Zhenyu (James) Kong

Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors

The objective of this work is to identify failure modes and detect the onset of process anomalies in additive manufacturing (AM) processes, specifically focusing on fused filament fabrication (FFF). We accomplish this objective using advanced Bayesian nonparametric analysis of in situ heterogeneous sensor data. Experiments are conducted on a desktop FFF machine instrumented with a heterogeneous sensor array including thermocouples, accelerometers, an infrared (IR) temperature sensor, and a real-time miniature video borescope. FFF process failures are detected online using the nonparametric Bayesian Dirichlet process (DP) mixture model and evidence theory (ET) based on the experimentally acquired sensor data. This sensor data-driven defect detection approach facilitates real-time identification and correction of FFF process drifts with an accuracy and precision approaching 85% (average F-score). In comparison, the F-score from existing approaches, such as probabilistic neural networks (PNN), naive Bayesian clustering, support vector machines (SVM), and quadratic discriminant analysis (QDA), was in the range of 55–75%.

Time series forecasting for nonlinear and non-stationary processes: a review and comparative study

Forecasting the evolution of complex systems is noted as one of the 10 grand challenges of modern science. Time series data from complex systems capture the dynamic behaviors and causalities of the underlying processes and provide a tractable means to predict and monitor system state evolution. However, the nonlinear and non-stationary dynamics of the underlying processes pose a major challenge for accurate forecasting. For most real-world systems, the vector field of state dynamics is a nonlinear function of the state variables; i.e., the relationship connecting intrinsic state variables with their autoregressive terms and exogenous variables is nonlinear. Time series emerging from such complex systems exhibit aperiodic (chaotic) patterns even under steady state. Also, since real-world systems often evolve under transient conditions, the signals obtained therefrom tend to exhibit myriad forms of non-stationarity. Nonetheless, methods reported in the literature focus mostly on forecasting linear and stationary processes. This article presents a review of these advancements in nonlinear and non-stationary time series forecasting models and a comparison of their performances in certain real-world manufacturing and health informatics applications. Conventional approaches do not adequately capture the system evolution (from the standpoint of forecasting accuracy, computational effort, and sensitivity to quantity and quality of a priori information) in these applications.

Criticality index analysis based optimal RFID reader placement models for asset tracking

This study is aimed at optimising the RFID network design in the healthcare service sector for tracking medical assets. Two different optimisation models corresponding to two possible scenarios in RFID network design are developed based on the enhancement of location set covering problem (LSCP) and maximal covering location problem (MCLP). They are validated by considering a healthcare facility to optimise the real-time locating system for tracking assets. The methodology is original in that it analyses the trade-off between cost effectiveness and overall RFID system performance and hence provides possible decision guidance to optimise the RFID system. It is vital for healthcare providers to locate crucial assets in the shortest possible time, particularly in emergency situations where human lives are at risk. Hence, increasing the overall RFID system performance will definitely have a valuable effect on real-time information sharing, thereby decreasing related search time for crucial assets.

Process Performance Prediction for Chemical Mechanical Planarization (CMP) by Integration of Nonlinear Bayesian Analysis and Statistical Modeling

Chemical mechanical planarization (CMP) process has been widely used in the semiconductor manufacturing industry for realizing highly finished (Ra ~ 1 nm) and planar surfaces (WIWNU ~ 1%, thickness standard deviation (SD) ~ 3 nm) of in-process wafer polishing. The CMP process is rather complex with nonlinear and non-Gaussian process dynamics, which brings significant challenges for process monitoring and control. As an attempt to address this issue, a method is presented in this paper that integrates nonlinear Bayesian analysis and statistical modeling to estimate and predict process state variables, and therewith to predict the performance measures, such as material removal rate (MRR), surface finish, surface defects, etc. As an example of performance measure, MRR is chosen to demonstrate the performance prediction. A sequential Monte Carlo (SMC) method, namely, particle filtering (PF) method is utilized for nonlinear Bayesian analysis to predict the CMP process-state and for tackling the process nonlinearity. Vibration signals from both wired and wireless vibration sensors are adopted in the experimental study conducted using the CMP apparatus. The process states captured by the sensor signals are related to MRR using design of experiments and statistical regression analysis. A case study was conducted using actual CMP processing data by comparing the PF method with other widely used prediction approaches. This comparison demonstrates the effectiveness of the proposed approach, especially for nonlinear dynamic processes.

A graph-theoretic approach for quantification of surface morphology variation and its application to chemical mechanical planarization process

We present an algebraic graph-theoretic approach for quantification of surface morphology. Using this approach, heterogeneous, multi-scaled aspects of surfaces; e.g., semiconductor wafers, are tracked from optical micrographs as opposed to reticent profile mapping techniques. Therefore, this approach can facilitate in situ real-time assessment of surface quality. We report two complementary methods for realizing graph-theoretic representation and subsequent quantification of surface morphology variations from optical micrograph images. Experimental investigations with specular finished copper wafers (surface roughness (Sa) ∼ 6 nm) obtained using a semiconductor chemical mechanical planarization process suggest that the graph-based topological invariant Fiedler number (λ2) was able to quantify and track variations in surface morphology more effectively compared to other quantifiers reported in literature.

Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing Using Spectral Graph Theory

The ability of additive manufacturing (AM) processes to produce components with virtually any geometry presents a unique challenge in terms of quantifying the dimensional quality of the part. In this paper, a novel spectral graph theory (SGT) approach is proposed for resolving the following critical quality assurance concern in the AM: how to quantify the relative deviation in dimensional integrity of complex AM components. Here, the SGT approach is demonstrated for classifying the dimensional integrity of standardized test components. The SGT-based topological invariant Fiedler number (λ2) was calculated from 3D point cloud coordinate measurements and used to quantify the dimensional integrity of test components. The Fiedler number was found to differ significantly for parts originating from different AM processes (statistical significance p-value <1%). By comparison, prevalent dimensional integrity assessment techniques, such as traditional statistical quantifiers (e.g., mean and standard deviation) and examination of specific facets/landmarks failed to capture part-to-part variations, proved incapable of ranking the quality of test AM components in a consistent manner. In contrast, the SGT approach was able to consistently rank the quality of the AM components with a high degree of statistical confidence independent of sampling technique used. Consequently, from a practical standpoint, the SGT approach can be a powerful tool for assessing the dimensional integrity of the AM components, and thus encourage wider adoption of the AM capabilities.

Process-Machine Interaction (PMI) Modeling and Monitoring of Chemical Mechanical Planarization (CMP) Process Using Wireless Vibration Sensors

We present a deterministic process-machine interaction (PMI) model that can associate different complex time-frequency patterns, including nonlinear dynamic behaviors that manifest in vibration signals measured during a chemical mechanical planarization (CMP) process for polishing blanket copper wafer surfaces to near-optical finish (Ra ~ 5 nm) to specific process mechanisms. The model captures the effects of the nonuniform structural properties of the polishing pad, pad asperities, and machine kinematics on CMP dynamics using a deterministic 2 ° of freedom nonlinear differential equation. The model was validated using a Buehler (Automet 250) bench top CMP machine instrumented with a wireless (XBee IEEE 802.15.4 RF module) multi-sensor unit that includes a MEMS 3-axis accelerometer (Analog Devices ADXL 335). Extensive experiments suggest that the deterministic PMI model can capture such significant signal patterns as aperiodicity, broadband frequency spectra, and other prominent manifestations of process nonlinearity. Remarkably, the deterministic PMI model was able to explain not just the physical sources of various time-frequency patterns observed in the measured vibration signals, but also, their variations with process conditions. The features extracted from experimental vibration data, such as power spectral density over the 115-120 Hz band, and nonlinear recurrence measures were statistically significant estimators (R2 ~ 75%) of process parameter settings. The model together with sparse experimental data was able to estimate process drifts resulting from pad wear with high fidelity (R2 ~ 85%). The signal features identified using the PMI model can lead to effective real-time in-situ monitoring of wear and anomalies in the CMP process.

An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data

ABSTRACT The objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC). The novelty of the OSEC approach is in representing data from sensor signals as an underdetermined linear system of equations and subsequently solving the underdetermined linear system using a newly developed greedy Bayesian estimation method. We apply the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical–mechanical planarization process. Using the proposed OSEC approach, process drifts are detected and classified with higher accuracy compared with popular machine learning techniques. Process drifts were detected and classified with a fidelity approaching 90% (F-score) using OSEC. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, quadratic discriminant analysis, naïve Bayes—were evaluated with F-score in the range of 40% to 70%.

Nonlinear Sequential Bayesian Analysis-Based Decision Making for End-Point Detection of Chemical Mechanical Planarization (CMP) Processes

Chemical mechanical planarization (CMP) process has been widely used in the semiconductor manufacturing industry for realizing highly polished (surface roughness Ra ~1 nm ) and planar [WIWNU ~ 1%, thickness variation standard deviation ~3 nm] surfaces of an in-process wafer. In CMP, accurate and timely decisions for end-point detection (EPD) are extremely important to enable the process to effectively respond to demand variations and disruptions. In this paper, we apply nonlinear sequential Bayesian analysis and decision theory to establish a quantitative relationship that connects the features (inputs) extracted from on-line wireless vibration sensor signals with the process performance measures, such as material removal (outputs) for EPD in copper CMP process. A case study with actual CMP data is provided to demonstrate the effectiveness of the present approach. Note to practitioners. The semiconductor industry widely uses CMP process for realizing highly polished planar surfaces on inter-level dielectrics and metallic interconnects in the fabrication of integrated circuits. Accurate and timely detection of the end-point (EPD) of the CMP process is critical to prevent over-polishing or under-polishing of wafer surfaces, and thus meet the wafer yield requirements under growing demands on wafer density and performance. An EPD system uses information from in-process sensors and/or inspection instruments to facilitate decisions on when to stop the polishing process, and adjust process settings for optimal performance. However, the issue of developing cost-effective sensors, and addressing the uncertainty in the sensor information remains a challenge. We have developed an EPD system based on deriving and sequentially updating a cost-function using the uncertain information from wireless MEMS vibration sensors mounted on a CMP apparatus. Decisions on EPD are made based on optimizing the updated cost function at every time-step. Our experimental investigations suggest that the sensor information can be effectively used for implementing EPD, and it can minimize the costs of over-polishing and under-polishing of wafers during CMP process. As part of future work, we are investigating the robustness of the EPD system to different forms of uncertainty in the sensor information, and much wider configurations of sensors and CMP setups.

Significant Winter Weather Events and Associated Socioeconomic Impacts (Federal Aid Expenditures) across Oklahoma: 2000–10

AbstractExceptionally severe winter storms that overwhelm local government result in major disaster declarations. Each National Weather Service forecast office in the United States reports winter events for a specific group of counties, known as the county warning area. Such events are reported as blizzard, ice storm, winter storm, heavy snow, or winter weather. They are archived by the National Climatic Data Center and are published in Storm Data, a monthly periodical. Using Storm Data, all winter reports in Oklahoma from 1 November 1999 to 1 May 2010 were compiled into a database. The results of this study demonstrated that while counties in northern Oklahoma received the highest number of winter reports, when compared with climatology winter storm, heavy snow, ice storm, and blizzard storm types yielded an above-average occurrence across much of southwest and central Oklahoma over the study period.Disaster information, obtained from the Federal Emergency Management Agency, showed that from 1 November 1...