally STL file format), so s

32、econdary modeling is unavoidable when using simulation packages because the models in the RP and simulation systems are different. Considering these defects, the surface model of the cavity is introduced as datum planes in the simulation, instead of the middle-plane. According to the previous inves

33、tigations [4–6], fillinggoverning equations for the flow and temperature field can be written as: where x, y are the planar coordinates in the middle-plane, and z is the gap-wise coordinate; u, v,w are the velocity components in the x, y, z directions; u, v are the average whole-gap thicknesses;

34、and η, ρ,CP (T), K(T) represent viscosity, density, specific heat and thermal conductivity of polymer melt, respectively. Fig.2-1 a–d. Schematic procedure of the simulation with middle-plane model. a The 3-D surface model b The middle-plane model c The meshed middle-plane model d The display of t

35、he simulation result In addition, boundary conditions in the gap-wise direction can be defined as: where TW is the constant wall temperature (shown in Fig. 2a). Combining Eqs. 1–4 with Eqs. 5–6, it follows that the distributions of the u, v, T, P at z coordinates should be symmetrical, with the

36、 mirror axis being z = 0, and consequently the u, v averaged in half-gap thickness is equal to that averaged in wholegap thickness. Based on this characteristic, we can divide the whole cavity into two equal parts in the gap-wise direction, as described by Part I and Part II in Fig. 2b. At the same

37、time, triangular finite elements are generated in the surface(s) of the cavity (at z = 0 in Fig. 2b), instead of the middle-plane (at z = 0 in Fig. 2a). Accordingly, finite-difference increments in the gapwise direction are employed only in the inside of the surface(s) (wall to middle/center-line),

38、which, in Fig. 2b, means from z = 0 to z = b. This is single-sided instead of two-sided with respect to the middle-plane (i.e. from the middle-line to two walls). In addition, the coordinate system is changed from Fig. 2a to Fig. 2b to alter the finite-element/finite-difference scheme, as shown in F

39、ig. 2b. With the above adjustment, governing equations are still Eqs. 1–4. However, the original boundary conditions in the gapwise direction are rewritten as: Meanwhile, additional boundary conditions must be employed at z = b in order to keep the flows at the juncture of the two parts at the sa

40、me section coordinate [7]: where subscripts I, II represent the parameters of Part I and Part II, respectively, and Cm-I and Cm-II indicate the moving free melt-fronts of the surfaces of the divided two parts in the filling stage. It should be noted that, unlike conditions Eqs. 7 and 8, ensuring

41、 conditions Eqs. 9 and 10 are upheld in numerical implementations becomes more difficult due to the following reasons: 1. The surfaces at the same section have been meshed respectively, which leads to a distinctive pattern of finite elements at the same section. Thus, an interpolation operation sho

42、uld be employed for u, v, T, P during the comparison between the two parts at the juncture. 2. Because the two parts have respective flow fields with respect to the nodes at point A and point C (as shown in Fig. 2b) at the same section, it is possible to have either both filled or one filled (and o

43、ne empty). These two cases should be handled separately, averaging the operation for the former, whereas assigning operation for the latter. 3. It follows that a small difference between the melt-fronts is permissible. That allowance can be implemented by time allowance control or preferable locati

44、on allowance control of the melt-front nodes. 4. The boundaries of the flow field expand by each melt-front advancement, so it is necessary to check the condition Eq. 10 after each change in the melt-front. 5. In view of above-mentioned analysis, the physical parameters at the nodes of the same se

45、ction should be compared and adjusted, so the information describing finite elements of the same section should be prepared before simulation, that is, the matching operation among the elements should be preformed. Fig. 2a,b. Illustrative of boundary conditions in the gap-wise direction a of the

46、middle-plane model b of the surface model 2.2.2 Numerical implementation Pressure field. In modeling viscosity η, which is a function of shear rate, temperature and pressure of melt, the shear-thinning behavior can be well represented by a cross-type model such as: where n corresponds to the po

47、wer-law index, and τ? characterizes the shear stress level of the transition region between the Newtonian and power-law asymptotic limits. In terms of an Arrhenius-type temperature sensitivity and exponential pressure dependence, η0(T, P) can be represented with reasonable accuracy as follows: E

48、quations 11 and 12 constitute a five-constant (n, τ?, B, Tb, β) representation for viscosity. The shear rate for viscosity calculation is obtained by: Based on the above, we can infer the following filling pressure equation from the governing Eqs. 1–4: where S is calculated by S = b0/(b?z)2 η

49、dz. Applying the Galerkin method, the pressure finite-element equation is deduced as: where l_ traverses all elements, including node N, and where I and j represent the local node number in element l_ corresponding to the node number N and N_ in the whole, respectively. The D(l_) ij is calculated

50、 as follows: where A(l_) represents triangular finite elements, and L(l_) i is the pressure trial function in finite elements. Temperature field. To determine the temperature profile across the gap, each triangular finite element at the surface is further divided into NZ layers for the finite-di

51、fference grid. The left item of the energy equation (Eq. 4) can be expressed as: where TN, j,t represents the temperature of the j layer of node N at time t. The heat conduction item is calculated by: where l traverses all elements, including node N, and i and j represent the local node numbe

52、r in element l corresponding to the node number N and N_ in the whole, respectively. The heat convection item is calculated by: For viscous heat, it follows that: Substituting Eqs. 17–20 into the energy equation (Eq. 4), the temperature equation becomes: 2.3 Structural analysis of the mold

53、 The purpose of structural analysis is to predict the deformation occurring in the photopolymer mold due to the thermal and mechanical loads of the filling process. This model is based on a three-dimensional thermoelastic boundary element method (BEM). The BEM is ideally suited for this application

54、 because only the deformation of the mold surfaces is of interest. Moreover, the BEM has an advantage over other techniques in that computing effort is not wasted on calculating deformation within the mold. The stresses resulting from the process loads are well within the elastic range of the mold

55、material. Therefore, the mold deformation model is based on a thermoelastic formulation. The thermal and mechanical properties of the mold are assumed to be isotropic and temperature independent. Although the process is cyclic, time-averaged values of temperature and heat flux are used for calculat

56、ing the mold deformation. Typically, transient temperature variations within a mold have been restricted to regions local to the cavity surface and the nozzle tip [8]. The transients decay sharply with distance from the cavity surface and generally little variation is observed beyond distances as sm

57、all as 2.5 mm. This suggests that the contribution from the transients to the deformation at the mold block interface is small, and therefore it is reasonable to neglect the transient effects. The steady state temperature field satisfies Laplace’s equation 2T = 0 and the time-averaged boundary condi

58、tions. The boundary conditions on the mold surfaces are described in detail by Tang et al. [9]. As for the mechanical boundary conditions, the cavity surface is subjected to the melt pressure, the surfaces of the mold connected to the worktable are fixed in space, and other external surfaces are ass

59、umed to be stress free. The derivation of the thermoelastic boundary integral formulation is well known [10]. It is given by: where uk, pk and T are the displacement, traction and temperature,α, ν represent the thermal expansion coefficient and Poisson’s ratio of the material, and r = |y?x|. clk

60、(x) is the surface coefficient which depends on the local geometry at x, the orientation of the coordinate frame and Poisson’s ratio for the domain [11]. The fundamental displacement ?ulk at a point y in the xk direction, in a three-dimensional infinite isotropic elastic domain, results from a unit

61、load concentrated at a point x acting in the xl direction and is of the form: where δlk is the Kronecker delta function and μ is the shear modulus of the mold material. The fundamental traction ?plk , measured at the point y on a surface with unit normal n, is: Discretizing the surface of the

62、 mold into a total of N elements transforms Eq. 22 to: where Γn refers to the nth surface element on the domain. Substituting the appropriate linear shape functions into Eq. 25, the linear boundary element formulation for the mold deformation model is obtained. The equation is applied at each no

63、de on the discretized mold surface, thus giving a system of 3N linear equations, where N is the total number of nodes. Each node has eight associated quantities: three components of displacement, three components of traction, a temperature and a heat flux. The steady state thermal model supplies tem

64、perature and flux values as known quantities for each node, and of the remaining six quantities, three must be specified. Moreover, the displacement values specified at a certain number of nodes must eliminate the possibility of a rigid-body motion or rigid-body rotation to ensure a non-singular sys

65、tem of equations. The resulting system of equations is assembled into a integrated matrix, which is solved with an iterative solver. 2.4 Shrinkage and warpage simulation of the molded part Internal stresses in injection-molded components are the principal cause of shrinkage and warpage. These resi

66、dual stresses are mainly frozen-in thermal stresses due to inhomogeneous cooling, when surface layers stiffen sooner than the core region, as in free quenching. Based on the assumption of the linear thermo-elastic and linear thermo-viscoelastic compressible behavior of the polymeric materials, shrinkage and warpage are obtained implicitly using displacement formulations, and the governing equations can be solved numerically using a finite element method. With the basic assumptions of injection