Simulation of ultrasonic lamb wave generation, propagation and detection for an air coupled robotic scanner

A computer simulator, to facilitate the design and assessment of a reconfigurable, air-coupled ultrasonic scanner is described and evaluated. The specific scanning system comprises a team of remote sensing agents, in the form of miniature robotic platforms that can reposition non-contact Lamb wave transducers over a plate type of structure, for the purpose of non-destructive evaluation (NDE). The overall objective is to implement reconfigurable array scanning, where transmission and reception are facilitated by different sensing agents which can be organised in a variety of pulse-echo and pitch-catch configurations, with guided waves used to generate data in the form of 2-D and 3-D images. The ability to reconfigure the scanner adaptively requires an understanding of the ultrasonic wave generation, its propagation and interaction with potential defects and boundaries. Transducer behaviour has been simulated using a linear systems approximation, with wave propagation in the structure modelled using the local interaction simulation approach (LISA). Integration of the linear systems and LISA approaches are validated for use in Lamb wave scanning by comparison with both analytic techniques and more computationally intensive commercial finite element/difference codes. Starting with fundamental dispersion data, the paper goes on to describe the simulation of wave propagation and the subsequent interaction with artificial defects and plate boundaries, before presenting a theoretical image obtained from a team of sensing agents based on the current generation of sensors and instrumentation

Integrated Strategy toward Self-Powering and Selectivity Tuning of Semiconductor Gas Sensors

Inorganic conductometric gas sensors struggle to overcome limitations in high power consumption and poor selectivi-ty. Herein, recent advances in developing self-powered gas sensors with tunable selectivity are introduced. Alternative general approaches for powering gas sensors were realized via proper integration of complementary functionalities (namely; powering and sensing) in a singular heterostructure. These solar light driven gas sensors operating at room temperature without applying any additional external powering sources are comparatively discussed. The TYPE-1 gas sensor based on integration of pure inorganic interfaces (e.g. CdS/n-ZnO/p-Si) is capable of delivering a self-sustained sensing response, while it shows a non-selective interaction towards oxidizing and reducing gases. The structural and the optical merits of TYPE-1 sensor are investigated giving more insights into the role of light activation on the modu-lation of the self-powered sensing response. In the TYPE-2 sensor, the selectivity of inorganic materials is tailored through surface functionalization with self-assembled organic monolayers (SAMs). Such hybrid interfaces (e.g. SAMs/ZnO/p-Si) have specific surface interactions with target gases compared to the non-specific oxidation-reduction interactions governing the sensing mechanism of simple inorganic sensors. The theoretical modeling using density functional theory (DFT) has been used to simulate the sensing behavior of inorganic/organic/gas interfaces, revealing that the alignment of organic/gas frontier molecular orbitals with respect to the inorganic Fermi level is the key factor for tuning selectivity. These platforms open new avenues for developing advanced energy-neutral gas sensing devices and concepts

Improving precision of material extrusion 3D printing by in-situ monitoring and predicting 3D geometric deviation using Conditional Adversarial Networks

The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the geometric deviation of the predicted profiles by making compensation to the CAD model

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

Sensing and sensitivity:Computational chemistry of graphene‐based sensors

Highly efficient, tunable, biocompatible, and environmentally friendly electrochemical sensors featuring graphene-based materials pose a formidable challenge for computational chemistry. In silico rationalization, optimization and, ultimately, prediction of their performance requires exploring a vast structural space of potential surface-analyte complexes, further complicated by the presence of various defects and functionalities within the infinite graphene lattice. This immense number of systems and their periodic nature greatly limit the choice of computational tools applicable at a reasonable cost. An alternative approach using finite nanoflake models opens the doors to many more advanced and accurate electronic structure methods, while sacrificing the realism of representation. Locating the surface-analyte complex is followed by an in-depth in silico analysis of its energetic and electronic properties using, for example, energy decomposition schemes, as well as simulation of the signal, for example, a zero-bias transmission spectra or a current–voltage curve, by means of the nonequilibrium Green's function method. These and other properties are examined in the context of a sensor's selectivity, sensitivity, and limit of detection with an aim to establish design principles for future devices. Herein, we analyze the advantages and limitations of diverse computational chemistry methods used at each of these steps in simulating graphene-based electrochemical sensors. We present outstanding challenges toward predictive models and sketch possible solutions involving such contemporary techniques as multiscale simulations and high-throughput screening

Charakterisierung funktionaler Nanomaterialien für biomagnetische Sensoren und Atemanalyse

The presented thesis is covering materials aspects for the development of magnetoelectric sensors for biomagnetic sensing and solid state sensors for breath monitoring. The electrophysiological signals of the human body and especially their irregularities provide extremely valuable information about the heart, brain or nerve malfunction in medical diagnostics. Similar and even more detailed information is contained in the generated biomagnetic fields which measurement offers improved diagnostics and treatment of the patients. A new type of room temperature operable magnetoelectric composite sensors is developed in the framework of the CRC1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics. This thesis focuses on the individual materials structure-property relations and their combination in magnetoelectric composite sensors studied by electron beam based techniques, at lengths scales ranging from micrometers to atomic resolution. The first part of this thesis highlights selected studies on the structural and analytic aspects of single phase materials and their composites using TEM as the primary method of investigation. With respect to the piezoelectric phase, alternatives to AlN have been thoroughly investigated to seek for improvement of specific sensor approaches. In this context, the alloying of Sc into the AlN matrix has been demonstrated to yield high quality films with improved piezoelectric and unprecedented ferroelectric properties grown under the control of deposition parameters. Lead-free titanate films with large piezo-coefficients at the verge of the morphotropic phase boundary as alternative to PZT films have been investigated in terms of crystal symmetry, defect structure and domains of cation ordering. New morphologies of ZnO and GaN semiconductors envisioned for a piezotronic-based sensor approach were subject of in-depth defect and analytical studies describing intrinsic defects and lattice strains upon deposition as well as hollow composite structures. When the dimensions of a materials are reduced, novel exciting properties such as in-plane piezoelectricity can arise in planar transition-metal dichalcogenides. Here, the turbostratic disorder in a few-layered MoSe2 film has been investigated by nanobeam electron diffraction and Fast Fourier Transformations. From the perspective of magnetic materials, the atomic structure of magnetostrictive multilayers of FeCo/TiN showing stability up to elevated temperatures has been analyzed in detail regarding the crystallographic relationship of heteroepitaxy in multilayer composites exhibiting individual layer thicknesses below 1 nm. Further, magnetic hard layers have been investigated in the context of exchange spring concepts and ME composites based on shape memory alloy substrates have been studied regarding structural changes implied by different annealing processes. The second part of this thesis introduces materials aspects and sensor studies on gas detection in the clinical context of breath analysis. The detection of specific vapors in the human breath is of medical relevance, since certain species can be enriched depending on the conditions and processes within the human body. Hence, they can be regarded as biomarkers for the patients condition of health. The selection of suitable materials and the gas measurement working principle are considered and selected studies on solid state sensors with different surface functionalization or targeted application on basis of ZnO or CuO-oxide and Fe-oxide species are presented