Process Study on Compression Moulding of SMC using ...1017911/FULLTEXT01.pdf · MASTER’S THESIS 2008:052 CIV Jimmy Olsson Process Study on Compression Moulding of SMC using Factorial

MASTER’S THESIS

2008:052 CIV

Jimmy Olsson

Process Study on Compression Moulding of SMC

using Factorial Design

MASTER OF SCIENCE PROGRAMME Engineering Physics

Luleå University of Technology Department of Applied Physics and Mechanical Engineering

Division of Fluid Mechanics

2008:052 CIV • ISSN: 1402 - 1617 • ISRN: LTU - EX - - 08/052 - - SE

Preface

The work presented in this report has been carried out at SICOMP andin a collaboration with the department of applied physics and mechanicalengineering at Lulea University of Technology.

First, I would like to thank my supervisors, Staffan Lundstrom at Luleauniversity of technology, Kurt Olofsson at SICOMP, and Joakim Pettersonat ABB Plast. For giving me the opportunity to do this work, and also forgiving me support when problem have occurred.

I would specially like to thank Ake Westerlund at ABB Plast for all hisexpertise help, with both theoretical and practical problems.

I would also like to thank Kerstin Vannman at Lulea University of tech-nology for a brief explanation about the basics in factorial design.

Finally, I would like to thank everybody at SICOMP who have helped mewith all kind of problems and also for making my time in Pitea nice.

Jimmy OlssonPitea, 2008

I

Abstract

During compression moulding of sheet moulding compounds, voids are formedthat can deteriorate the surface finish of the final product as well as its prop-erties such as the electrical insulation. A large number of processing andmaterial parameters can however be tuned in order to reduce the amountof voids in the final product. There is no profound investigation couplingsuch parameters to the void content. Hence, factorial design is here used toplan an experimental series where material as well as processing conditionsare varied. In particular vacuum assisted compression moulding is used in acircular shaped mould. The experimental series is evaluated by counting thenumber of surface voids on the plates and by measurement of their electricalinsulation. One result is that the latter can be considerably improved bychoosing optimal processing conditions.

II

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Aim and Objective . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Previous work . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Description of project . . . . . . . . . . . . . . . . . . . . . . 21.5 Outline of this thesis . . . . . . . . . . . . . . . . . . . . . . . 2

2 Theory 52.1 Sheet Molding Compound, SMC . . . . . . . . . . . . . . . . 5

2.1.1 Raw materials . . . . . . . . . . . . . . . . . . . . . . 82.2 Statistical Design . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.1 The 2k Factorial Design . . . . . . . . . . . . . . . . . 102.2.2 Analysis with MODDE . . . . . . . . . . . . . . . . . 11

2.3 DETECT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.1 Speckle Pattern . . . . . . . . . . . . . . . . . . . . . . 122.3.2 Correlation Analysis . . . . . . . . . . . . . . . . . . . 13

2.4 High Voltage Insulation Test . . . . . . . . . . . . . . . . . . 13

3 Method 153.1 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.1.1 Preparing the samples . . . . . . . . . . . . . . . . . . 153.1.2 Displacement of charges . . . . . . . . . . . . . . . . . 153.1.3 Vacuum Tool . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 Measurements to quantify the quality . . . . . . . . . . . . . 183.2.1 Surface defects with DETECT . . . . . . . . . . . . . 183.2.2 Surface defects with painting method . . . . . . . . . 213.2.3 Image Analysis of the Weld line . . . . . . . . . . . . . 223.2.4 Electrical Insulation . . . . . . . . . . . . . . . . . . . 233.2.5 Microscopic Analysis of Voids . . . . . . . . . . . . . . 23

3.3 Analysis with MODDE . . . . . . . . . . . . . . . . . . . . . . 243.4 Vacuum Treatment of Prepreg . . . . . . . . . . . . . . . . . 25

4 Results 294.1 Effect of air exposion on prepreg . . . . . . . . . . . . . . . . 294.2 Experimental run 1, Vacuum Assisted Moulding. . . . . . . . 29

4.2.1 Factorial design . . . . . . . . . . . . . . . . . . . . . . 294.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 314.2.3 Comparison of DETECT and Paint . . . . . . . . . . 42

4.3 Experimental run 2 . . . . . . . . . . . . . . . . . . . . . . . . 434.3.1 Factorial design . . . . . . . . . . . . . . . . . . . . . . 43

III

CONTENTS

4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 444.3.3 Comparison of DETECT and Paint . . . . . . . . . . 45

4.4 Experimental run 3 . . . . . . . . . . . . . . . . . . . . . . . . 464.4.1 Factorial design . . . . . . . . . . . . . . . . . . . . . . 464.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 464.4.3 Comparison of DETECT and Paint . . . . . . . . . . 49

4.5 Experimental run 4 . . . . . . . . . . . . . . . . . . . . . . . . 504.5.1 Factorial design . . . . . . . . . . . . . . . . . . . . . . 504.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 504.5.3 Comparison of DETECT and Paint . . . . . . . . . . 52

4.6 The effect of aged prepreg . . . . . . . . . . . . . . . . . . . . 534.6.1 Factorial Design . . . . . . . . . . . . . . . . . . . . . 534.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 544.6.3 Comparison of DETECT and Paint . . . . . . . . . . 56

4.7 The effect of changing prepreg . . . . . . . . . . . . . . . . . 574.7.1 Factorial Design . . . . . . . . . . . . . . . . . . . . . 574.7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 584.7.3 Comparison of DETECT and Paint . . . . . . . . . . 61

4.8 Microscopical analysis of void contents . . . . . . . . . . . . . 624.9 Vacuum treatment for prepreg . . . . . . . . . . . . . . . . . 69

5 Conclusions and Future Work 71

A Appendix 75A.1 Raw data experimental run 1 . . . . . . . . . . . . . . . . . . 75A.2 Raw data experimental run 2 . . . . . . . . . . . . . . . . . . 76A.3 Raw data experimental run 3 . . . . . . . . . . . . . . . . . . 77A.4 Raw data experimental run 4 . . . . . . . . . . . . . . . . . . 78A.5 CAD drawing of Vacuum tool . . . . . . . . . . . . . . . . . . 78

IV

1Introduction

1.1 Background

Due to mechanical properties and costs, the use of fibre reinforced polymercomposites have increased during the last decades. There exist several meth-ods for manufacturing these materials. One method with potential for largescale productions of lightweight vehicle components is called sheet mouldingcompound (SMC). One example of SMC-product in the vehicle industry canbe seen in figure 1.1. The main reason why it has not come into wider usein the vehicle industry is the unsatisfactory condition of the surface finish.Many improvements of the technique have been made since the beginningin the 1950s; however, some further improvements need to be done.

Figure 1.1: Lightweight vehicle component of SMC.

1.2 Aim and Objective

The main ambition of this project is to establish the factors that have thehighest influence on the quality of the compression moulded SMC product,

1

CHAPTER 1. INTRODUCTION

both as independent factors and in combination with other factors, by usingfactorial design methodology. Secondly, use the recently developed measure-ment tool DETECT, which also is a part of the FYS (Forbattrade Ytor iSMC-produkter) project, to quantify the condition of the surfaces. Further-more, log the data from the experiments and send them to LTU in orderfor them to use the data to optimize a CFD-model (Computational FluidDynamics)for SMC.

1.3 Previous work

Over the years, many have tried to define processing conditions to create”class A” surfaces for SMC. It is commonly known that vacuum assistedmoulding (VAM) is the superior method to create ”class A” surfaces [1] [2][3], but no one of the reports that was found in the literature search had anyexperimental data which supported their thesis. ”Class A” surface is notstrictly defined and depends on several parameters like surface roughness,defects, optical phenomena, etc. The most used instrument for detectingdefects in the industry is probably the human eye. Several methods havebeen developed to provide more quantitative data. The DETECT system,which is a result of previous work carried out at SICOMP, will be used toquantify the quality of the surfaces in this project. Furthermore, methodand result from previous experimental work carried out at SICOMP, byGreger Nilsson, in order to decrease void amount in prepreg by means ofvacuum assisted prepreg manufacturing, will be presented in this report.

1.4 Description of project

In this project the SMC process will be investigated by using factorial designmethodology. For analysis and setup of experimental plan, the softwareMODDE 7.0 will be used. Among the altered factors, the vacuum levelinside the moulding tool is probably the most interesting. To quantify thequality of the samples, different techniques will be utilized. The surfaces willbe studied with DETECT, image analysis, and simulation of the paintingprocess by using a paint that is not suitable for SMC materials. Hence,defects that should have been hidden with a good coating will in this casecreate a blowout that is easy to detect with the human eye. An electricalinsulting test, developed by ABB Plast, will be used to quantify the materialquality of the samples.

1.5 Outline of this thesis

First, a theoretical part will be given. Which includes the SMC-process,factorial design, the DETECT tool, and the insulation test. The methods,

2

1.5. OUTLINE OF THIS THESIS

including previous experimental studies with vacuum assisted prepreg man-ufacturing, are then presented in the second part. Thirdly, a chapter withexperiment descriptions and results, including previous accomplished resultswith vacuum assisted prepreg manufacturing. Finally, it follows a chapterfor discussion and conclusions for the entire project.

3

2Theory

2.1 Sheet Molding Compound, SMC

SMC is a compression moulding process where the material used is SMCprepreg, which contains fibres, resin and additives. The basic idea behindSMC is simple, but the whole process from raw materials to a finished prod-uct is very complex. First, the prepreg can be created in numerous ways byvarying the raw material setup. Hence, different properties can be achievedin the created SMC product. One example is fire retardant products. Sec-ondly, the properties of the prepreg are changed in the handling processbetween mixing and moulding. If the viscosity is to low the material willflow faster. To high viscosity have the opposite effect, the prepreg will resistflow. Both of the above mentioned scenarios can cause quality deteriora-tions. To obtain the desired viscosity, prepregs has to be stored for sometime, and then used in a time period where the viscosity level is similar. It isalso commonly known that the prepreg dries out if it is in contact with air.Thirdly, by altering the parameters in the compression moulding processthe manufactured products are affected. However, a brief explenation of thewhole process and the usual raw materials will be given here. The basicprinciple of a prepreg machine is visualised in figure 2.1. In addition to thesketch, images of a real prepreg machine located at ABB Plast in Pitea, canbe seen in figure 2.2.

Figure 2.1: Basic principle for prepreg machines.[4]

5

CHAPTER 2. THEORY

(a) Overview.

(b) Compactor. (c) Chopping and spreading of fi-bres.

Figure 2.2: The SMC prepreg machine at ABB Plast.

The prepreg sheets are then stored after leaving the compactor rolls. Whenthe prepreg is matured, it is cut into desired shaped pieces. The pieces arethen piled together without the carrier film, into what is called a charge. Itis of great importance that the charge has the right shape and weight, sothat complete and effective mouldfilling is achieved. Usually, the intentionof with prepreg placement is in the moulding tool is to allow flow over ap-proximately 30-40% of the surface. A flow that exceeds 40% of the surfaceusually increase the amount of surface voids [2]. The temperature insidethe mould is in the range 120◦C - 180◦C for unsaturated polyester based

6

2.1. SHEET MOLDING COMPOUND, SMC

SMC-material[5]. After placing the charge in the tool it is closed with hy-draulic pressure, as fast as possible to force the charge to fill the mould. Thepressure is then held between 3-20 MPa for 1-4 minutes until the mouldingis complete [5]. Closing time is depending on thickness, material properties,pressure and heat. A schematic sketch of the SMC moulding process can beseen in figure 2.3.

Figure 2.3: Compression molding of SMC [5].

However, this method is not perfect since the surface of the painted SMCproducts have small defects. These defects are generally caused by surfacevoids in the SMC, which have evolved into more visible defects in the paint-ing process. So called pinholes and blowouts are among the more commontypes. In figure 2.4, these typical defects are visualized.

(a) Pinhole at 200X. (b) Blowout at 150X.

Figure 2.4: Typical deffects magnified by scanning electron microscopy(SEM) [6].

7

CHAPTER 2. THEORY

Blowouts are generally larger in size than pinholes, but harder to detect byvisual inspection since the determining factor for formation of pinholes orblowouts is the size of the openings of the voids. A small opening leadsto blowouts while larger openings (typically 50µm) lead to pinholes. Animportant factor which contributes to formation of blowouts is coverage ofthe surface voids by a thin resin-rich layer of SMC-material [6].

2.1.1 Raw materials

SMC prepreg is based on resin together with curing agent, filler, thickenerand lubricant. By mixing these in different combinations, different proper-ties are achieved of the material.

Resins

• PolyesterOf the resins, polyester is the most widely used since it can be appliedfor many applications due to its excellent physical properties and re-sistance to booth chemical media and weather. Moreover, polyestercan also be pigmented, filled and fibre-reinforced when the polyesterstill is in liquid form.

• Vinyl esterThe molecular structure of vinyl ester is similar the one of polyester.Vinyl ester is also generally tougher and more resilient than polyester.This is because the reactive sites are positioned at the ends of themolecular chains. Due to this structure, the whole length of the molec-ular chain can absorb load. Another important property of vinylesteris that it contains fewer ester groups. Therefore vinyl ester is moreresistant against water and other chemicals.

• EpoxyEpoxy resins are used for high performance products. Generally theyhave outstanding properties compared to polyester and vinyl esterresins. Epoxy resins are similar to vinyl ester resins since they bothhave reactive sites at the ends of the molecular chain. The differenceis that epoxy resins have epoxy groups positioned there. Epoxy resinsalso benefit from two ring groups located at the centre of the chain.These ring groups are superior to ordinary linear groups in absorbingboth mechanical and thermal stresses. The reason why epoxy resinsare not more widely used in the industry is economical. Material ex-penses are larger as well as the cure cycles are longer.

8

2.2. STATISTICAL DESIGN

Fibers

The commonly used fibre material is glass. Due to low cost, the disad-vantages can be neglected. Those are low tensile modulus, relatively highspecific gravity, sensitivity to abrasion and high hardness that causes exces-sive wear on manufacturing tools. However, glass also has advantages apartfrom costs; high tensile strength; high chemical resistance and excellent in-sulating properties [2].

Fillers

To minimise the cost, fillers are added to the prepreg mixture. But it is alsoused to decrease shrinkage, and serve as a binder to improve surface quality.Calcium Carbonate (CaCO3) is the most widely used filler[1].

Additives

• Catalyst and acceleratorCatalysts are added to the resin in order to initiate the polymerizationreaction without taking part in the chemical reaction. Accelerators areadded to the resin to allow the reaction at lower temperatures and atgreater speed.

• Thickening agentThickening agents such as oxides of magnesium, or hydroxides of mag-nesium, and calcium can be used to increase the viscosity.

• Thermoplastic additivesBy adding thermoplastic additives to polyester and vinylester resins,the SMC polymerization shrinkage can be reduced. It is possible toobtain zero or even a positive shrinkage after moulding. [2]

• Release agentIn order to aid the release of the SMC from the moulding tool, a releaseagent can be added. A release agent have low melting point, thereforeit moves to the surface of the SMC in the mould. As a consequence,a thin oily film is created. Calcium (Ca) Zink (Zn) stearate are usedas release agents. [2]

2.2 Statistical Design

Statistical techniques are widely used in research areas where the processinvestigated is not fully understood, and thereby has to be studied withexperiments. This can be described as in figure 2.5. First, factors arealtered and then put in a box of unknown contents, Secondly, the responseis analysed in order to obtain knowledge about the contents in the box. By

9

CHAPTER 2. THEORY

Figure 2.5: Simple illustration of the idea with statistical design where theprocess in not fully understood.

using statistical techniques while experimenting, the work can be done withhigher efficiency. Hence, both time and money can be saved. The conceptsof statistical experimental techniques have been developed since the 1920sand early 1930s, when Sir Ronald A. Fisher stated the basic principles ofexperimental design in his pioneering work [7]. The general route while doingan investigation of this type is first to decide which factors that possibly canhave an effect on the result, often called response while working with factorialdesign. Secondly, perform the initial experiments, which is called screeningtests, where the objective is to identify factors that have significant influenceon the response. When the most important factors have been established,an RSM (Response Surface Model) design is made. This is done in orderto obtain an improved understanding in how the factors affect the responseand to do valid conclusions that can optimize the process.

2.2.1 The 2k Factorial Design

When there are many factors involved in an experimental study, and jointeffects between the factors are expected, factorial designs are often utilised.There exist several versions of the general factorial design. However, thedesign that is going to be used in this project to determine what propertiesthat influences on the result most, since it is particularly useful for screeningexperiments, is the 2k factorial design. With a complete replicate of a 2k

factorial design, it will be possible to reveal main effects as well as jointeffects between factors that influence the quality of the finished product.The name originates from the principle of the design, namely k factors at2 levels, high or low. These are often denoted with + and - sign, or by 1and 0. Since the factors can be both quantitative and qualitative, factorialdesign can be utilized in many research areas. Moreover, the designs canalso be shown geometrically. Since only two levels are tested for each factor,the response can be assumed to be linear. The name also reveals how manyruns a complete replicate requires, namely 2k. For example, an experimentwith 4 factors requires 16 runs, and an experiment with 5 factors requires

10

2.2. STATISTICAL DESIGN

32 runs. Since the number of tests runs grows exponentially for a completereplicate with number of factors, even a small number of factors will requiremany test runs. Both the experimental costs and time can exceed the desiredrestrictions, as a consequence of many test runs. Therefore, the 2k factorialdesigns are often carried out with single tests. A single replicate of a factorialdesign is often called unreplicated factorial. Hence, there will be no internalestimate of error, and conclusions made from the result may be unreliable.

2.2.2 Analysis with MODDE

MODDE (MODeling and DEsign) is software developed for generation andevaluation of statistical experimental designs and will be used for calcula-tions and visualization of the results in this project. The coefficients inthe models have been estimated with multiple linear regression (MLR), alsoknown as least squares analysis applied to several factors, which is done bysolving the system of equations:

Y = X ∗B + E�� 2.1

Where Y is an n∗m matrix of responses, X an n∗p matrix which is called theextended design matrix, B is the matrix of regression, and E is the matrixof residuals [8]. In order to measures the goodness of fit when the system ofequations is solved, MODDE computes the value of

Q2 =SS − PRESS

SS,

�� 2.2

andR2 =

SS − SSresid

SS

�� 2.3

Where PRESS is the shortening of Prediction Residual Sum of Squares,and is given by:

PRESS =∑

i

(Yi − Yi)(1− hi)2

,

SS =Sum of Squares of Y corrected for the mean, and hi the ith diagonalelement of the Hat matrix X(X ′X)−1X ′[8]. Q2 is called goodness of predic-tion, and gives a lower estimate to how well the model predicts the outcomeof new experiments, and R2, which is called goodness of fit, gives an upperestimate. R2 and Q2 can maximum reach the value 1, which would indi-cate a perfect model. Furthermore, R2 can minimum be 0, while Q2 canbe minus infinity. Q2 values larger than 0 indicates that the dimension issignificant[8]. However, it is not enough for establish the model as good. Agood model is recognised by high values of both R2 and Q2, and they shouldnot differ with more than 0,2-0,3. A Q2 value of 0.5 is usually accepted asgood, and 0.9 as excellent [8].

11

CHAPTER 2. THEORY

2.3 DETECT

Recent work at SICOMP has concluded with an automatic system for de-tecting defects on the surface. The system, called DETECT, can potentiallybe integrated in the industry to save both time and effort. DETECT willbe used in this project to quantify the quality of the surfaces in the differentexperiments. The basic principle of DETECT is to record images over atime interval with a CCD-camera, of laser speckles reflected from a surfacethat have been moistened with a fluid. By analysing the recorded imageswith cross correlation techniques, it is possible to establish where surfacedefects are located by means of computer algorithms since a larger amountof moister is located in the surface defects and therefore require more timeto fully evaporate.

2.3.1 Speckle Pattern

By illuminating a rough surface with an even light, produced by a lasersource and beam expanders, a speckle pattern can be observed. The speckleeffect is visible due to the optical property high coherence of laser light.In order to create a speckle pattern, two properties have to be fulfilled.The roughness of the surface has to be larger than the wavelength of theilluminating light and the coherence length of the illuminating light has tobe larger than the roughness of the surface. A typical speckle pattern canbe seen in figure 2.6. As seen in figure 2.6, the pattern seems to be very

Figure 2.6: Speckle pattern from one of the plates.

random. But, the pattern is stationary if the conditions are unchanged.When there is a change of the conditions, for example movement or as inthis project evaporation, the speckle pattern is affected

12

2.4. HIGH VOLTAGE INSULATION TEST

2.3.2 Correlation Analysis

Correlation analysis is a method where images, recorded over time, is anal-ysed with different computer algorithms. The technique is common in fluiddynamics research, since the technique makes it possible to track the pathof particles in the fluid. In other words, see the streamlines. How can thisbe used to detect defects when the defects are stationary? The idea is thatdefects will assemble a larger amount of moisture than the surrounding ar-eas, and therefore acquire more time to evaporate. As a consequence, thespeckle pattern will tend to sparkle for a longer time where there is a surfacedefect. The basic principle of correlation analysis is described in figure 2.7.However, a more detailed description of the algorithm can be found in [9]

Figure 2.7: Cross correlation principle [9].

2.4 High Voltage Insulation Test

Electrical insulation is used in every application where electricity is ap-plied, to prevent for example short circuits. To avoid such undesired errors,the correct electrical insulation has to be selected. The insulating materi-als are subjected to electric stresses, and the electric breakdown strengthof insulating materials depends on a variety of parameters including pres-sure, temperature, humidity, field configurations, nature of applied voltage,imperfections in dielectric materials, material of electrodes, and surface con-dition of electrodes, etc. The most common cause of insulation failure is thepresence of discharges, either within the voids in the insulation or over thesurface of the insulation [10]. Therefore, the electrical insulation test wasapplied in order to determine relative void contents in the samples.

13

3Method

3.1 Experiments

Since the experiments were carried out in order to establish the factors,which have the greatest influence of a successful SMC manufacturing process,it was of great importance to carry out the experiments under controlled cir-cumstances. Repeatability was the goal for every part in the experimentalchain. Therefore much work and testing was done before the real experi-mental procedures could start. Moreover, to gain practical knowledge froma real compression moulding industry, one day was spent at the floor in ABBPlast in Pitea. Because of technical issues with the moulding tool, only oneexperimental run could be carried out with VAM technology. Furthermore,microscope analysis was carried out for 6 different samples that had signifi-cantly separated results in the high voltage insulation test in order to verifythat insulation properties highly depended on void contents.

3.1.1 Preparing the samples

For every experimental session, the charges were prepared and then storedin air sealed containments. This was done in order to give all samplesapproximately the same amount of air exposure. The circular samples wasprepared by placing the prepreg in layers, removing the protecting plasticsfrom all layers except the top and bottom, as described in figure 3.1, andthen use a special circular cutting tool to cut the samples, as described infigure 3.2. Moreover, because of irregularities of the prepreg, which wereamplified because the sheets were always laid up in the same direction, themass first altered for the prepared charges. Typically they could be groupedinto three groups, too light, even, and too heavy. By placing mixing sheetsfrom the light and heavy groups, every charge could get almost the samemass.

3.1.2 Displacement of charges

According to Hans Bernlind at Polytec the maximum time should be 10seconds from adding the charges in the mould, to completed mouldfilling.

15

CHAPTER 3. METHOD

Figure 3.1: Prepreg prepared in layers with removed protecting plasticsexcept at top and bottom.

Figure 3.2: Charge generation with cutting tool.

This is to avoid onset of significant cure (and hence increased viscosity ofthe resin) during mouldfilling. These instructions were followed but some-times the press stalled which lead to delays in the mould for the samples.These samples were rejected in order to improve the repeatability of theexperiments. Laser lines were applied inside the tool in order to increasethe accuracy of fast charge placement. To create the patterns, two laserequipments were used, producing two laser lines each. An example of howthe laser lines were created can be seen in figure 3.3. Moreover, in order tosimulate an critical area for compression moulding of SMC, it was decidedto split the circular charges into two. As a result, a weld line was formed dueto dual charges. Instead of only using a circular charge placed in the middle,the dual charges was placed as described in figure 3.4. Two examples of weldlines can be seen in figure 3.11. Furthermore, together with the prepreg arelease agent spray was delivered by ABB Plast. The use of the spray wasnot obvious and first applied at the surfaces inside the tool before every

16

3.1. EXPERIMENTS

(a) Laser pattern. (b) Laser setup.

Figure 3.3: Example of laser pattern and laser setup.

Figure 3.4: How the dual charges were placed.

single compression moulding. As a consequence, the surfaces inside the toolsuffered from remaining SMC material after a compression moulding whichwere hard to remove. However, after guidance from Ake Westerlund at ABBPlast, the problem with remaining SMC at the tool surfaces disappeared.The proper spray technique spray was to apply it only once every day, andthen carry out a couple of preparing compression mouldings before startingwith the real.

3.1.3 Vacuum Tool

According to Hans Bernlind at Polytec, the vacuum should be enabled justbefore the moulding tool reached the top surface of the SMC charge, toget the best effect. Therefore, an adjustable switch was installed that wasconfigured to enable applied vacuum at correct tool closure position. Bystudying graphs from sensors, it was possible to adjust the height to a veryprecise level. To accomplish vacuum inside the tool as fast as possible, a

17

CHAPTER 3. METHOD

large vacuum tank was placed next to the equipment. One important thingto remember while using the vacuum tool is to cover the hole to the airpressurised ejection tool for the sample plates. Otherwise, the plug inside themoulding tool is in risk for being opened due to the vacuum, and thereforebecome filled by flowing prepreg. A mistake like that will make the ejectiontool unusable. The moulding tool with the vacuum assistance future can beseen in figure A.5.

3.2 Measurements to quantify the quality

Because of unexpected problems with the equipment a small number if sam-ple plates were made. To get as much useful information as possible fromexisting plates they were cut into pieces, as described in figure 3.5.

Figure 3.5: How the sample plates were devided to maximise the usage.

3.2.1 Surface defects with DETECT

The DETECT equipment turned out to be very sensitive and troubling touse. However, a method, which can be scrutinised, was developed with trialand error methodology. Since the results were about to be used in the facto-rial analysis, the ambition was to achieve repeatability in the measurements.The method used for DETECT results in this report have been developedin several steps. First, a method was found were the number of defects weresimilar between measurements. However, when the result images were stud-ied by eye, it was found that they were not similar and therefore the resultscould not be trusted. An enhanced method was developed which gave simi-lar results both for the number of defects and result images. Moreover, the

18

3.2. MEASUREMENTS TO QUANTIFY THE QUALITY

effect of cleaning the plates properly and often was underestimated duringthe method development phase. Therefore the plates were just cleaned once,and then repeatedly measured to find a working method and settings. Infigure 3.6, three result images before post processing can be seen were theplate only were cleaned once before the first measurement. The cleaningmethod was to first add IPA to the plate and then wipe it downwards witha sheet. As seen, the white spots which are supposed to be defects seem tobe reduced with time but the images are very similar nonetheless.

(a) 15:24 (b) 15:28

(c) 15:33

Figure 3.6: Result from DETECT when a plate were measured repeatedlywithout cleaning.

One problem that caused much confusion was dust. Therefore the surfaceshad to be properly cleaned just before they were measured. The methodthat seemed to fit the assignment was to wet a sheet with IPA, and thendraw that sheet downwards on the plate. Observe, just downwards, not incircles or up and down. Examples of two other cleaning methods that didnot work were: use pressurised air to blow the surface clean, put IPA directly

19

CHAPTER 3. METHOD

to the surface and then dry it with a dry sheet. When the samples were notproperly cleaned, it seemed like temperature of the added moisture, roomtemperature, and relative moisture contents had very large influence on theresults. When the plates were properly cleaned from dust, these problemsseemed to be of less importance. In figure 3.7, a dust particle that DETECTcategorised as a surface defect can be seen that was captured in the micro-scope before the area was carefully cleaned.

(a) Uncleaned (b) Cleaned.

Figure 3.7: Dust particle that was detected as an defect.

The problem with ghost defects, which probably occurred due to dust, cre-ated uncertain results for the factorial design analysis. A plate which maybewas in good health, could due to large amount of dust particles be analysedas a plate with bad surface conditions. However, if a surface was cleanedonce, and then measured repeatedly without any cleanings, the results be-come similar, which also could be seen in figure 3.6. The area investigatedwas 100 ∗ 76 mm. This was achieved by placing the sample 76.5 cm awayfrom the camera. The sample was then moisturised from a straight anglefor about 3 seconds with moister that held 45◦C. The settings used by theDETECT software can be seen in table 3.1. Smaller sub picture size andtime frame size caused too much noise in the pictures. In figure 3.8, the

Exposure time 30Time Step 200

Sub Picture size 4Time Frame size 4Dry Threshold 0.018Time Window 2

Defect Threshold 0.100Filter Radius 6

Table 3.1: Settings for the DETECT software.

20


Figure 3.8: Setup for DETECT equipment

DETECT equipment be seen. Notice the cardboard box that protects theexperimental setup from air blowing from the computer behind it. By usingthe settings in table 3.1, and above mentioned method, DETECT seemedto have problems to detect large defects. A plate that have been paintedcan be seen if figure 3.9(a), the area visualized contain three very large sinksin the middle. These three defects were not detected as defects every mea-surement. However, the area become white every time which can be seen infigure 3.9(b).

(a) After painting. (b) With DETECT..

Figure 3.9: Three sinks visualised after painting and with DETECT.

3.2.2 Surface defects with painting method

The idea with this test was to simulate the painting process under bad con-dition. Instead of hiding surface defects, the goal was to find a coating that

21

CHAPTER 3. METHOD

would create a blowout for every significant surface defect, which would beeasy to see in the microscope. Together with Beckers, KTH tried differentcoatings and then sent the one that seemed to be best to detect defects.Meanwhile, three ordinary spray-paintings were tested at SICOMP. Fortu-nately, one of them showed good results for very bad plates. The coatingfrom Beckers was also tested, but the spray paint found at SICOMP turnedout to be both easier to use and seemed to be more likely to create blowoutsdue to surface defects. Therefore the regular white spray paint was usedin this test since it was faster, easier to handle, and seemed to give moreblowouts. The plates were first cleaned with IPA. Then a layer was appliedto the surface by spray painting them. The painted plates were then placedin an oven at 105◦C for 10 minutes. After cooling down, the area studiedwith DETECT were also studied with microscope. Paint defects that clearlywas caused by surface defects were then counted by hand. Figure 3.10, showa clear blowout and the same area with paint removed.

(a) Blowout (b) Paint removed.

Figure 3.10: Clear surface defect detected as an blowout.

3.2.3 Image Analysis of the Weld line

During the learning period it was noticed that the weld line of the plates,created due to dual loads and hence located at the position where two flowfronts meet, had different appearance for different settings. In figure 3.11,two weld lines can be seen, before image analysis was performed, with differ-ent size due to different settings which can be found in table 4.3. Thereforea method was developed to quantify the size of this area. First, the plateswere cleaned with IPA. Second, the plates were polished with carbon pow-der. The plates were then cleaned again with IPA to remove the excessof carbon powder. However, the carbon powder located at the weld linesstayed and made them more able to be seen. Thirdly, images were recorded

22


(a) Plate 4 (b) Plate 5

Figure 3.11: Weld lines clarified from experimental run 1, before imageanalysis was performed. Manufacturing settings can be found in table 4.3

of approximately the same area that were studied with DETECT. Finally,phase analysis was performed for the recoded images with the commercialsoftware analySIS. In order to acquire reliable results from the phase analy-sis, a sharpening filter (Sharpen I) was first applied to the images, fallowedby RGB colour separation, and last the threshold was set individually foreach image.

3.2.4 Electrical Insulation

The electrical insulation test was carried out at ABB Plast and followedthe directives from one of their standard tests, IEC 60243-1, called the stepmethod. However, the steps were slightly modified to find small differences.As described in figure 3.5, squares with the dimensions 110 ∗ 110 mm wherecut out from the sample plates. Each square was then placed in an oil bathwith an electrode placed on each side of the sample, as in figure 3.12. Thevoltage was then raised to what was thought to be 40 % of the final voltage.Initial tests shoved that this value was 16 kV. More voltage was then addedevery 20th second. The first steps, up to 30 kV, were by 2 kV. After reaching30 kV, each step only added 1 kV.

3.2.5 Microscopic Analysis of Voids

While analysing the sample plates for voids with microscopic analysis, it isthe cross section of small pieces that have been cut from the plates thatare being studied. The cross sections have to be very nice and smooth forthis method to work. Therefore the pieces were placed in small cylindricalcontainers together with epoxy, and thereafter cured at 75◦C. These smallepoxy cylinders were then removed from the cylindrical containers. When

23

CHAPTER 3. METHOD

Figure 3.12: Sample plate prepared for high voltage insulation test.

the sample pieces were fixed in the hardened epoxy, they could be polishedby the grinding machine that can be seen in figure. In order to get a sur-face that can be analysed, the polish has to be done very carefully. A nicesurface is achieved by starting the grinding with a rough grinding paperand then use smoother grinding paper. In figure 3.13(b), the samples arevisualised after some polishing in the grinding machine that can be seen infigure 3.13(a). The grinding equipment had to be cleaned frequently since asmall piece of waste could devastate the surfaces which make phase analy-sis impossible. When the cross sections of the samples were prepared, theycould be analysed. A set of images were analysed for each piece to get themean value for every sample plate.

3.3 Analysis with MODDE

The default settings in MODDE appeared to be inadequate since the R2and Q2 values were insufficient. In order to improve the validity of themodels some adjustments were made individually for each experiment andresponse. The cause of poor models were reduced in two ways. Insignificanteffects were deleted from the model. They could be established for exampleby studying the coefficient plot in MODDE. Small effects were deleted fromthe model and also effects with a confidence interval of 95% that passed0, since they can be seen as statistically insignificant [8]. By studying theeffect plots, one can see which effects that were left in the model. The effectplots are sorted according to the size of the effect. Due to orthogonalityof the experimental design one can remove factors from the model withoutaffect the numerical values for effects, however, the confidence intervals may

24

3.4. VACUUM TREATMENT OF PREPREG

(a) The Struers grinding machine. (b) Samples.

Figure 3.13: Dust particle that was detected as an defect.

be shortened [8]. The other method for reducing poorness was to removeexperimental runs that heavily differed from other results. The normal prob-ability plot of residuals is an excellent tool for identifying outliners. ExceptR2 and Q2 values, the p value for regression were studied in the ANNOVAtable. A p value, which is a probability coefficient, for regression near 0indicates good models statistically and could therefore be used as a tool toindicate condition of the models.

3.4 Vacuum Treatment of Prepreg

Here follows an method description of previous work carried out at SICOMPby Greger Nilsson. The basic principle of layers and material can be seenin figure 3.14. Image 3.15, show images from the manufacturing process.Furthermore, the compression moulding process variables used in this ex-periment can be seen in table 3.2.

Charge Weight 380gPressure 11 MPaTemperature 140◦CVelocity 5 mm/sMouldning Time 300s

Table 3.2: Process variables for vacuum treated, and standard prepreg.

25

CHAPTER 3. METHOD

Figure 3.14: Illustration of vacuum tratment

26

3.4. VACUUM TREATMENT OF PREPREG

(a) The silicone bag. (b) The SMC-paste.

(c) Sprinkling of glass fibre. (d) Perforated silicone bag.

(e) Vacuum bag on top of gore-tex. (f) SMC-prepreg prepared for mould-ing.

Figure 3.15: Preparation of vacuum treated SMC-prepreg.

27

4Results

4.1 Effect of air exposion on prepreg

In a period of 24 hours the sample stored in an large but air proof containerlost 0.8% of it’s weight. The sample that was stored on a table lost 6.5%of its weight. This result clearly shows that it is of great importance thatall the samples had to be exposed to air approximately the same amount oftime in order to keep the same properties for all the samples.

4.2 Experimental run 1, Vacuum Assisted Moulding.

The factorial design, and results will here be presented for experimental run1, were vacuum level was one of the altered factors. The raw data used forMODDE analysis can be seen in table A.1. Description of utilised prepregcan be seen in table 4.1.

Material Quantity (%)Polyester resin 18Polystyrene 10Calcium Carbonate 451” Glass Fibre 20

Table 4.1: Recipe description of prepreg LS20 DE1056600-720.

4.2.1 Factorial design

29

CHAPTER 4. RESULTS

Factor 1 -1Vacuum 4 kPa 101.3 kPaPressure 1060kN 565kNVelocity 10mm/s 2mm/s

Temperature 160◦C 145◦C

Table 4.2: Values for hi and low level.

Experiment Vacuum Pressure Velocity Temperature1 -1 -1 -1 -12 1 -1 -1 -13 -1 1 -1 -14 1 1 -1 -15 -1 -1 1 -16 1 -1 1 -17 -1 1 1 -18 1 1 1 -19 -1 -1 -1 110 1 -1 -1 111 -1 1 -1 112 1 1 -1 113 -1 -1 1 114 1 -1 1 115 -1 1 1 116 1 1 1 1

Table 4.3: Experimental design for test run 1.

30

4.2. EXPERIMENTAL RUN 1, VACUUM ASSISTED MOULDING.

4.2.2 Results

MODDE analysis of DETECT response

In figure 4.1, the summary of fit for DETECT response has been plottedwhich resulted in low R2 and Q2 values which indicates a poor model, how-ever, the Q2 value was positive. Due to the poor model, only one effectcould be found with a confidence interval separated from zero. As seen infigure 4.2, the confidence interval is not significantly separated from zeroand belongs to the interaction effect between velocity and pressure. Onlyone effect seems to be significant for the response, and that is the interac-tion effect between high pressure and high velocity. The interaction effectis negative for the surface quality since the bar is positive. Therefore, highlevel of velocity and pressure cause more surface defects. When the factorsare applied one at the time, the negative bars indicate less surface defects.However, the single improvement effect from pressure and velocity is nottrustworthy since the confidence interval bars pass 0. The colour codingfor result in figure 4.3 indicate the same effects as the effects plot, in figure4.2. Applying high level of pressure or velocity at the same time indicatesa worse quality, while indicating a increase in quality when the factors areapplied one at the time.

Figure 4.1: Summary of fit for DETECT response.

31

CHAPTER 4. RESULTS

Figure 4.2: Effects plot for DETECT response.

Figure 4.3: Geometrical design and result for DETECT response.

32


Figure 4.4: Interaction plot for DETECT response.

33

CHAPTER 4. RESULTS

MODDE analysis of Paint response

High values of R2 and Q2 indicates a very good model in figure 4.5. How-ever, to achieve these levels, plate 14 had to be excluded since it was anoutliner. As seen in figure 4.6, many effects revealed with confidence inter-val separated from zero. The largest effect, which also is negative for thesurface quality, is applied vacuum. However, the effects plot indicates thatthe interaction effects between vacuum-velocity, and vacuum-pressure havea positive influence on the surface quality. Figure 4.7, clarifies what theeffects plot in figure 4.6 indicated, high level of vacuum cause decrease inquality itself but increase the quality combined with high pressure mainly,but also with high velocity. One corner is missing and one is excluded, sinceplate number 14 had to be excluded from the results, because it was anoutliner. Figure 4.8, visualise the main interaction effect between pressureand vacuum. Clearly, pressure should be at a high level when vacuum isutilised in order to minimize surface defects.

Figure 4.5: Summary of fit for paint response.

34


Figure 4.6: The effects plot for paint response.

Figure 4.7: Geometrical design and results for the paint response.

35

CHAPTER 4. RESULTS

Figure 4.8: Interaction plot for interaction effects in the paint test.

36


MODDE analysis of insulation response

Large R2 and Q2 values in figure 4.9, indicate a good model. As seenin figure 4.10, and 4.11, the combination of high temperature and appliedvacuum diminish the insulating properties significantly. The colour codingfor response clarifies the interaction effect between high temperature andapplied vacuum, since every position is marked as low where the level ishigh of both temperature and vacuum. Furthermore, the interaction is alsovisualised in figure 4.12, were the effect is plotted for altered temperatureand vacuum held at a constant level.

Figure 4.9: Summary of fit plot for insulation response.

Figure 4.10: Effect plot for insulation test.

37

CHAPTER 4. RESULTS

Figure 4.11: The geometrical design and result for insulation response.

Figure 4.12: Interactions plot for insulation test.

38


MODDE analysis of weld line response

Large R2 and Q2 values in figure 4.13, indicate a good model. The effectsplot in figure 4.14, reveal that high velocity is the single-handed best factorfor minimising the size of the weld line. Applied vacuum cause larger weldlines, but the interaction effect between applied vacuum and velocity reducethe size of the weld line. In figure 4.15, the geometrical design and responsecan be seen. The plot clarifies what figure 4.14 already has indicated. Everyposition in plot where the velocity level is high has a low response for weldline size. Applied vacuum positions is marked as high except where the ve-locity is set as high. High temperature seems to increase the size of the weldline. The two indicated interaction effects between velocity-temperature,and vacuum-velocity, that can be seen in figure 4.14, can also be seen infigure 4.16 and 4.17 where the interaction effect is clarified.

Figure 4.13: Summary of fit for weld line response.

39

CHAPTER 4. RESULTS

Figure 4.14: The effects plot for weld line response.

Figure 4.15: Geometrical design and result for the weld line response.

40


Figure 4.16: Interaction plot for interactions in the weld line test. Here, theinteraction is between temperature and velocity.

Figure 4.17: Interaction plot for interactions in the weld line test. Here, theinteraction is between temperature and vacuum.

41

CHAPTER 4. RESULTS

4.2.3 Comparison of DETECT and Paint

The plots in figure 4.18 show similar results for some of the plates. However,many of the plates show different result for detect and paint response. Figure4.19, show how the effect plot for DETECT and paint response should looklike with every single and two factor interaction included in the model, andwithout excluding any raw data. As seen, the two effect plots do not givethe same indications for main effects.

Figure 4.18: Replicate plot for DETECT and paint response.

Figure 4.19: Effects plots for DETECT and Paint response.

42

4.3. EXPERIMENTAL RUN 2

4.3 Experimental run 2

For this experimental run, the same prepreg was used as in experimental run1, with contents descried in table 4.1. However, the prepreg had aged muchlonger (about 20 weeks). Another difference from previous experimentalrun is the change of moulding tool. Because of technical issues, an oldmoulding tool without the possibility to mount vacuum had t be used. But,the dimensions is still a circular plate with diameter 30cm.


Only 3 factors could be altered, since the possibility to mount vacuum didnot exist with this moulding tool.

Factor 1 -1Pressure 1060kN 565kNVelocity 10mm/s 2mm/s

Temperature 160◦C 145◦C

Table 4.4: Hi and low levels for experimental run 2, 3, and 4

Exp No Pressure Velocity Temperature1 -1 -1 -12 1 -1 -13 -1 1 -14 1 1 -15 -1 -1 16 1 -1 17 -1 1 18 1 1 19 -1 -1 -110 1 -1 -111 -1 1 -112 1 1 -113 -1 -1 114 1 -1 115 -1 1 116 1 1 1

Table 4.5: Factorial design for experimental run 2, 3, and 4.

43

CHAPTER 4. RESULTS

4.3.2 Results

As seen in figure 4.20 the R2 and Q2 values are low and hence no resultsare presented for this experimental run.

(a) DETECT. (b) Paint defects.

(c) Insulation.

Figure 4.20: Summary plots for measured responses.

44



Many of the plates gave similar results for both DETECT and paint responseas seen in the replicate plots in figure 4.21. Figure 4.22, show the effect plotsfor DETECT and paint response, with every single as well as two factor twofactor interaction included in the model, and no excluded raw data. Thebars for the different effects have the same sign for the two methods butordered differently.

Figure 4.21: Replicate plot for DETECT and paint response. Plate number14 is excluded from the plots.

Figure 4.22: Effect plots for DETECT and paint response.

45

CHAPTER 4. RESULTS


Here, experimental run 2 is replicated but with a newer prepreg. The prepregin this experimental run is about 7 weeks old.


The factorial design can be seen in table 4.5.

4.4.2 Results

As seen in figure 4.23 low R2 and Q2 values indicates poor models for everyresponse. However, the response from DETECT and insulation measure-ments resulted in small indications. First, in figure 4.24 were the DETECTresponse have been analysed, a small indication that high temperature canincrease surface defects can be seen. Second, figure 4.25, show that the in-teraction effect between velocity and pressure possibly can have an positiveeffect on insulating properties.

46


(a) DETECT (b) Insulation

(c) Paint

Figure 4.23: Summary of fit plots for the responses.

Figure 4.24: Effects plot for DETECT response.

47

CHAPTER 4. RESULTS

Figure 4.25: Effects plot for insulation responce.

48



The two tests in figure 4.26, show similar response for the different plates. Infigure 4.27, the effect plots have can be seen with every single and two factorinteraction effect included in the model. The two measurement methods gavevery similar results for effects.


Figure 4.27: Effects plot for DETECT and paint response.

49

CHAPTER 4. RESULTS


This experimental run is a replicate of experimental run 2, however, a dif-ferent type of prepreg were used in this experimental run which can be seenin table 4.6. The main difference compared to the earlier prepreg is thepresence of Aluminium Hydroxide and the heavily reduced amount of Cal-cium Carbonate. Due to this change in prepreg recipe, the moulded SMCproduct obtain fire retardant properties.

Material Quantity (%)Polyester resin 16Polystyrene 9Calcium Carbonate 5Aluminium Hydroxide 401” Glass Fibre 20

Table 4.6: Approximate description of prepreg FRH20 DE1055508-720.


The factorial design can be seen in table 4.5.

4.5.2 Results

R2 and Q2 values are low in figure 4.28, which indicates poor models. As aconsequence, no results for main effects are presented for this experimentalrun.

50


(a) DETECT. (b) Paint.

(c) Insulation.

Figure 4.28: Summary plots for the responses.

51

CHAPTER 4. RESULTS


The two measurement techiques show similar results for some of the platesin figure 4.29. Therefore similar rankings for effects can be seen in figure4.30.



52

4.6. THE EFFECT OF AGED PREPREG

4.6 The effect of aged prepreg

Experimental run 2 and 3 had prepreg with the same recipe, but the prepregin experimental run 1 was almost 13 weeks older.

4.6.1 Factorial Design

By combining experimental run 2 and 3, a 24 factoral design is achived withthe 4:th constant age. The factorail design can be seen in table 4.8, and theproperties for high and low level can be seen in table 4.7.

Factor 1 -1Pressure 1060kN 565kNVelocity 10mm/s 2mm/s

Temperature 160◦C 145◦CAge 7 weeks 20 weeks

Table 4.7: Hi and low levels for experimental run 2, and 3 combined.

Exp No Pressure Velocity Temperature Age1 -1 -1 -1 -12 1 -1 -1 -13 -1 1 -1 -14 1 1 -1 -15 -1 -1 1 -16 1 -1 1 -17 -1 1 1 -18 1 1 1 -19 -1 -1 -1 110 1 -1 -1 111 -1 1 -1 112 1 1 -1 113 -1 -1 1 114 1 -1 1 115 -1 1 1 116 1 1 1 1

Table 4.8: Factorial design, which is replicated once, for experimental run 2and 3 combined.

53

CHAPTER 4. RESULTS

4.6.2 Results

Low R2 and Q2 values indicate a poor models for every response as seen infigure 4.31 and 4.33. Hence, no effects could be found from the DETECTand paint measurements. However, as seen in figure 4.33, the Q2 is stillpositive for the insulation response. As a consiquense, a main effect couldbe found when every effects was subtracted from the model except the agefactor. In figure 4.33, it can be seen that newer material is indicated to havea positive effect for insulating properties.

MODDE analysis of DETECT and paint response

Figure 4.31: Summary of fit plots for responses.

54

4.6. THE EFFECT OF AGED PREPREG

MODDE analysis of Insulation response


Figure 4.33: Effects plot for insulation.

55

CHAPTER 4. RESULTS


The response is very similar for DETECT and paint method as seen in thereplicate plot in figure 4.34. From the effects plots in figure 4.35, it can beseen that calculated effects are similar for DETECT and paint response whenevery single and two factor interaction effects are included in the model, withno excluded raw data.



56

4.7. THE EFFECT OF CHANGING PREPREG

4.7 The effect of changing prepreg

Here, experimental run 3 and 4 was combined, since they contain differentprepregs.

4.7.1 Factorial Design

The different prepregs that were used in experimental run 3 and 4 hade thesame moulding properties. Therefore, the two designs could be combinedinto one 24 factorial design with the new qualitative factor prepreg material.

Exp No Pressure Velocity Temperature Material1 -1 -1 -1 -12 1 -1 -1 -13 -1 1 -1 -14 1 1 -1 -15 -1 -1 1 -16 1 -1 1 -17 -1 1 1 -18 1 1 1 -19 -1 -1 -1 110 1 -1 -1 111 -1 1 -1 112 1 1 -1 113 -1 -1 1 114 1 -1 1 115 -1 1 1 116 1 1 1 1

Table 4.9: Factorial design, which is replicated once, for experimental run3, and 4 combined.

57

CHAPTER 4. RESULTS

4.7.2 Results

MODDE analysis of Insulation response

By removing every effect except material, the summary of fit plot indicatea good model.

The only factor with confidence interval separated from zero was thematerial factor. Since the bar is negative for material B (Aluminium Hy-droxide), the plot reveals that material B increased the insulation properties.

The colour coding clarifies the result from the effect plot. Every cornerin the cube for Aluminum Hydroxide prepreg show low insulation results.


Figure 4.37: Effect plot for insulation.

58


Figure 4.38: The geometrical design and response for insulation test.

59

CHAPTER 4. RESULTS

MODDE analysis of DETECT and Paint response

Low R2 and Q2 values indicate poor models. Therefore, no conclusionscould be made concerning effects.

Figure 4.39: Summary of fit plots for responses.

60



As seen in the replicate plot in figure 4.40, the results are similar for DE-TECT and paint measurements. Hence, the effects plots in figure 4.41 showsimilar results when raw data for every factor and two factor interaction isincluded in the model. The results are very similar for the two measurementmethods.



61

CHAPTER 4. RESULTS

4.8 Microscopical analysis of void contents

The high voltage insulation test gave significant level changes in insulationproperties for plate 11 - 16 in experimental run 1. Therefore, they werechosen for microscope analysis in order to study if void contents could bethe reason for poorer insulation properties. In this test plate number 11-16 from experimental run 1 were studied, with settings that can be seen intable 4.3. The exact level of void contents could not be measured since someareas become dark, which clearly was void free also were counted as voidsin the phase analysis. However, a significant difference could be seen for themeasured plates. Clearly, the plates that was created with vacuum assistedmoulding method had a higher level of internal voids. In figure 4.42, 4.43,and 4.45 cross sections is visualised with help from merged images.

62

4.8. MICROSCOPICAL ANALYSIS OF VOID CONTENTS

Figure 4.42: Plate 11, approximately 0.75% of voids. Small amount of voidsand many of the dark areas are most likely results from insufficient polishing.The image is from the middle of the weld line, bottom to toppom.

63

CHAPTER 4. RESULTS

Figure 4.43: Plate 12, approximately 1.36% of voids. Many typical voidscan be seen that certainly is voids. The image is from the middle of theweld line and show bottom to top of the plate.

64


Figure 4.44: Plate 13, approximately 0.79% voids. The image is tilted 90◦

and show the middle section along the plate were the weld line was located.65

CHAPTER 4. RESULTS

Figure 4.45: Plate 14, approximately 1.15% voids. Show fewer defects thanplate 12, however, these defect are significantly larger in size. The imageshow bottom to top of the plate from the area were the weld line was located.

66



and show the middle section along the plate were the weld line was located.

67

CHAPTER 4. RESULTS


and show the middle section along the plate were the weld line was located.

68

4.9. VACUUM TREATMENT FOR PREPREG

4.9 Vacuum treatment for prepreg

(a) Vacuum treated (b) Standard

Figure 4.48: Vacuum treated prepreg VS. standard prepreg.

(a) Vacuum treated (b) Standard

Figure 4.49: Vacuum treated prepreg VS. standard prepreg.

69

CHAPTER 4. RESULTS

Std. SMC Vacuum SMC Std. Prepreg Vacuum Prepreg1.76 1.22 9.47 8.641.92 1.11 20.63 0.091.36 1.7 22.06 0.041.16 0.75 25.06 4.821.15 1.77 25.88 20

Average 1.47 1.31 20.62 6.72StdAv 0.35 0.43 6.59 8.25

Figure 4.50: Void contents for standard SMC-prepreg, and vacuum treatedSMC-prepreg, before and after compression moulding.

70

5Conclusions and Future Work

The main conclusion that can be drawn from the experimental run whichinvolved vacuum, is that vacuum assisted moulding far from always facilitatethe SMC-process. Applied alone, vacuum often reduce the quality. Butapplied together with other factors, such as high velocity or temperature,vacuum can improve the quality for SMC-products.

The MODDE analysis of the surface defects measurements with the paintmethod, indicates that only applying vacuum cause more surface defects,while applying vacuum simultaneously as high pressure or velocity seems toreduce amount of surface defects.

No positive effect regarding insulating properties were found in experi-mental run 1. However, negative effects were found that should be avoidedin order to create products with good insulating properties. The effect ofhigh temperature and applied vacuum reduce the insulating properties, bothsingle-handed and primary as interaction effect. This negative effect is prob-ably caused by boiling styrene, since the boiling point of styrene is decreasedunder vacuum condition [11].

In order to create small weld lines, the MODDE analysis indicated thathigh velocity should be utilised. Moreover, vacuum alone should be avoided,but the combination of vacuum and high velocity seems to reduce the sizeof the weld line.

However, more experimental work have to be carried out in order to im-prove the conclusions by using more advanced models, and thereby be ableto give a better description in when, or when not to use vacuum, and atwhich level. The level of vacuum were only on, or off, in this study. Forinstance, it is commonly known in the industry that some prepreg materialsbehave worse under full vacuum than under half vacuum. Therefore it wouldbe interesting to carry out experiment at different levels of vacuum

Experimental run 2, 3, and 4 did not show any significant main effects.The reason for this is not clear. One reason could be the moulding tool.Another possible reason can be that the prepregs used was too old andtherefore suffered from fluctuations in quality. MODDE analysis of experi-mental run 2 and 3 supports this thesis since the analysis indicates that age

71

CHAPTER 5. CONCLUSIONS AND FUTURE WORK

have negative effect on insulation properties. The analysis also showed thatage of prepreg did not have any significant effect on surface quality. HigherR2 values in experimental run 3 than 2, and raw data in tables A.2 and A.3and indicates that the older prepreg had larger fluctuations in test results aswell as lower average insulation results. The older prepreg had an averageof 34.375kV while the newer had an average of 38kV.

The comparison between DETECT and Paint results showed similar re-sults for several experimental runs, especially those who were done with theold moulding tool without the vacuum future. One explanation, of course,is the human error since the process could not be carried out automatically.The other explanation could be a smoother surface in the new moulding toolwith vacuum future, which in some way cause measurement problems withDETECT. However, since the two measurement techniques show similar re-sults in many cases, when the result image is studied by eye, it is probablypossible to adjust the DETECT equipment and settings with further work,so that it can perform quality measurements in automatic mode and therebybe more reliable since counting defects by eye is both time consuming andunreliable process when many samples are analysed.

The microscope analysis of the 6 sample plates with significant difference ininsulating properties verified the thesis that a higher level of internal voidswould cause lower insulating properties.

The vacuum treatment method for prepreg was found to be a successfulmethod for creating SMC-prepreg regarding amount of internal voids beforecompression moulding. However, the vacuum treated prepreg did not showany significant reduction in void contents, compared to standard prereg af-ter compression moulding.

72

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[1] Kia H.G. Sheet Molding Compounds: Science and Technology. Hanser,Munich, 1993.

[2] Pierre Cormier. Formation of surface voids in smc. Technical ReportLiTH-IKP-PR–04/08–SE, Engineering Materials, Linkopings Univer-sitet, 2004.

[3] Vincent Biard. Priming and painting of smc. Technical ReportLiTH-IKP-PR–04/06–SE, Engineering Materials, Linkopings Univer-sitet, 2004.

[4] L. Saggese M. Revellino and E. Gaiero. Compression molding of smcs.Comprehensive Composite Materials, 2:763–805.

[5] Torbjorn Odenberger. PhD thesis, Lulea University of Technology,2005.

[6] Anders Sjogren. Pinholes and blowouts in smc: A summary of resultsobtained in the kex project. Technical Report TR04-05, SICOMP, 2004-07-05.

[7] Douglas C. Montgomery. Design and Analysis of Experiments. Wiley,sixth edition.

[8] N. Kettaneh-Wold C. Wikstrom L. Eriksson, E. Johansson and S. Wold.Design of Experiments - Principles and Applications. Umea: UmetricsAcademy ; Stockholm: Learnways, 2000.

[9] Andreas Pettersson. Dekorrelationsmatningar pa ytor som forandras.Technical Report CR06-012, SICOMP, 2006-02-24.

[10] M.S. Naidu and V Kamaraju. High Voltage Engineering. McGraw-Hill,second edition.

[11] T. S. Lundstrom et al. Void formation in rtm. Journal of ReinforcedPlastics and Composites, 12:1339–1349, December 1993.

73

AAppendix

A.1 Raw data experimental run 1

Exp No Detect Paint Insulation Weld Line1 8 0 39 1.182 8 3 38 18.83 2 0 38 1.24 0 1 39 24.55 0 0 37 1.566 1 2 39 6.487 1 0 38 1.128 5 0 38 4.449 3 0 38 2010 3 3 33 35.4111 4 0 38 16.712 2 1 32 25.2913 1 0 39 0.8214 3 0 32 0.6315 2 1 39 0.6516 2 0 33 6.55

Table A.1: Results for experimental run 1. DETECT, as well as paint isgiven in number of defects. Insulation is given in how many kV the asmplemanaged to handle. Weld line is given in % of image covered by the weldline.

75

APPENDIX A. APPENDIX


Exp No Detect Paint Insulation1 1 0 362 1 0 363 2 1 364 1 1 395 0 0 266 0 0 357 0 0 158 2 1 369 2 0 3510 1 1 3811 0 0 3512 0 0 3613 0 0 3714 15 40 3615 1 1 3716 0 0 37

Table A.2: Results for experimental run 2. DETECT, as well as paint isgiven in number of defects. Insulation is given in how many kV the asmplemanaged to handle.

76

A.3. RAW DATA EXPERIMENTAL RUN 3




77

APPENDIX A. APPENDIX




A.5 CAD drawing of Vacuum tool

78

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Process Study on Compression Moulding of SMC using ...1017911/FULLTEXT01.pdf · MASTER’S THESIS 2008:052 CIV Jimmy Olsson Process Study on Compression Moulding of SMC using Factorial