# 【精品文档】87中英文双语有关注塑模具设计相关的毕业设计外文文献翻译成品：立体光照成型的注塑模具工艺的综合模拟.doc

此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子一次的事！ 外文标题：Integrated simulation of the injection molding process with stereolithography molds 外文作者：Elijah Austin , Connor Sean 文献出处:The International Journal of Advanced Manufacturing Technology.2018(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做) 英文4365单词，23398字符(字符就是印刷符)，中文6278汉字。 Integrated simulation of the injection molding process with stereolithography molds Abstract Functional parts are needed for design veriﬁcation testing, ﬁeld trials, customer evaluation, and production planning. By eliminating multiple steps, the creation of the injection mold directly by a rapid prototyping (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 modiﬁcations to the mold material 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 But all current simulation packages for conventional injection molding are no longer applicable to this new type of injection molds, mainly because the property of the mold material changes greatly.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 corresponding simulation system is developed. Comparisons with experimental results are employed for veriﬁcation, which show that the present scheme is well suited to handle RP fabricated stereolithography (SL) molds. Keywords Injection molding Numerical simulation Rapid prototyping 1 Introduction 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 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 molding 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 stereolithography (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 modiﬁed and optimized from traditional methodologies due to the completely different tool material. However, there is still not a fundamental understanding of how the modiﬁcations 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 properties 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 techniques 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 process 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 experimental 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 main 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 inﬂuence, which have been neglected in current models. For instance, [3] used a simple three-step simulation process to consider 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 applied as an evaluation tool for RP mold design and process optimization. Our simulation system is veriﬁed 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. 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 used to create physical models for visual inspection and form-ﬁt 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 molds. 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 temperature and load boundary conditions cause signiﬁcant 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 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 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 injection 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 shrinkage and warpage simulation of the injection molded part is then applied, which calculates the ﬁnal distortions 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 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 temperature and load boundary conditions cause significant 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 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 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 injection 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 shrinkage and warpage simulation of the injection molded part is then applied, which calculates the final distortions 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 the 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-called 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 secondary 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 investigations [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; 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 the 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 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 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), 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 Fig. 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 same 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 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 should 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 one 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 location 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 section 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 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 power-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: Equations 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 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