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1. IntroductionMicro Injection Moulding (MIM) is a relatively new technology which is popular in the industry for micromanufacture because of its mass production capability and low component cost. In order to achieve the highest quality components with minimal costs using MIM it is important to understand the process and identify the effects of different independent parameters. One of the methods that can be employed to investigate the overall operation of MIM is Design of Experiments (DoE). In general, DoE can be used to collect data from any process and gain an understanding of the process through data analysis. This procedure can help to optimise the process and eventually lead to quality improvements.This paper is organized as follows. The MIM process is described in Section 2. In Section 3 the DoE is introduced. The collection of experimental data is explained in section 4 followed by results and dataanalysis in section 5. The discussion of results is presented in section 6. Finally the paper ends with conclusions given in section 7.2. Micro Injection Moulding (MIM)Micro-injection moulding 1 is a relatively new technology in the manufacturing world, and as such, it needs to be thoroughly investigated. According to Micro-powder injection moulding, conducted by Liu et.al. 2, micro-system technology were widely used in the new 21st century because of its successful applications in many different fields, e.g. in fluidic, medical, optical and telecommunications. Presented with massproduction capability and low component cost, make the MIM technology to be one of the key production processes for micro manufacturing. The Components of MIM fall into one of the following two categories:Type A: Overall size less than 1mmType B: Micro feature less than 200um.Initial work on DoE and data analysis on MIM, conducted by Sha et. al. 3, primarily focused on the analysis of 5 different factors (the melt and mouldtemperature, injection speed, pressure and flow status) affecting the achievable aspect ratios in three different polymer materials. The aspect ratio is the ratio of a longer dimension to its shorter dimension of a specially designed micro feature for this experiment. Their study concluded that Melt Temperature (Tb) and Injection Speed (Vi) were the key factors affecting the aspect ratios achievable in replicating micro features in all three polymers materials.The effect of tool surface quality in MIM, conducted by Griffiths et. al. 4, primarily focused on the factors affecting the flow behavior and also the interactionbetween the melt flow and the tool surface.The findings of these earlier investigations are taken into consideration in this study.Fig 1 shows a picture of a MIM machine. The planning of DoE and the data analysis was carried out using the statistical software package “Minitab 16”.3. Design of Experiments (DoE)The technique of defining and investigating all possible conditions in an experiment involving multiple factors is known as the Design of Experiments.The two types of DoE that are widely used are the Factorial design and Taguchi Method. According to Minitab design of experiment 6, Factorial design is atype of designed experiment that allows for the simultaneous study of the effects that several factors may have on a response. When performing an experiment, varying the levels of all factors simultaneously rather than one at a time, allows for the study of interactions between the factors.In a full factorial experiment, responses are measured at all combinations of the experimental factor levels. The combinations of factor levels represent the conditions at which responses will be measured. Each experimental condition is called “ run ” and the response measurement an observation. The entire set of runs is the “design”.To minimize time and cost, it is possible to exclude some of the factor level combinations. Factorial designs in which one or more level combinations are excluded are called fractional factorial designs.Fractional factorial designs are useful in factor screening because they reduce the number of runs to a manageable size. The runs that are performed are a selected subset or fraction of the full factorial design. But Roy 7 mentions that using full factorial and fractional factorial DoE may contribute to the following issues: The experiments become unwieldy in cost andtime when the number of variable is large; Two designs for the same experiment may yielddifferent results; The designs normally do not permitdetermination of the contribution of each factor; The interpretation of experiment with a largenumber of factors may be quite difficult.Hence, Taguchi method was developed in order to overcome some of these issues. Taguchi method is the technique of defining and investigating all possible conditions in an experiment involving multiple factors. Taguchi method was first introduced by Dr. Genichi Taguchi after the Second World War 8, 9. He came up with three basic concepts 7:1. Quality should be designed into the product and not inspected into it.2. Quality is best achieved by minimising the deviation from a target. The product should be so designed that it is immune to uncontrollable environmental factors.3. The cost of quality should be measured as a function of deviation from the standard and the losses should be measure system-wide.Dr. Taguchi setup a three stage process to achieve the enhancement of product quality by DoE based upon the concepts above, namely, System design, Parameterdesign, and Tolerance design.For the first stage, system design is to determine the suitable working levels of design factors. It includes design and test of a system based on selected materials,parts and nominal product/process parameters.Parameter design is for finding the factor level that can achieve the best performance of the product/process.The last stage which is the tolerance design is to decrease the tolerance of factors which is significantly affecting the product /process.A special set of arrays called Orthogonal Arrays (OAs) were constructed to lay out the experiment. The OA simplify the experiment design process. It is done byselecting the most suitable OA, assigning the factors to the appropriate columns, and describing the combinations of the individual experiments called the trial conditions.In this study a fractional factorial DoE was conducted in combination with Taguchi design concepts for quality enhancement.4. Collection of Experimental DataThe experiment was designed and set-up as defined by Sha, et. al. 10. This aim of this experiment is to analyse the effects of six factors on the achievable aspect ratios and find the most significant factors in order to reach the optimal settings which would give the highest aspect ratios. Fig. 2 shows the test part with micro features in the form of legs with two level of width (W),200 or 500 um ,and depth(D), 700( ) or 1100 um( ) where the features having the same depth, D1 or D2,were grouped on 1one side of the part.Three different materials, namely, semi-crystalline polymers such as polypropylene (PP), polyoxymethylene (POM) and an amorphous polymer such as acrylonitrilebutadiene-styrene (ABS) were in this study. The parameters investigated were barrel temperature (Tb), mould temperature (Tm), injection speed (Vi), holdingpressure (Ph), the existence of air evacuation (Va) and the width (W) of micro-legs.The aspect ratios, i.e. the ratios between the length of the micro feature and their depths, D1 or D2, are measured during the experiment. The average values of 24 measured responses with the same W and D (two per part) while applying the process setting given in Table 1 are used in this study.5. Results and Data AnalysisA 2-level six factors fractional factorial design (26-2) was applied in this experiment. The DoE was used to identify the factors that were active and significant to study the filling of micro channels. The purpose of this exercise is to look at the results of the DoE responses in order to understand the process and select the significant factors with their appropriate settings which are necessary for optimal performance.5.1. ResultsThe measured experimental responses for the DoE for the ratios between the length of the melt fills and the depth of the channels, D1 or D2 are recorded in Table 2. The value of D1 and D2 shown on the table are the average values of 24 measurements.5.2. Data AnalysisThe statistical software package “Minitab 16” was used to analyse the results obtained from the experiment.The result of the analysis for PP for both the cases of D1 and D2 is given in Table 3.In Table 3 the “Effect” column shows the positive or negative effect of the factor on the measured response. Hence the higher the effect the more significant the factor in consideration will be.The “effect” column determines the factors relative strength,the “p-values” determine which of the factors are statistically significant. In this study the values in the P column of the Estimated Effects and Coefficients table are used to determine which of the effects are significant. To make a decision concerning which factors are significant, further analysis is necessary and this will be discussed in the next section. A typical value for the significance was chosen to be 0.05 throughout this study.6. Discussion of ResultsThe above results were utilised to produce moreevidence to support the claims for strong factors whichmatter the most for the MIM process.Using = 0.05, for PP D1, the p-values found for Tb is 0.038 and Vi is 0.009 indicate that the main effects from these two single factors Tb and Vi are significant, i.e. their p-values are less than 0.05. These two single factors and their effects and other calculated values are highlighted in Table 3. In addition, the above results show that none of the two-way interactions are significant. This is clearly shown by the “Normal Plot of the Standardized Effects” (Fig3) and the “Pareto Chart of the Standardized Effects” (Fig 4).6.1. Normal Effects PlotA normal effects plot is used to compare the relative magnitude and the statistical significance of both main and interaction effects. As shown in Fig 3, Minitab draws a straight line to indicate where the points would be expected to fall if all effects were close to zero. Points that do not fall near the straight line usually signal factors with significant effects. Such effects are larger and generally go further away from the fitted straight line compared to the unimportant effects. By default, Minitab use a=0.05 and labels any effect that is significant. This is shown in Fig 3 by clearly marked labels for factors C and A. The factor C having a much greater weight on the MIM process for PP-D1 compared to factor A can also be seen on this graph.6.2. Pareto ChartA pareto chart of the effects is used to compare the relative magnitude and the statistical significance of both main and interaction effects. As shown in Fig 4, Minitab plots the factor effects in decreasing order of the absolute value of the effects. The reference line on the chart indicates which factor effects are significant. Whenyour model contains an error term, by default, Minitab use a=0.05 to draw the reference line.The results in Fig 3 confirm the results displayed in Fig 4 as factors Cand A are the only two factors that have passed the reference line, and factor C having a much larger effect6.3. Main Effects PlotThe main effects plot shows the basic effect of changing the significant factors. These one-factor effects are called main effects. In this plot bigger main effect isdepicted by a line with steeper slope compared to the effects contributed by less significant factors. To calculate main effects, Minitab procedure subtracts themean response at the low or first level of the factor from the mean response at the high or second level of the factor. It can be seen from Fig 5 that changing Vi fromlevel 1 to 2 has a bigger main effect than changing Tb. This is depicted by a line with steeper slope for Vi.6.4. Interaction EffectsThe next step in the analysis is to look at the significant interactions. The two-way interaction effects calculated in Table 3 can be visually displayed on the interaction plot to see how big these effects are. An interaction plot shows the impact of two suspected interacting factors that changing the settings of one factor has on another factor. Because an interaction can magnify or diminish main effects, i.e. depending onwhether the interaction is positive or negative, evaluating interactions is extremely important. While close to parallel lines indicate little or no interaction between the factors, intersecting lines signal an interaction. The amount of interaction is proportional to the angle of intersection, i.e. close to 90 portrays the strongest possible interaction.The interaction plot in Fig 6 shows that the response, i.e. the aspect ratio for Vi at 100 is higher than for Vi at 50 at both levels of Tb. However, it can be seen that thedifference in aspect ratio between runs using Vi at 100 and runs using Vi at 50 at Tb set to 225 is much greater than the difference in aspect ratio between runs using Viat 100 and runs using Vi at 50 at Tb set to 200. This suggests that to get the highest aspect ratio Tb should be set at 225 while Vi is kept at 100.Similar analysis was carried out for PP D2. Likewise, the experimental results were analysed for POM and ABS for D1 and D2. The significant single factors andinteraction factors for each of these different materials and the recommended settings for the selected significant factors are summarised in Table 4.This study shows that in most cases the aspect ratio is influenced by single factors except in POM-D2, ABS-D1 and ABS-D2 with a two-way interaction. In the case ofPP-D1, Tb and Vi and for PP-D2, Vi only. For POM-D1, Tb, Tm, Vi and W and for POM-D2, Tb, Tm, Vi, W and TbXVi. When ABS was used for D1 the contributingfactors were Tb, Vi, W and TmXPh; for D2 the significant factors were Vi, W and TmXPh. The entries shown in bold in Table 4 indicate the chosen settings for thesignificant factors. The shaded cells in Table 4 show two-way interaction between the factors.Using the process of elimination the critical factors for PP was identified as barrel temperature ( ) and injection speed ( ), for POM as barrel temperature ( ), mould temperature ( ), injection speed ( ) and width (W), and for ABS as barrel fixed at 75. Hence the factors holding pressure (Ph) and the existence of air evacuation (Va) can be ignored in the MIM process. This gives a full factorial of 4 trials for , 16 trials for POM and 8 trials for ABS. Further, as a result of this study, the optimal settings to achieve the highest aspects ratio for different materials used can be summarised as follows: PP-D1: Tb at 225 and Vi at 100; PP-D2: Vi at 100; POM-D1: Tb at 200, Tm at 60, Vi at 100 and W at 500; POM-D2: Same as for D1 except W; ABS-D1: Tb at 258, Vi at 100, W at 500 while Tm is fixed at 75; ABS-D2: Vi at 100, W at 500 while Tm is fixed at 75. Confirmatory trials were conducted to verify the optimal performance for the above settings which have been identified theoretically and repeated 24 times andthe average measured responses gave the best aspect ratios to be found so far. They are as follows: for PP and POM the best aspect ratio of 20 and for ABS it was 21.7. ConclusionsIn this paper an analytical method for understanding the MIM process and optimising the process parameters using DoE has been presented. A fractional factorial experiment with Taguchis quanlity concepts has been conducted in order to save time and effort in performing the trials. The data collected in the form of measured responses has been successfully analysed to identify the significant single factors as well as two-way interactions. Further, the optimal process parameter setting identified through DoE method for different materials used in the study have been validated by running confirmatory trials and the measured responses verified the theoretical results by achieving high aspect ratios for the optimal settings found for the MIM process parameters. The knowledge of MIM gained through this study will help understand and optimise Nano Injection Moulding (NIM) process 11.AcknowledgementsThe authors would like to thank the EC FP7 FlexiTool project for supporting this work.References1 Trotta, G., Surace, R., Modica, F., Spina, R., Fassi, I., 2011. Micro Injection Moulding of Polymeric Components,” AIP Conf. Proc. 2011; 1315:1273-8.2 Liu, ZY, Loh, NH, Tor, SB, Khor, KA, Murakoshi, Y., Maeda, R., Shimizu, T., 2002. Micro-powder injection molding. J Material Processing Technology 2002; 127(2), p. 165.3 Sha, B., Dimov, S., Griffiths, C., Packianather, MS, 2007. Investigation of micro-injection moulding: Factors affecting the replication quality. J Materials Processing Technology 2007; 183, p. 284.4 Griffiths, CA, Dimov, SS, Brousseau, EB, Hoyle, RT, 2007. The effects of tool surface quality in micro-injection moulding. J Materials Processing Technology 2007; 189(1):, p. 418.5 Griffiths, CA, Dimov, SS, Brousseau, EB, Chouquet, C., Gavillet, J., Bigot, S., 2010. Investigation of surface treatment effects in micro-injection-moulding. Int J Advanced Manufacturing Technology 2010; 47(1):, p. 99.6 Minitab Handbook. 5th ed. Canada: Curt Hinrichs; 2005.7 Roy, R., 1990. A Primer on the Taguchi Method. USA: VanNostrand Reinhold; 1990.8 Sudhakar, PR., 1995. An Introduction to Quality Improvement Through Taguchi Methods. Quality 1995, p. 54.9 Taguchi, G., 1996. The Role of D.O.E. For Robust Engineering:A Commentry. Int J Quality and Reliability Eng 1996; 12:, p. 73.10 Sha, B., Dimov, S., Griffiths, C., Packianather, MS, 2007. Microinjection moulding: Factors affecting the achievable aspect ratios. Int J Adv Manuf Technoly 2007; 33, p. 147.11 Zhang, N., Cormac, J., Byrne, CJ, Browne, DJ, Gilchrist, MD, 2012. Towards nano-injection molding. Materials Today 2012; 15(5), p. 216.通过实验设计优化微注射成型工艺M. Packianathera*, F. Cha , C. Griffith , S. Dimo , D.T. Phan aIMME 英国卡迪夫 CF243AA 游行皇后大厦卡迪夫大工程学院英国伯明翰 B152TT 伯明翰大学机械工程学院*通讯作者联系电话：+44-29-20875911;传真：+44-29-20874695；电子邮件地址：packianathermscf.ac.uk摘要本文提出通过试验设计（DOE）优化微注射成型（MIM）过程。MIM 是一种相对较新的用于微部件的快速制造的技术。由于改变工艺参数，为了满足质量和可靠性的限制，减少操作过程中变异的是非常重要。在这项研究中，对 MIM工艺的理解，它是通过 DOE 的六个影响表面质量的参数，流动长度和长宽比来优化的。显著单一的工艺参数以及它们之间的相互作用是通过统计分析确定。为 2 级的试验中，20：21：20 的纵横比，分别对应聚丙烯（PP）丙烯腈 - 丁二烯 - 苯乙烯（ABS）和聚甲醛（POM）实现关键词：微注射成型（MIM） ，试验设计（DOE） ，全因子，部分因子，优化设计的设计第一章 引言因为它的大批量生产能力和低元件成本，微注射成型（MIM）是一种在微型制造行业内流行的相对较新的技术。为了使 MIM 以最小的成本实现最高品质的元件，理解的过程并确定不同的独立参数的影响是很重要的。一种可以采用的调查 MIM 的整体操作的方法是试验设计（ DOE）的设计。在一般情况下，DOE（ DoE）可用于收集从每个过程，并通过数据分析获得加工工艺的理解。这个程序可以帮助优化过程，并最终使得质量的提高。本文的结构如下，在 MIM 工艺在第 2 节所述，在第 3 节 DOE 的介绍，实验数据的收集之后第 4 节解释，结果和数据分析进行说明在第 5 节说明。结果的讨论，在第 6 节提出，最后在第 7 节给出结论的文件结束。2212-82712013 的作者。由 Elsevier BV 公司负责出版，罗伯托特提教授同行评议 DOI：10.1016/j.procir.2013.09.052第二章 微注射成型（MIM）微注射成型1是在制造世界一个相对较新的技术，因此，它需要被深入研究调查。据 Liu 等人2进行微粉末注射成型，因为它在许多不同的领域，例如医学，光学和电信，成功的应用，使得微系统技术被广泛使用在新的 21 世纪，。带有大批量生产能力和低元件成本，使得 MIM 技术是进行微制造中的一个关键生产工序。MIM 的组件分为以下两个类别之一：A 型：外形尺寸小于 1mm ；B 型：微特征小于 200 。由 Sha 等人3在美国 DOE 进行初步工作和 MIM 的数据分析，主要集中在 5个不同的受三个不同的聚合物材料可达到的高宽比影响的因素（熔体和模具温度，注射速度，压力和流动状态）的分析。本实验纵横比是一个特殊设计的微特征，其为较长尺寸与较短尺寸的的比率。他们的研究结论是，熔体温度（TB）和注射速度（六）是受在复制所有三种聚合物材料的微观特性中可达到的长宽比的影响的关键因素。由 Griffiths 等人4进行的 MIM 工具的表面质量效果主要集中于影响熔体流动和模具表面之间的流动行为，并相互作用的因素。这些早期的调查结果都考虑到了这项研究。图 1 示出了 MIM 型机的画面。DOE 的规划和数据分析使用的统计软件包“Minitab 16”进行。图 1 微型注塑机5第三章 设计实验（DOE）在实验中定义和调查所有可能的条件涉及多重因素的技术被称为实验的设计。这两种 DOE 类型被广泛采用是析因设计与田口方法。根据实验 Minitab 的设计6 ，析因设计是一种设计的实验，允许同时影响研究，一些因素可能对产生同一个影响结果。当进行实验，不同的所有因素的水平同步，而不是一次一个，允许相互作用的因子的研究。在全面析因实验，响应于实验因子水平的所有组合计算。因子水平的组合代表了在响应将被测量的条件。每个实验条件称为运行和响应测量观察。整组运行的是“设计” 。为了最大限度地减少时间和成本，因此能够排除一些因子水平的组合
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