Autonomous Visual Detection of Defects from Battery Electrode Manufacturing

The increasing global demand for high-quality and low-cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long-term reduction of reject rates, shortening of the production ramp-up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You-Only-Look-Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes

Fabrication, characterization of high-entropy alloys and deep learning-based inspection in metal additive manufacturing

Alloying has been used to confer desirable properties to materials. It typically involves the addition of small amounts of secondary elements to a primary element. In the past decade, however, a new alloying strategy that involves the combination of multiple principal elements in high concentrations to create new materials called high- entropy alloys (HEAs) has been in vogue. In the first part, the investigation focused on the fabrication process and property assessment of the additive manufactured HEA to broaden its engineering applications. Additive manufacturing (AM) is based on manufacturing philosophy through the layer-by-layer method and accomplish the near net-shaped components fabrication. Attempt was made to coat AlCoCrFeNi HEA on an AISI 304 stainless steel substrate to integrate their properties, however, it failed due to the cracks at the interface. The implementation of an intermediate layer improved the bond and eliminated the cracks. Next, an AlCoCrFeNiTi0.5 HEA coating was fabricated on the Ti6Al4V substrate, and its isothermal oxidation behavior was studied. The HEA coating effectively improved the Ti6Al4V substrate\u27s oxidation resistance due to the formation of continuous protective oxides. In the second part, research efforts were made on the deep learning-based quality inspection of additive manufactured products. The traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. A neural-network approach was developed toward robust real-world AM anomaly detection. The results indicate the promising application of the neural network in the AM industry --Abstract, page iv

Engineering Principles

Over the last decade, there has been substantial development of welding technologies for joining advanced alloys and composites demanded by the evolving global manufacturing sector. The evolution of these welding technologies has been substantial and finds numerous applications in engineering industries. It is driven by our desire to reverse the impact of climate change and fuel consumption in several vital sectors. This book reviews the most recent developments in welding. It is organized into three sections: “Principles of Welding and Joining Technology,” “Microstructural Evolution and Residual Stress,” and “Applications of Welding and Joining.” Chapters address such topics as stresses in welding, tribology, thin-film metallurgical manufacturing processes, and mechanical manufacturing processes, as well as recent advances in welding and novel applications of these technologies for joining different materials such as titanium, aluminum, and magnesium alloys, ceramics, and plastics

Laser Surface Treatment and Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3D Printer and the Application of Machine Learning in Materials Science

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 evaluated the effects of laser beam irradiation-based surface modifications of Ti-10Mo alloy samples under either Ar or N2 environment to the corrosion resistance and cell integration properties. The customized 3D printer was used to conduct the laser surface treatment. The electrochemical behaviors of the Ti-10Mo samples were evaluated in simulated body fluid maintained at 37 ± 0.5 ̊C, and a cell-material interaction test was conducted using the MLO-Y4 cells. Laser surface modification in the Ar environment was found to enhance corrosion behavior but did not affect the surface roughness, element distribution, or cell behavior, compared to the non-laser scanned samples. Processing the Ti-10Mo alloy in N2 formed a much rougher TiN surface that improved both the corrosion resistance and cell-material integration compared with the other two conditions. The mechanical behavior of spark plasma sintering (SPS) treated SLM Inconel 939 samples was evaluated in Chapter 4. Flake-like precipitates (η and σ phases) are observed on the 800-SPS sample surface which increased the hardness and tensile strength compared with the as-fabricated samples. However, the strain-to-failure value decreased due to the local stress concentration. γ’/ γ’’ phases were formed on the 1200-SPS sample. Although not fully formed due to the short holding time, the 1200-SPS sample still showed the highest hardness value and best tensile strength and deductibility. Apply machine learning to the materials science field was discussed in the fifth chapter. Firstly, a simple (Deep Neural Network) DNN model is created to predict the Anti-phase Boundary Energy (APBE) based on the limited training data. It achieves the best performance compared with Random Forest Regressor model and K Neighbors Regressor model. Secondly, the defects classification, the defects detection, and the defects image segmentation are successfully performed using a simple CNN model, YOLOv4 and Detectron2, respectively. Furthermore, defects detection is successfully applied on video by using a sequence of CT scan images. It demonstrates that Machine Learning (ML) can enable more efficient and economical materials science research

IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency

딥러닝 기반 와류기인 선박 프로펠러 진동 탐지 기술

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부(멀티스케일 기계설계전공), 2022. 8. 김윤영.국제해사기구(IMO)의 탄소 배출량 저감 규제 등의 규제에 따라 조선 해운업계는 선박의 초대형화와 에너지 저감장치(ESD) 등 친환경 장치 적용으로 대응하고 있다. 이에 따라 선박의 프로펠러, 러더, ESD 등 수중 구조물의 설계 변화가 요구되고 있다. 새로운 설계 요구조건에 맞춰 주요 제원이 결정되며 전산유체해석 및 수조시험을 통한 성능설계, 진동해석 및 구조강도해석을 통한 구조설계가 진행된다. 수중구조물 제작 이후에는 품질검사를 거쳐 시운전 중에 성능과 진동평가를 마치면 선박이 인도된다. 친환경 장치가 설치된 대형 상선의 선미 구조물은 형상이 복잡하여 유동 및 진동특성의 설계 민감도가 크고 생산 공차에 따른 피로수명의 산포가 크기 때문에 초기 설계단계에서 모든 품질문제를 걸러 내기 어려운 문제가 있다. 특히 유동장에 있는 수중구조물의 경우 특정 유속에서 와류 이탈이 발생하게 되며 와류 이탈 주파수가 구조물의 고유진동수가 일치하는 경우 공진에 인한 와류기인진동(Vortex Induced Vibration; VIV) 문제가 종종 발생되어 수중구조물 피로손상의 원인이 되고 있다. VIV 문제가 있는 상태로 선박이 인도될 경우 설계수명을 만족하지 못하고 단기간에 파손이 되는 경우가 많아 조선소에 큰 피해를 주기 때문에 선박 인도 직전인 선박 시운전 단계에서 진동이나 응력 계측을 통해 VIV 발생 여부의 확인이 필요하다. 구조물에 작용하는 하중을 계측하는 전통적인 방법은 구조물에 스트레인게이지를 설치하고 수중 텔레미터리를 설치하여 구조물의 스트레인을 직접 계측하는 방법이지만 계측을 위해 많은 비용이 소요되고 계측 실패의 가능성이 매우 높다는 문제가 있다. 본 논문에서는 대형 상선 프로펠러의 대표적인 손상 원인인 Vortex Induced Vibration을 시운전 단계에서 선체 진동 계측을 통해 간접적으로 검출할 수 있는 방법을 제안하였다. 특정 VIV가 문제가 되는 경우는 유속에서 와류 이탈 주파수가 구조물의 고유진동수가 일치하는 경우 공진에 의해 와류이탈 강도가 증가하고 유속이 증가하더라도 와류이탈 주파수가 유지되는 Lock-in 현상이 발생하는 경우로 간접 계측을 통해 이를 명시적으로 확인할 수 있다. 이를 위해서는 진동 전문가의 반복적인 진동 계측 및 평가 프로세스가 필요한데 본 연구에서는 전문가를 대신한 딥러닝 알고리즘을 이용한 VIV 탐지 시스템을 제안하였다. 진동 분석과 VIV 검출 자동화를 위해 이미지 기반의 Object detection을 위해 널리 이용되고 있는 CNN(Convolution Neural Network) 알고리즘을 이용하였다. 본 연구에서는 Object detection을 수행하되 Classification은 수행하지 않아도 되는 특징이 있어 이에 특화된 CNN 모델 개발을 위해 Hyper parameter를 조정하여 Hidden Layer를 증가하는 방법으로 30개의 CNN모델을 검토하였고 최종적으로 과적합이 없이 탐지 성능이 높은 5개의 Hidden layer 가진 모델을 제안하였다. CNN 학습을 위해 필요한 대규모의 데이터 생성을 위해 진동 모드 중첩법 기반의 간이 선박 모델을 제안하였고 프로펠러 기진력을 모사하였다. 간이 모델을 이용하여 실제 진동계측 결과와 유사한 진동 특성을 보이는 10,000개의 데이터를 생성하여 학습에 이용하였고 1,000개의 데이터를 이용하여 테스트한 결과 82%이상의 탐지 성공률을 보였다. 제안된 탐지시스템의 검증을 위해 축소모델 시험을 수행하였다. 프로펠러에서 Vortex shedding 주파수와 블레이드의 수중 고유진동수가 일치하도록 설계된 1/10 스케일의 선박 추진 시스템 축소 모델을 이용하여 프로펠러에서 Vortex Induced Vibration을 발생시키고 프로펠러 주변 구조물에서 가속도계를 이용하여 Lock-in 현상에 의한 진동을 측정하였다. 이 신호를 이용하여 개발된 시스템으로 VIV의 검출이 가능함을 보였다. 마지막으로 VIV문제가 발생했던 원유운반선의 시운전 중 기관실 내에서 계측된 선체 구조 진동값을 이용하여 개발된 탐지 시스템의 타당성을 검증하고 실제 선박에서의 적용 가능성도 확인하였다. 개발된 시스템은 VIV 검출은 위한 자동화 시스템으로 활용이 가능할 것으로 보이며 향후 실선 데이터가 확보될 경우 유용성이 증가할 것으로 기대된다Due to the International Maritime Organization’s (IMO) regulations on carbon emission reduction, the shipbuilding and shipping industry increases the size of ships and adopts energy-saving devices (ESD) on ships. Accordingly, design changes of underwater structures such as propellers, rudders, and ESD of ships are required in line with these trends. The lock-in phenomenon caused by vortex-induced vibration (VIV) is a potential cause of vibration fatigue and singing of the propellers of large merchant ships. The VIV occurs when the vibration frequency of a structure immersed in a fluid is locked in its resonance frequencies within a flow speed range. Here, a deep learning-based algorithm is proposed for early detection of the VIV phenomenon. A salient feature in this approach is that the vibrations of a hull structure are used instead of the vibrations of its propeller, implying that indirect hull structure data relatively easy to acquire are utilized. The RPM-frequency representations of the measured vibration signals, which stack the vibration frequency spectrum respective to the propeller RPMs, are used in the algorithm. The resulting waterfall charts, which look like two-dimensional image data, are fed into the proposed convolutional neural network architecture. To generate a large data set needed for the network training, we propose to synthetically produce vibration data using the modal superposition method without computationally-expensive fluid-structure interaction analysis. This way, we generated 100,000 data sets for training, 1,000 sets for hyper-parameter tuning, and 1,000 data sets for the test. The trained network was found to have a success rate of 82% for the test set. We collected vibration data in our laboratory's small-scale ship propulsion system to test the proposed VIV detection algorithm in a more realistic environment. The system was so designed that the vortex shedding frequency and the underwater natural frequency match each other. The proposed VIV detection algorithm was applied to the vibration data collected from the small-scale system. The system was operated in the air and found to be sufficiently reliable. Finally, the proposed algorithm applied to the collected vibration data from the hull structure of a commercial full-scale crude oil carrier in her sea trial operation detected the propeller singing phenomenon correctly.CHAPTER 1. INTRODUCTION 1 1.1 Motivation 1 1.2 Research objectives 8 1.3 Outline of thesis 9 CHAPTER 2. PROPELLER VORTEX-INDUCED VIBRATION MEASUREMNT METHOD 24 2.1 Structural vibration measurement methods 24 2.2 Direct measuremt method for propeller vibration 26 2.3 Indirect measuremt method for propeller vibration 28 CHAPTER 3. DEEP LEARNING NETWORK FOR VIV IDENTIFICATION 39 3.1 Convolution Neural Network 39 3.2 Data generation using mode superposition 46 3.3 Structure of the proposed CNN model 50 3.4 Deep neural networks 53 3.5 Training and diagnosis steps 55 3.6 Performance of the diagnositc model 56 CHAPTER 4. EXPERIMETS AND RESULTS 76 4.1 Experimental apparatus and data collection 76 4.2 Results and discussion 78 CHAPTER 5. ENHANCEMENT OF DETECTION PERFORMANCE USING MULTI-CHANNEL APPROACH 98 CHAPTER 6. VORTEX-INDUCED VIBRATIOIN IDENTIFICATION IN THE PROPELLER OF A CRUDE OIL CARRIER 105 CHAPTER 7. CONCLUSION 114 REFERENCES 118 ABSTRACT(KOREAN) 127박

Ultrafast Microfluidic Immunoassays Towards Real-time Intervention of Cytokine Storms

Biomarker-guided precision medicine holds great promise to provide personalized therapy with a good understanding of the molecular or cellular data of an individual patient. However, implementing this approach in critical care uniquely faces enormous challenges as it requires obtaining “real-time” data with high sensitivity, reliability, and multiplex capacity near the patient’s bedside in the quickly evolving illness. Current immunodiagnostic platforms generally compromise assay sensitivity and specificity for speed or face significantly increased complexity and cost for highly multiplexed detection with low sample volume. This thesis introduces two novel ultrafast immunoassay platforms: one is a machine learning-based digital molecular counting assay, and the other is a label-free nano-plasmonic sensor integrated with an electrokinetic mixer. Both of them incorporate microfluidic approaches to pave the way for near-real-time interventions of cytokine storms. In the first part of the thesis, we present an innovative concept and the theoretical study that enables ultrafast measurement of multiple protein biomarkers (<1 min assay incubation) with comparable sensitivity to the gold standard ELISA method. The approach, which we term “pre-equilibrium digital enzyme-linked immunosorbent assay” (PEdELISA) incorporates the single-molecular counting of proteins at the early, pre-equilibrium state to achieve the combination of high speed and sensitivity. We experimentally demonstrated the assay’s application in near-real-time monitoring of patients receiving chimeric antigen receptor (CAR) T-cell therapy and for longitudinal serum cytokine measurements in a mouse sepsis model. In the second part, we report the further development of a machine learning-based PEdELISA microarray data analysis approach with a significantly extended multiplex capacity using the spatial-spectral microfluidic encoding technique. This unique approach, together with a convolutional neural network-based image analysis algorithm, remarkably reduced errors faced by the highly multiplexed digital immunoassay at low analyte concentrations. As a result, we demonstrated the longitudinal data collection of 14 serum cytokines in human patients receiving CAR-T cell therapy at concentrations < 10pg/mL with a sample volume < 10 µL and 5-min assay incubation. In the third part, we demonstrate the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a compact fluorescence optical scanner for the potential near-bedside readout. The automated system has achieved high interassay precision (~10% CV) with high sensitivity (<0.4pg/mL). Our data revealed large subject-to-subject variability in patient responses to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays. Lastly, an AC electroosmosis-enhanced localized surface plasmon resonance (ACE-LSPR) biosensing device was presented for rapid analysis of cytokine IL-1β among sepsis patients. The ACE-LSPR device is constructed using both bottom-up and top-down sensor fabrication methods, allowing the seamless integration of antibody-conjugated gold nanorod (AuNR) biosensor arrays with microelectrodes on the same microfluidic platform. Applying an AC voltage to microelectrodes while scanning the scattering light intensity variation of the AuNR biosensors results in significantly enhanced biosensing performance. The technologies developed have enabled new capabilities with broad application to advance precision medicine of life-threatening acute illnesses in critical care, which potentially will allow the clinical team to make individualized treatment decisions based on a set of time-resolved biomarker signatures.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163129/1/yujing_1.pd

Cold Micro Metal Forming

This open access book contains the research report of the Collaborative Research Center “Micro Cold Forming” (SFB 747) of the University of Bremen, Germany. The topical research focus lies on new methods and processes for a mastered mass production of micro parts which are smaller than 1mm (by forming in batch size higher than one million). The target audience primarily comprises research experts and practitioners in production engineering, but the book may also be of interest to graduate students alike

Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

Today the topic of incremental sheet forming (ISF) is one of the most active areas of sheet metal forming research. ISF can be an essential alternative to conventional sheet forming for prototypes or non-mass products. Single point incremental forming (SPIF) is one of the most innovative and widely used fields in ISF with the potential to form sheet products. The formed components by SPIF lack geometric accuracy, which is one of the obstacles that prevents SPIF from being adopted as a sheet forming process in the industry. Pillow effect and wall displacement are influential contributors to manufacturing defects. Thus, optimal process parameters should be selected to produce a SPIF component with sufficient quality and without defects. In this context, this study presents an insight into the effects of the different materials and shapes of forming tools, tool head diameters, tool corner radiuses, and tool surface roughness (Ra and Rz). The studied factors include the pillow effect and wall diameter of SPIF components of AlMn1Mg1 aluminum alloy blank sheets. In order to produce a well-established study of process parameters, in the scope of this paper different modeling tools were used to predict the outcomes of the process. For that purpose, actual data collected from 108 experimentally formed parts under different process conditions of SPIF were used. Neuron by Neuron (NBN), Gradient Boosting Regression (GBR), CatBoost, and two different structures of Multilayer Perceptron were used and analyzed for studying the effect of parameters on the factors under scrutiny. Different validation metrics were adopted to determine the quality of each model and to predict the impact of the pillow effect and wall diameter. For the calculation of the pillow effect and wall diameter, two equations were developed based on the research parameters. As opposed to the experimental approach, analytical equations help researchers to estimate results values relatively speedily and in a feasible way. Different partitioning weight methods have been used to determine the relative importance (RI) and individual feature importance of SPIF parameters for the expected pillow effect and wall diameter. A close relationship has been identified to exist between the actual and predicted results. For the first time in the field of incremental forming study, through the construction of Catboost models, SHapley Additive exPlanations (SHAP) was used to ascertain the impact of individual parameters on pillow effect and wall diameter predictions. CatBoost was able to predict the wall diameter with R 2 values between the range of 0.9714 and 0.8947 in the case of the training and testing dataset, and between the range of 0.6062 and 0.6406 when predicting pillow effect. It was discovered that, depending on different validation metrics, the Levenberg–Marquardt training algorithm performed the most effectively in predicting the wall diameter and pillow effect with R 2 values in the range of 0.9645 and 0.9082 for wall diameter and in the range of 0.7506 and 0.7129 in the case of the pillow effect. NBN has no results worthy of mentioning, and GBR yields good prediction only of the wall diameter