@phdthesis{Spiess2023, author = {Spieß, Benjamin}, title = {The artificial engineer : a smart holistic framework for the automated transfer of geometry to analysis models}, doi = {10.26127/BTUOpen-6530}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-65308}, school = {BTU Cottbus - Senftenberg}, year = {2023}, abstract = {The creation of adequate simulation models for complex assemblies is an extensive process that requires a lot of experience, and on the other hand involves a multitude of manual, tedious tasks. These are significant obstacles for improving the process performance and capabilities. The objective of this research is to develop methods which digitally imitate the way of thoughts of the engineer in the design process towards a digital system understanding and which support the automation of the involved manual workflow. This thesis presents a strategy to translate engineering reasoning and actions to an equivalent in the computer domain. A cardinal step is to gain understanding of system arrangements, boundary conditions and its components. Based on this evaluation, the identification of assembly parts is forming the foundation for optimized process chains for the transfer to the analysis environment. Model complexity relates to computational effort, which in turn affects model capabilities and manageability. To achieve a satisfactory compromise of model quality and complexity, this transfer process is strongly dependent on the visual analysis, reasoning and manual implementation of skilled engineers. The principle of translating engineering logics is pursued from the assembly system to its smallest parts. Component segmentation methods allow subdividing regions of interest into substructures which are assigned with a feature vector. This vector comprises metrics describing the substructures with regard to specific aspects and is the key decision point for subsequent steps as idealization, suitable Finite-Element modeling and ultimately building an analysis model. The created system database is continuously maintained and supports these process chains as well as the final setup of the assembly simulation model. An automated workflow like this implies advantages for efficiency, but also creates opportunities for further use cases. This workflow has been exploited for generating a training data set from the different simulation variants as a basis to a knowledge representation imitating engineering experience. An algorithm from the graph neural network field is applied to this data set as a conceptual approach. The intention pursued in this concept is to model the learning progress about estimating the influence of modelling decisions on simulation results and quality. This research proposes a holistic strategy and describes methods to achieve the objectives of decreasing manual effort, introducing an automated and geometry-based process and digitally replicating engineering experience by introducing a knowledge database.}, subject = {Engineering; Automation; Artificial intelligence; Design; Machine learning; Automatisierung; Simulation; K{\"u}nstliche Intelligenz; CAD; FEM; Automatisierung; CAD; Finite-Elemente-Methode; K{\"u}nstliche Intelligenz; Simulation}, language = {en} } @phdthesis{UrbanoCaguasango2022, author = {Urbano Caguasango, Jos{\´e} Mauricio}, title = {Learning-based manufacturing deviation estimation for robust computer-aided design}, doi = {10.26127/BTUOpen-6154}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-61544}, school = {BTU Cottbus - Senftenberg}, year = {2022}, abstract = {Surface variations are an unavoidable byproduct of any manufacturing process and may lead to deviating part performance and even elevated part rejection rates. Because traditional computer aided-design approaches are aimed towards production of idealized, nominal geometric shapes, the wide geometric-and statistical-variability typical for any manufacturing process remains unrepresented and is frequently ignored during design. Thus, the present work aims at a more realistic design approach and, therefore, develops a collection of computer-aided design strategies for accurate representation, statistical analysis and prospective estimation of surface deviations with validation examples on aero engine turbine blades. The CAD representation of real manufactured surfaces requires the ability to accurately recreate complex geometric shapes. This is achieved by automated re-parametrization of any CAD face of interest as B-spline surface with a rather dense control point grid. Face matching to scanned manufactured samples is then performed by calculating control point displacements, which successfully deliver surface representation errors below typical measurement uncertainties on multiple matching examples from turbine shank and hot-gas faces. Since inference of performance variability due to manufacturing is usually limited by the amount of scanned manufactured parts, a probabilistic model is formulated based on singular-value decomposition of control point displacements and identification of dominant manufacturing modes. This allows generation of an infinite set of synthetic deviating surfaces faithful to experimental deviation patterns. Nominal geometric features may significantly differ between design iterations and manufacturing modes may not necessarily be transferable between different designs. Thus, deviation estimation may remain infeasible before manufacturing. To enable deviation estimation during the design phase, the present work proposes a machine learning strategy to identify deviation patterns explained by nominal geometric properties-such as relative position and local orientation-and use them for deviation estimation on new designs. This strategy is able to predict realistic stress variability induced by shank deviations of a turbine blade design using only surface deviation information from three given designs, which encourages machine-learning approaches as valuable tool for geometric deviation estimation as part of robust design.}, subject = {Machine learning; Computer-adided design; Robust design; Manufacturing deviation; Turbine blade; Maschinelles Lernen; CAD; Robuster Entwurf; Fertigungsabweichungen; Turbinenschaufel; Turbinenschaufel; CAD; Maschinelles Lernen; Fertigung; Abweichung}, language = {en} } @phdthesis{Apte2020, author = {Apte, Anisha}, title = {A new analytical design method of ultra-low-noise voltage controlled VHF crystal oscillators and it's validation}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-51386}, school = {BTU Cottbus - Senftenberg}, year = {2020}, abstract = {The design of high Q oscillators, using Crystals at lower frequencies, (and dielectric resonators at much higher frequencies), has long been considered a black art. This may be due to the fact that a systematic approach with optimized design guideline for crystal oscillators could not be found after extensive literature search. In this dissertation, after analyzing the first crystal oscillator by W.G. Cady (1921), other high performance crystal oscillators will be discussed, analyzed and calculated. A single transistor crystal oscillator design as used by HP (Hewlett Packard) in one of their designs, the HP10811A is considered in this thesis for mathematical analysis and CAD (Computer aided Design) simulation. This was also measured on state-of-the-art signal source analyzer. After validation, this design is scaled to 100MHz, the frequency of interest for this dissertation. Though most designers use a single transistor based oscillator circuit, it is not an optimized design because of limited control over key design parameters such as loop gain, dc current etc. This dissertation is an attempt to overcome the limitation due to the single transistor circuit and to give a step by step procedure, explaining the significance of a two transistor design with thorough analysis and design simulation results. This two stage transistor circuit is also not yet a best solution in terms of phase noise performance and output power, and some add-on circuitry will be needed for an optimized performance. An important contribution of this work is to show that since the voltage gain is the ratio of the collector resistor and the emitter resistor, the performance is practically independent of the VHF transistor and gives better control over various parameters of the oscillator, in order to optimize the design. A grounded-base amplifier is then introduced and added for improving the isolation and the output power. Unlike most oscillators, that take the output from the collector, a novel concept introduced by Rohde [14], is incorporated here, where the crystal is used as a filter that is then connected to the grounded base amplifier, a technique which many companies have been using. This dissertation will show that this technique increases the output power without significantly affecting the phase noise. Such a validation is needed for better understanding and as per my knowledge, has not been done so far. For the oscillator, the tuning diode sensitivity and flicker noise contribution are also taken into consideration, by calibrating the mathematics and its validation is shown. Crystal resonators of the type AT and stress-compensated (SC) cut devices will be considered as they give the best performance. The one port Colpitts type oscillator is considered first and the two port two transistor design later. Both will need a post amplifier/buffer stage. A complete step by step design procedure for an optimized 100MHz crystal oscillator is then presented. For completeness, CAD Simulation and Experimental results are provided for 10 MHz, 128 MHz and 155 MHz VCO circuits.}, subject = {Quartz crystal; Crystal oscillator; Phase noise; Reference source; Voltage controlled; Quarzkristall; Kristalloszillator; Phasenrauschen; Referenzquelle; Spannungsgesteuert; Quarzoszillator; UKW; CAD}, language = {en} }