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編號(hào)：
畢業(yè)設(shè)計(jì)(論文)外文翻譯
（原文）
學(xué) 院： 機(jī)電工程學(xué)院
專 業(yè)： 機(jī)械設(shè)計(jì)制造及其自動(dòng)化
學(xué)生姓名： 韋良華
學(xué) 號(hào)： 1000110129
指導(dǎo)教師單位： 機(jī)電工程學(xué)院
姓 名： 陳虎城
職 稱： 助教
2014年 5 月26 日
1. Introduction
Micro 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 data
analysis 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 mass
production 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 1mm
Type 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 mould
temperature, 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 interaction
between 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 a
type 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:
l The experiments become unwieldy in cost and
time when the number of variable is large;
l Two designs for the same experiment may yield
different results;
l The designs normally do not permit
determination of the contribution of each factor;
l The interpretation of experiment with a large
number 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, Parameter
design, 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 by
selecting 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 Data
The 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(D1) or 100 um( D1) where the features having the same depth, D1 or D2,were grouped on one 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), holding
pressure (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 Analysis
A 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. Results
The 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 Analysis
The 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 Results
The 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 Plot
A 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 Chart
A 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. When
your 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 C
and A are the only two factors that have passed the reference line, and factor C having a much larger effect
6.3. Main Effects Plot
The 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 is
depicted by a line with steeper slope compared to the effects contributed by less significant factors. To calculate main effects, Minitab procedure subtracts the
mean 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 from
level 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 Effects
The 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 on
whether 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 the
difference 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 Vi
at 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 and
interaction 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 of
PP-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 contributing
factors 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 the
significant 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 (Tb) and injection speed (Vi), for POM as barrel temperature (Tb), mould temperature (Tm), injection speed (Vi) 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 Ph, 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:
l PP-D1: Tb at 225 and Vi at 100;
l PP-D2: Vi at 100;
l POM-D1: Tb at 200, Tm at 60, Vi at 100 and W
l at 500;
l POM-D2: Same as for D1 except W;
l ABS-D1: Tb at 258, Vi at 100, W at 500 while
l Tm is fixed at 75;
l 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 and
the 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. Conclusions
In 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 Taguchi’s 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].
Acknowledgements
The authors would like to thank the EC FP7 FlexiTool project for supporting this work.
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