英文翻译尽量减少生产成本的超薄注塑成型.docx

1、英文翻译尽量减少生产成本的超薄注塑成型Minimizing manufacturing costs for thin injection molded plastic components1. IntroductionIn most industrial applications, the manufacturing cost of a plastic part is mainly governed by the amount of material used in the molding process. Thus, current approaches for plastic part d

2、esign and manufacturing focus primarily on establishing the minimum part thickness to reduce material usage. The assumption is that designing the mold and molding processes to the minimum thickness requirement should lead to the minimum manufacturing cost.Nowadays, electronic products such as mobile

3、 phones and medical devices are becoming ever more complex and their sizes are continually being reduced. The demand for small and thin plastic components for miniaturization assembly has considerably increased in recent years.Other factors besides minimal material usage may also become important wh

4、en manufacturing thin plastic components. In particular, for thin parts, the injection molding pressure may become significant and has to be considered in the first phase of manufacturing.Employing current design approaches for plastic parts will fail to produce the true minimum manufacturing cost i

5、n these cases.Thus, tackling thin plastic parts requires a new approach, alongside existing mold design principles and molding techniques.1.1 Current researchToday, computer-aided simulation software is essential for the design of plastic parts and molds. Such software increases the efficiency of th

6、e design process by reducing the design cost and lead time 1. Major systems, such as Mold Flow and C-Flow, use finite element analysis to simulate the filling phenomena, including flow patterns and filling sequences. Thus, the molding conditions can be predicted and validated, so that early design m

7、odifications can be achieved. Although available software is capable of analyzing the flow conditions, and the stress and the temperature distribution conditions of the component under various molding scenarios, they do not yield design parameters with minimum manufacturing cost 2,3. The output data

8、 of the software only give parameter value ranges for reference and leaves the decision making to the component designer. Several attempts have also been made to optimize the parameters in feeding 47, cooling 2,8,9, and ejection These attempts were based on maximizing the flow ability of molten mate

9、rial during the molding process by using empirical relation ships between the product and mold design parameters. Some researchers have made efforts to improve plastic part quality by Reducing the sink mark 11 and the part deformation after molding 12, analyzing the effects of wall thickness and the

10、 flow length of the part 13, and analyzing the internal structure of the plastic part design and filling materials flows of the mold design 14. Reifschneider 15 has compared three types of mold filling simulation programs, including Part Adviser, Fusion, and Insight, with actual experimental testing

11、. All these approaches have established methods that can save a lot of time and cost. However, they just tackled the design parameters of the plastic part and mold individually during the design stage. In addition, they did not provide the design parameters with minimum manufacturing cost. Studies a

12、pplying various artificial intelligence methods and techniques have been found that mainly focus on optimization analysis of injection molding parameters 16,17. For in-stance He et al. 3 introduced a fuzzy- neuro approach for automatic resetting of molding process parameters. By contrast , Helps et

13、al. 18,19 adopted artificial neural networks to predict the setting of molding conditions and plastic part quality control in molding. Clearly, the development of comprehensive molding process models and computer-aided manufacturing provides a basis for realizing molding parameter optimization 3 , 1

14、6,17. Mok et al. 20 propose a hybrid neural network and genetic algorithm approach incorporating Case-Based Reasoning (CBR) to derive initial settings for molding parameters for parts with similar design features quickly and with acceptable accuracy. Moks approach was based on past product processin

15、g data, and was limited to designs that are similar to previous product data. However, no real R&D effort has been found that considers minimizing manufacturing costs for thin plastic components. Generally, the current practical approach for minimizing the manufacturing cost of plastic components is

16、 to minimize the thickness and the dimensions of the part at the product design stage, and then to calculate the costs of the mold design and molding process for the part accordingly, as shown in Fig. 1.The current approach may not be able to obtain the real minimum manufacturing cost when handling

17、thin plastic components.1.2Manufacturing requirements for a typical thin plastic component As a test example, the typical manufacturing requirements for a thin square plastic part with a center hole, as shown in Fig. 2, are given in Table 1.Fig.1. The current practical approachFig.2. Test example of

18、 a smallplastic component Table1. Customer requirements for the example component2. The current practical approachAs shown in Fig.1, the current approach consists of three phases: product design, mold design and molding process parameter setting. A main objective in the product design is to establis

19、h the physical dimensions of the part such as its thickness, width and length. The phases of molded sign and molding subsequently treat the established physical dimensions as given inputs to calculate the required details for mold making and molding operations.When applying the current practical app

20、roach for tackling the given example, the key variables are handled by the three phases as follows:Product design* Establish the minimum thickness (height) HP, and then calculate the material cost. HP is then treated as a predetermined input for the calculation of the costs of molddesign and molding

21、 operations. HP Mold design* Calculate the cooling time for the determined minimumthickness HP in order to obtain the number of mold cavities required. The mold making cost is then the sum of the costs to machine the: Depth of cutting (thickness) HPNumber of cavitiesRunner diameter DRGate thickness

22、HG Molding process* Determine the injection pressure Pin, and then the cost of power consumption Determine the cooling time t co, and then the cost of machine operations. The overall molding cost is the sum of the power consumption cost and machine operating cost.The total manufacturing cost is the

23、sum of the costs of plastic material, mold making and molding operations. Note that, in accordance with typical industry practice, all of the following calculations are in terms of unit costs.2.1 Product design This is the first manufacturing phase of the current practical approach. The design minim

24、izes the thickness HP of the plastic component to meet the creep loading deflection constraint , Y (1.47mmafter1yearofusage),and to minimize plastic material usage cost Cm. Minimizing HP requires 21:Figure 3 plots changes in HP through Eqs.1 and 2.The graphs show that the smallest thickness that mee

25、ts the 1.47mm maximum creep deflection constraint is 0 .75mm,with a plastic material cost of $0.000483558/unit and a batch size of 200000 units. This thickness will be treated as a given input for the subsequent molded sign and molding process analysis phases. 2.2Mold design2.2.1 Determination of co

26、oling timeThe desired mold temperature is 25 C. The determined thickness is 0.75mm. Figure 4 shows the cooling channels layout following standard industry practices. The cooling channel diameter is chosen to be 3mm for this example.From 22, the cooling time t co:And the location factor, BysolvingEqs

27、.3and4, and substituting HP =0.75mm and the given values of the cooling channel design parameters, the cooling time (3.1s) is obtained.The cycle time t cycle, given by E q. 5, is proportional to the molding machine operating costs, and consists of injection time (t in), ejection time (t e j), dry cy

28、cle time (t d c), and cooling time (t c o). 2.2.2 Determination of the number of mold cavities In general, the cost of mold making depends on the amount of machining work to form the required number of cores/cavities, runners, and gates. The given example calls for a two-plate mold Fig.3. Deflection

29、 and plastic materials costs versus part thickness Fig.4. Cooling channel layout that does not require undercut machining. Therefore, the ma chining work for cutting the runners and gates is proportional to the work involved in forming the cores/cavities and need not be considered. In the example, m

30、old making cost Cmm is governed by (n, HP).Generally, the minimum number of cavities, Nmin, is chosen to allow for delivery of the batch of plastic parts on time图3 。 After substitutionwhich is rounded To n =3,since the mold cannot contain 2.64 cavities. The machine operation capacity and the lead-ti

31、me of production in the example are given as 21.5h/d and 21d, respectively. Moreover, as mentioned in the previous section, the cycle time is directly proportional to the part thickness HP. A curve of batch size against thickness is plotted in Fig. 5. As shown, at HP =0.75mm, the production capabili

32、ty (batch size) is 242470units.Thus the production capability of n =3 is larger than the required lot size (200000units).For simplicity, the time taken for machining the depth of a thin component is treated as a given constant and added to the required time t CC for making a cavity insert. The C mm can then be calculated by n as expressed 12.3Molding process In the molding process, the cycle cost and power consumption cost are used to establish the molding operations cost as described in the following sect