07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8
Browse
5 results
Search Results
Item Open Access FIB-SEM tomography for porosity characterization of inkjet printed nanoparticle gold ink(2024) Ruehl, Holger; Reguigui, Hajer; Guenther, Thomas; Zimmermann, AndréInkjet printing is a versatile technology for the manufacturing of electronic devices to be used in various applications [1,2]. Common inks to create conductive layers are suspensions of a solvent with metal nanoparticles such as gold or silver [3]. After the deposition and solidification of an ink on a substrate, the metal nanoparticles are sintered to realize the conductivity of the printed layer. A porous, solid metal matrix remains, whereby the conductivity of the metal layer tends to be dependent on the porosity. To characterize the porosity of inkjet printed conductive layers, focused ion beam-scanning electron microscope (FIB-SEM) tomography is suggested as a potential characterization method in the presented study. For the experiment, a wafer diced silicon substrate with size of 10 x 10 mm² was used, onto which a 1.2 µm thin layer of commercially available nanoparticle gold ink was inkjet printed and then sintered. Subsequently, a four-step procedure for the FIB-SEM tomography-based porosity characterization was performed: 1) FIB preparation of the volume of interest (VOI), 2) serial sectioning including image acquisition, 3) image processing and 4) 3D-reconstruction and porosity analysis. The steps 1) and 2) were conducted using a FIB-SEM dual beam system ZEISS AURIGA 40 (Carl Zeiss Microscopy Deutschland GmbH, Germany). Prior to serial sectioning, a thin platinum layer was FIB induced deposited on top of the inkjet printed gold layer. A cube-shaped VOI with the size 5000 x 6000 x 5000 nm³ was then prepared by FIB milling. The surface to be sectioned was end face polished and a line trench serving as a reference marker for the image processing was milled along the VOI. The prepared VOI prior to FIB sectioning is shown in Figure 1. a). Next, the serial sectioning was conducted. The ion acceleration voltage was set to 30 kV. The aperture current was set to 50 pA, resulting in an ion beam spot size of 12.5 nm, which corresponds to the section slice thickness. No melting and re-sintering of the solid metal structure could be observed during sectioning. SEM images of the revealing surface areas were acquired with 1024 x 768 pixels image resolution and a pixel size of 5.82 nm. Both a secondary electron (SE) detector as well as a backscattered electron (BSE) detector were used for imaging. In total, a 2D stack of 368 SEM images was recorded. For comparison of individual sections, Figure 1. b) and c) show BSE detector images of the cross-sectioned VOI after slice 70 and slice 140. One can clearly see that the size and distribution of sintered metal particles varies along the VOI, forming a porosity network within the solid gold. Since the images acquired with the BSE detector presented a higher contrast and thus, a better distinction between the pores and the metal structure, these images were used for the image processing and final porosity analysis, for which the software AVIZO (Thermo Fisher Scientific Inc., USA) was used. First, the 2D images were aligned to correct for the shifts which occurred during the serial sectioning. Then, a sub-VOI was cropped out to exclude the reference line. The new 3D VOI was of a size of 3026 x 1164 x 2750 nm³, representing a stack of BSE detector images ranging from slice 30 to 250. Noise interference was minimized by applying a Gaussian filter. Afterwards, thresholding was applied as a segmentation technique to differentiate between pores and the solid gold as well as erosion as morphological operation. As a result, a reconstructed 3D model of the pores located in the solid gold was obtained, as shown in Figure 2. a). Using this 3D pore model, the number of pores and their diameters within the VOI could be determined. For the calculation of the pore diameters, each pore was considered to be of a spherical shape. A total of 1509 pores was counted. The pore diameter distribution is shown in the box plot in Figure 2. b). As it can be obtained from Figure 2. b), a pore size of 23 nm represents the lower quartile, while a pore size of 112 nm represents the upper quartile. The median pore size is 44 nm, while the mean is 63 nm, which indicates a trend towards smaller pores surrounded by larger pores. Based on the obtained results, FIB-SEM tomography with subsequent image processing is assessed by the authors to be a proper method to characterize the porosity of inkjet printed conductive layers, which was tested by means of a nanoparticle gold ink.Item Open Access Ride Comfort Transfer Function for the MAGLEV Vehicle Transrapid(2018) Zheng, Qinghua; Dignath, Florian; Eberhard, Peter; Schmid, PatrickIn order to predict the ride comfort for the MAGLEV vehicle Transrapid TR09 for various scenarios, e.g. for higher vehicle speeds than hitherto travelled, a transfer function from the excitations given by the guideway position to the relevant car body acceleration is calculated by two different methods. Method A is based on a mechatronic simulation model of the Transrapid TR09 which describes a two- dimensional lateral cross section of the vehicle. The simulation model consists of a 2D multibody system describing the mechanical part, four network models of the electro-magnets - two levitation magnets and two guidance magnets - and a signal model of each magnet controller. These signal models contain a representation of the authentic C-Code of the control law used within the actual magnet control units within the vehicle TR09. The overall model can be exploited to calculate the accelerations of the car body for given excitations at the interfaces between guideway and vehicle. Moreover, it is possible to generate a model-based transfer function in the frequency domain from the guideway excitations to the car body accelerations. For method B, measurement results of test runs of the Transrapid TR09 at the test track TVE in Northern Germany are exploited which were recorded for vehicle dynamics analysis and ride comfort evaluation in 2009. From these measurement results two characteristic quantities are generated for several different velocities of the vehicle: Firstly, the position of the guideway is reconstructed by using an integration of the absolute accelerations of the magnets and the signals of the magnet's sensors for the air gap. Secondly, the relation between the accelerations at the car body of the vehicle and the guideway position is calculated as a transfer function in the frequency domain. For this, the measurement data and the reconstructed guideway position are both transformed into the frequency domain by a Fast Fourier Transformation (FFT). The resulting transfer function gives the relevant accelerations for the ride comfort for given excitations of the vehicle as calculated by Method A above. The two transfer functions from Method A and B are compared for validation. Then, a smoothed version of the validated transfer function is applied for estimating the ride comfort for travelling scenarios which have not yet been measured in practical operation, e.g. for higher velocities of the vehicle.Item Open Access Systematic method for axiomatic robustness-testing (SMART)(2014) Kemmler, Stefan; Bertsche, BerndSMART (Systematic Method for Axiomatic Robustness-Testing) is a method for the development of robust and reliable products. It combines elements from the robust design methodology with a holistic approach by using Axiomatic Design (AD) and the Taguchi Method (TM). These two methods were established and expanded by N.P. Suh [1990] (AD) and G. Taguchi [1949] (TM). SMART is based on the chronological sequence of the four phases of the Product Development Process (planning, conception, design and development) according to the VDI Guideline 2221. Using this chronological basis, the three process steps (System, Parameter and Tolerance Design) of the Taguchi Method are classified and integrated accordingly. The AD method is applied to the systematic examination of the robustness of designs. During the conceptual stage, one or more designs are generated by means of AD. AD also helps analyze the design’s complexity from the perspective of possible design modifications, thus assuring robust solutions. If a design has already been generated but needs improvement as things developed, AD is used as well. The design may not necessarily be changed in its basic structure but is examined in terms of its complexity. The results of AD support the setup of the P-Diagram according to Taguchi either after the conceptual stage or the design stage of the product. The following step is the Design of Experiments (DoE) of the product’s design parameters and noise factors that occur during its utilization. Testing may either be carried out by virtual or real tests. After analyzing the results of the tests, the design should be optimized accordingly in order to increase the robustness. A predicted reliability determination is possible as well. The last step is the adjustment of the tolerances of the design for cost optimization purposes. After a final robust design has been established, the actual durability and reliability of the design can be determined on the basis of reliability testing using Design for Reliability (DFR) methods. Basically, SMART can be used both in the initial stages as well as in the more developed stages of the development process.Item Open Access Method for the development of a functional adaptive simulation model for designing robust products(2014) Kemmler, Stefan; Dazer, Martin; Leopold, Tobias; Bertsche, BerndProducts have to ensure their function under the inuence of internal and external noise factors in order to remain competitive in the current market. Therefore the step of designing robust products should be integrated in early stages of the Product Development Process (PDP). Robust products are developed using the Robust Design Method SMART (Systematic Method for Axiomatic Robustness-Testing). Thus far, SMART was applied and veri ed based on a simple mechanical machine element. In this paper, the method will be applied to a complex technical system. Additionally, the confict of aiming between the high e orts and the level of detail in the creation of a simulation model are discussed. This confict is brought about owing to the complex functionality of the design. In order to solve the conict, an approach is given for the creation of an adjusted simulation model. Short simulation times are an advantage for the analysis of parameters regarding robustness. The adaptive simulation model discussed in this paper is based on a exible and equation-based model, which is extended with local -structural-mechanical SUB-models for a more detailed analysis. This approach o ers the option of obtaining rst insights about the functionality of the product and the opportunity to complement the simulation model iteratively for the following design phases. This approach complements SMART on the one hand in the simulative design of robust design parameters and, on the other hand, in their reliability prediction in both the Parameter Design and Tolerance Design phase.Item Open Access Combining knowledge and information - graph-based description of driving scenarios to enable holistic vehicle safety(2023) Bechler, Florian; Fehr, Jörg; Neininger, Fabian; Knöß, Stefan; Grotz, BernhardCurrently, vehicle safety is based on knowledge from injury values, crash pulses, and driving kinematics which leads to intervention strategies separated into isolated domains of active and passive safety. In this contribution, it is shown how vehicle safety can be approached holistically, allowing for human-centered and scenario-based safety decision-making. For this purpose, information from interior and exterior vehicle sensors can be linked by a mathematical framework, combining the knowledge that is already available in the individual domains. A universal graph representation for driving scenarios is developed to master the complexity of driving scenarios and allow for an optimized and scenario-based intervention strategy to minimize occupant injury values. This novel approach allows for the inclusion of sub-models, expert knowledge, results from previous simulations, and annotated databases. The resulting graph can be expanded dynamically for other objects or occupants to reflect all available information to be considered in case of urgency. As input, interior and exterior vehicle sensor data is used. Further information about the driving situation is subsequently derived from this input and the interaction between those states is described by the graph dynamically. For example, occupant attentiveness is derived from measurable eye gaze and eyelid position. From this quantity, reaction time can be estimated in turn. Combined with exterior information, it is possible to decide on the intervention strategy like e.g. alerting the driver. Physical or data-based functional dependencies can be used to represent such interactions. The uncertainties of the inputs and from the surrogate models are included in the graph to ensure a reliable decision-making process. An example of the decision-making process, by modeling the states and actuators as partially observable Markov decision process (POMDP), shows how to optimize the airbag efficiency by influencing the head position prior to an impact. This approach can be extended by additional parameters like driving environment, occupant occupancy, and seating positions in further iterations to optimize the intervention strategy for occupants. The proposed framework integrates scenario-based driving dynamics and existing knowledge from so far separated safety systems with individual activation logic and trigger points to enable holistic vehicle safety intervention strategies for the first time. It lays the foundation to consider new safety hardware, sensor information, and safety functions through a modular, and holistic approach.