Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik
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The aim of this study is to describe the consolidation in thermoplastic tape placement
process to obtain high quality structure, making the process viable for automotive
and aerospace industrial applications. The major barrier in this technique is very
short residence time of material under the consolidation roller to accomplished complete
polymer diffusion in the bonded region. Hence investigation is performed to find
out the optimize manufacturing parameters by extensive material, process, product
testing and through process simulation.
Temperature distribution and convective heat transfer under the hot gas torch is experimentally
mapped out. Bonding process inside the laminate is the combine effect
of layers (tapes) intimate contact Dic development and resulting polymer diffusion Dh
at these contacted sections. Three energy levels are identified based on the process
velocity and hot gas flow combinations. For the low energy parameter combinations,
the energy input to the incoming tape and substrate material is limited and result in
incomplete intimate contact which restricts the bonding process. On other hand high
energy input although could increase the bonding degree Db even up to the 97%, but
also activate the thermal degradation phenomena. It is found out that the rate of polymer
healing (diffusion) and polymer crosslinking follows the Arrhenius laws with the
activation energies of 43 KJ/mol and 276 KJ/mol. The polymer crosslinking at high
temperature exposure hinder the polymer diffusion process and reduces the strength
development. So the parameters combination at intermediate energy level provides
the opportunity of continuous interlaminar strength improvement through out the layup
process.
Deformation of tape edges is identified as the dictating factor for the laminate’s transverse
strength. Tape placement with slight overlap reinforced the transverse joint by
more 10 % as compared to pure matrix joint. Finally the simulation tool developed in
this research work is used for identifying the existing limitation to achieve full consolidation.
A parameter study shows that extended consolidation either by mean of additional
pass or by increasing consolidation length widens the high strength (over 90%)
bonding degree Db contour. Thus high lay-up velocity (up to 7 m/min) is viable for industrial
production rate.
In recent years the consumption of polymer based composites in many engineering
fields where friction and wear are critical issues has increased enormously. Satisfying
the growing industrial needs can be successful only if the costly, labor-intensive and
time-consuming cycle of manufacturing, followed by testing, and additionally followed
by further trial-and-error compounding is reduced or even avoided. Therefore, the
objective is to get in advance as much fundamental understanding as possible of the
interaction between various composite components and that of the composite against
its counterface. Sliding wear of polymers and polymer composites involves very
complex and highly nonlinear processes. Consequently, to develop analytical models
for the simulation of the sliding wear behavior of these materials is extremely difficult
or even impossible. It necessitates simplifying hypotheses and thus compromising
accuracy. An alternative way, discussed in this work, is an artificial neural network
based modeling. The principal benefit of artificial neural networks (ANNs) is their ability
to learn patterns through a training experience from experimentally generated data
using self-organizing capabilities.
Initially, the potential of using ANNs for the prediction of friction and wear properties
of polymers and polymer composites was explored using already published friction
and wear data of 101 independent fretting wear tests of polyamide 46 (PA 46) composites.
For comparison, ANNs were also applied to model the mechanical properties
of polymer composites using a commercial data bank of 93 pairs of independent Izod
impact, tension and bending tests of polyamide 66 (PA 66) composites. Different
stages in the development of ANN models such as selection of optimum network
configuration, multi-dimensional modeling, training and testing of the network were
addressed at length. The results of neural network predictions appeared viable and
very promising for their application in the field of tribology.
A case example was subsequently presented to model the sliding friction and wear
properties of polymer composites by using newly measured datasets of polyphenylene
sulfide (PPS) matrix composites. The composites were prepared by twinscrew
extrusion and injection molding. The dataset investigated was generated from
pin-on-disc testing in dry sliding conditions under various contact pressures and sliding speeds. Initially the focus was placed on exploring the possible synergistic effects
between traditional reinforcements and particulate fillers, with special emphasis on
sub-micro TiO2 particles (300 nm average diameter) and short carbon fibers (SCFs).
Subsequently, the lubricating contributions of graphite (Gr) and polytetrafluoroethylene
(PTFE) in these multiphase materials were also studied. ANNs were trained
using a conjugate gradient with Powell/Beale restarts (CGB) algorithm as well as a
variable learning rate backpropagation (GDX) algorithm in order to learn compositionproperty
relationships between the inputs and outputs of the system. Likewise, the
influence of the operating parameters (contact pressure (p) and sliding speed (v))
was also examined. The incorporation of short carbon fibers and sub-micro TiO2
particles resulted in both a lower friction and a great improvement in the wear resistance
of the PPS composites within the low and medium pv-range. The mechanical
characterization and surface analysis after wear testing revealed that this beneficial
tribological performance could be explained by the following phenomena: (i)
enhanced mechanical properties through the inclusion of short carbon fibers, (ii)
favorable protection of the short carbon fibers by the sub-micro particles diminishing
fiber breakage and removal, (iii) self-repairing effects with the sub-micro particles, (iv)
formation of quasi-spherical transfer particles free to roll at the tribological contact.
Still, in the high pv-range stick-slip sliding motion was observed with these hybrid
materials. The adverse stick-slip behavior could be effectively eliminated through the
additional inclusion of solid lubricant reservoirs (Gr and PTFE), analogous to the
lubricants used in real ball bearings. Likewise, solid lubricants improved the wear resistance
of the multiphase system PPS/SCF/TiO2 in the high pv-range (≥ 9 MPa·m/s).
Yet, their positive effect, especially that of graphite, was limited up to certain volume
fraction and loading conditions. The optimum results were obtained by blending
comparatively low amounts of Gr and PTFE (≈ 5 vol.% from each additive). An introduction
of softer sub-micro particles did not bring the desired ball bearing effect and
fiber protection. The ANN prediction profiles for PPS tribo-compounds exhibited very
good or even perfect agreement with the measured results demonstrating that the
target of achieving a well trained network was reached. The results of employing a
validation test dataset indicated that the trained neural network acquired enough
generalization capability to extend what it has learned about the training patterns to
data that it has not seen before from the same knowledge domain. Optimal brain surgeon (OBS) algorithm was employed to perform pruning of the network
topology by eliminating non-useful weights and bias in order to determine if the
performance of the pruned network was better than the fully-connected network.
Pruning resulted in accuracy gains over the fully-connected network, but induced
higher computational cost in coding the data in the required format. Within an importance
analysis, the sensitivity of the network response variable (frictional coefficient
or specific wear rate) to characteristic mechanical and thermo-mechanical input variables
was examined. The goal was to study the relationships between the diverse
input variables and the characteristic tribological parameters for a better understanding
of the sliding wear process with these materials. Finally, it was demonstrated that
the well-trained networks might be applied for visualization what will happen if a certain
filler is introduced into a composite, or what the impacts of the testing conditions
on the frictional coefficient and specific wear rate are. In this way, they might be a
helpful tool for design engineers and materials experts to explore materials and to
make reasoned selection and substitution decisions early in the design phase, when
they incur least cost.
Point defects in piezoelectric materials – continuum mechanical modelling and numerical simulation
(2010)
The topic of this work is the continuum mechanic modelling of point defects in piezoelectric materials. Devices containing piezoelectric material and especially ferroelectrics require a high precision and are exposed to a high number of electrical and mechanical load cycles. As a result, the relevant material properties may decrease with increasing load cycles. This phenomenon is called electric fatigue. The transported ionic and electric charge carriers can interact with each other, as well as with structural elements (grain boundaries, inhomogeneities) or with material interfaces (domain walls). A reduced domain wall mobility also reduces the electromechanical coupling effect, which leads to the electric fatigue effect. The materials considered here are barium titanate and lead zirconate titanate (PZT), in which oxygen vacancies is the most mobile and most frequently appearing defect species. Intentionally introduced foreign atoms (dopants) can adjust the material properties according to their field of application by generating electric dipoles with the vacancies. Agglomerations of point defects can strongly influence the domain wall motion. The domain wall can be slowed down or even be stopped by the locally varying fields in the vicinity of the clusters. Accumulations of point defects can be detected at electrodes, pores or in the bulk of fatigued samples. The present thesis concentrates focuses on the self interaction behaviour of point defects in the bulk. A micro mechanical continuum model is used to show the qualitative and the quantitative interaction behaviour of defects in a static setup and during drift processes. The modelling neglects the ferroelectric switching mechanisms, but is applicable to every piezoelectric material. The underlying differential equations are solved by means of analytical (Green's functions) and numerical (Finite Differences with discrete Fourier Transform) methods, depending on the boundary conditions. The defects are introduced as localised Eigenstrains, as electric charges and as electric dipoles. The required defect parameters are obtained by comparisons with atomistic methods (lattice statics). There are no standardised procedures available for the parameter identification. In this thesis, the mechanical parameter is obtained by a comparison of relaxation volumes of the atomic lattice and the continuum solution. Parameters for isotropic and anisotropic defect descriptions are identified. The strength of the electric defect is obtained by a comparison of the electric internal energies of atomistics and continuum. The appearing singularities are eliminated by taking only the energy difference of a infinite crystal and a periodic cell into account. Both identification processes are carried out for the cubic structure of barium titanate, which decouples the mechanical and the electrical problem. The defect interaction is analysed by means of configurational forces. The mechanical defect parameter generates a directional short-range attraction between defects. An electrical defect parameter produces the long-range Coulomb interaction, which predicts a repulsion of two similar charges. Additionally, an interaction with defect dipoles is taken into account. It is shown that a defect agglomeration is possible for any static defect configuration. Finally, defect drift is simulated using a thermodynamically motivated migration law based on configurational forces. In this context, the migration of point defects due to self interaction, and the influence of external fields is investigated.
The aim of this thesis was to link Computational Fluid Dynamics (CFD) and Population Balance Modelling (PBM) to gain a combined model for the prediction of counter-current liquid-liquid extraction columns. Parts of the doctoral thesis project were done in close cooperation with the Fraunhofer ITWM. Their in-house CFD code Finite Pointset Method (FPM) was further developed for two-phase simulations and used for the CFD-PBM coupling. The coupling and all simulations were also carried out in the commercial CFD code Fluent in parallel. For the solution methods of the PBM there was a close cooperation with Prof. Attarakih from the Al-Balqa Applied University in Amman, Jordan, who developed a new adaptive method, the Sectional Quadrature Method of Moments (SQMOM). At the beginning of the project, there was a lack of two-phase liquid-liquid CFD simulations and their experimental validation in literature. Therefore, stand-alone CFD simulations without PBM were carried out both in FPM and Fluent to test the predictivity of CFD for stirred liquid-liquid extraction columns. The simulations were validated by Particle Image Velocimetry (PIV) measurements. The two-phase PIV measurements were possible when using an iso-optical system, where the refractive indices of both liquid phases are identical. These investigations were done in segments of two Rotating Disc Contactors with 150mm and 450mm diameter to validate CFD at lab and at industrial scale. CFD results of the aqueous phase velocities, hold-up, droplet raising velocities and turbulent energy dissipation were compared to experimental data. The results show that CFD can predict most phenomena and there was an overall good agreement. In the next steps, different solution methods for the PBM, e.g. the SQMOM and the Quadrature Method of Moments (QMOM) were implemented, varied and tested in Fluent and FPM in a two-fluid model. In addition, different closures for coalescence and breakage were implemented to predict drop size distributions and Sauter mean diameters in the RDC DN150 column. These results show that a prediction of the droplet size distribution is possible, even when no adjustable parameters are used. A combined multi-fluid CFD-PBM model was developed by means of the SQMOM to overcome drawbacks of the two-fluid approach. Benefits of the multi-fluid approach could be shown, but the high computational load was also visible. Therefore, finally, the One Primary One Secondary Particle Method (OPOSPM), which is a very easy and efficient special case of the SQMOM, was introduced in CFD to simulate a full pilot plant column of the RDC DN150. The OPOSPM offers the possibility of a one equation model for the solution of the PBM in CFD. The predicted results for the mean droplet diameter and the dispersed phase hold up agree well with literature data. The results also show that the new CFD-PBM model is very efficient from computational point of view (two times less than the QMOM and five times less than the method of classes). The overall results give rise to the expectation that the coupled CFD-PBM model will lead to a better, faster and more cost-efficient layout of counter-current extraction columns in future.
Mechanical and electrical properties of carbon nanofiber–ceramic nanoparticle–polymer composites
(2010)
The present research is focused on the manufacturing and analysis of composites consisting of a thermosetting polymer reinforced with fillers of nanometric dimensions. The materials were chosen to be an epoxy resin matrix and two different kinds of fillers: electrically conductive carbon nanofibers (CNFs) and ceramic titanium dioxide (TiO2) and aluminium dioxide (Al2O3) nanoparticles. In an initial step of the work, in order to understand the effect that each kind of filler had when added separately to the polymer matrix, CNF–EP and ceramic nanoparticle–EP composites were manufactured and tested. Each type of filler was dispersed in the polymer matrix using two different dispersion technologies. CNFs were dispersed in the resin with the aid of a three roll calender (TRC) whereas a torus bead mill (TML) was used in the ceramic nanoparticle case. Calendering proved to be an efficient method to disperse the untreated CNFs in the polymer matrix. The study of the physical properties of undispersed CNF composites showed that the tensile strength and the maximum sustained strain, were more sensitive to the state of dispersion of the nanofibers than the elastic modulus, fracture toughness, impact energy and electrical conductivity (for filler loadings above the percolation threshold of the system). Rheological investigation of the uncured CNF–epoxy mixture at different stages of dispersion indicated the formation of an interconnected nanofiber network within the matrix after the initial steps of calendering. CNF–EP composites showed better mechanical performance than the unmodified polymer matrix. However, the tensile modulus and strength of the CNF composites accused the presence of remaining nanofiber clusters and did not reach theoretically predicted values. Fracture toughness and resistance against impact did not seem to be so sensitive to the state of nanofiber dispersion and improved consistently with the incorporation of the CNFs. The electrical conductivity of the CNF composites saw an eight orders of magnitude percolative enhancement with increasing nanofiber content. The percolation threshold for the achieved level of CNF dispersion was found to be 0.14 vol. %. It was also determined that, for these composites, the main mechanism of electrical transmission was the electron tunnelling mechanism. Ceramic nanoparticle–EP composites were manufactured using TiO2 and Al2O3 particles as fillers in the epoxy matrix. Mechanical dispersion of the nanoparticles in the liquid polymer by means of a torus bead mill dissolver led to homogeneous distributions of particles in the matrix. Remaining particle agglomerates had a mean value of 80 nm. However, micrometer sized agglomerates could clearly be observed in the microscopical analysis of the composites, especially in the TiO2 case. The inclusion of the nanoparticles in the epoxy resin resulted in a general improvement of the modulus, strength, maximum sustained strain, fracture toughness and impact energy of the polymer matrix. Nanoparticles were able to overcome the stiffness/toughness problem. On the other hand, nanoparticle–EP composites showed lower electrical conductivity than the neat epoxy. In general, there were no significant differences between the incorporation of TiO2 or Al2O3 particles. Based on the previous results, CNFs and nanoparticles were combined as fillers to create a nanocomposite that could benefit from the electrical properties provided by the conductive CNFs and, at the same time, have improved mechanical performance thanks to the presence of the well dispersed ceramic nanoparticles. Nanoparticles and CNFs were dispersed separately to create two batches which were blended together in a dissolver mixer. This method proved effective to create well dispersed CNF–nanoparticle–epoxy composites which showed improved electrical and mechanical properties compared with the neat polymer matrix. The well dispersed ceramic nanofillers were able to introduce additional energy dissipating mechanisms in the CNF–EP composites that resulted in an improvement of their mechanical performance. With high volume loadings of nanoparticles most of the reinforcement came from the presence of the nanoparticles in the polymer matrix. Therefore, the observed trends were, in essence, similar to the ones observed in the ceramic nanoparticle–EP composites. The enhancement in the mechanical performance of the CNF composites with the inclusion of ceramic nanoparticles came at the price of an increase in the percolation threshold and a reduction of the electrical conductivity of the CNF–nanoparticle–EP composites compared with the CNF–EP materials. A modified Weber and Kamal’s fiber contact model (FCM) was used to explain the electrical behaviour of the CNF–nanoparticle–EP composites once percolation was achieved. This model was able to fit rather accurately the experimentally measured conductivity of these composites.