A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems

Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue costs hinders the accurate diagnosis in the actual process. In addition, time-varying operational conditions that generate nonstationary process faults and the correlation information in the process require to consider for accurate fault diagnosis in the manufacturing systems. This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges. Specifically, the method is based on a practical assumption that it will likely have a few process faults (sparse). In addition, the hierarchical structure of CSSBL has several parameterized prior distributions to address the above challenges. As posterior distributions of process faults do not have closed form, this paper derives approximate posterior distributions through Variational Bayes inference. The proposed method's efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system. The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems

Kernel Density Estimation and Metropolis-Hastings Sampling in Process Capability Analysis of Unknown Distributions

ABSTRACT Strong normality assumption is associated with widely used process capability indices such as c p , c pk . Violation of the assumption will mislead the interpretation in applications. A nonparametric method is proposed for density estimation of any unknown distribution. Kernels are used for density estimation and metropolis-hastings (M-H) algorithm is adopted to generate samples from the density. M-H sampling provides a tool to accommodate different kernel functions and flexibility of future extension to multivariate cases. Conformity (yield) based indices (y p , y) are adopted to replace c p , c pk . These indices can be conveniently assessed by the proposed kernel density based M-H algorithm (K-M-H). The method is validated by several simulation case studies

Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization

The goal of this work is to use sensor data for online detection and identification of process anomalies (faults). In pursuit of this goal, we propose Dirichlet process Gaussian mixture (DPGM) models. The proposed DPGM models have two novel outcomes: 1) DP-based statistical process control (SPC) chart for anomaly detection and 2) unsupervised recurrent hierarchical DP clustering model for identification of specific process anomalies. The presented DPGM models are validated using numerical simulation studies as well as wireless vibration signals acquired from an experimental semiconductor chemical mechanical planarization (CMP) test bed. Through these numerically simulated and experimental sensor data, we test the hypotheses that DPGM models have significantly lower detection delays compared with SPC charts in terms of the average run length (ARL1) and higher defect identification accuracies (F-score) than popular clustering techniques, such as mean shift. For instance, the DP-based SPC chart detects pad wear anomaly in CMP within 50 ms, as opposed to over 140 ms with conventional control charts. Likewise, DPGM models are able to classify different anomalies in CMP

Quantification of Ultraprecision Surface Morphology using an Algebraic Graph Theoretic Approach

Assessment of progressive, nano-scale variation of surface morphology during ultraprecision manufacturing processes, such as fine-abrasive polishing of semiconductor wafers, is a challenging proposition owing to limitations with traditional surface quantifiers. We present an algebraic graph theoretic approach that uses graph topological invariants for quantification of ultraprecision surface morphology. The graph theoretic approach captures heterogeneous multi-scaled aspects of surface morphology from optical micrographs, and is therefore valuable for in situ real-time assessment of surface quality. Extensive experimental investigations with specular finished (Sa ~ 5 nm) blanket copper wafers from a chemical mechanical planarization (CMP) process suggest that the proposed method was able to quantify and track variations in surface morphology more effectively than statistical quantifiers reported in literature

Perspectives and challenges of applying the water-food-energy nexus approach to lake eutrophication modelling

Embargo until August 4, 2023The water-food-energy (WFE) nexus is about balancing competing interests to secure the sustainability of services provided by interconnected sectors. Ignoring the interconnections could cause serious consequences. For example, eutrophication caused by overemphasizing on food production maximization could threaten water security. Worldwide eutrophication intensification is one of the most important causes of the lake water quality deteriorations. Water quality models are usually important decision making tools for policy makers. This study attempts to explore the possibilities of applying the WFE nexus concept into water quality models. We propose the most significant challenge is lack of a common modelling framework to streamline connections between up- and downstream models. As the most important water quality issue, eutrophication modeling should increase its visibility in the United Nations Sustainable Develop Goals.acceptedVersio

The significance of glycolysis in tumor progression and its relationship with the tumor microenvironment

It is well known that tumor cells rely mainly on aerobic glycolysis for energy production even in the presence of oxygen, and glycolysis is a known modulator of tumorigenesis and tumor development. The tumor microenvironment (TME) is composed of tumor cells, various immune cells, cytokines, and extracellular matrix, among other factors, and is a complex niche supporting the survival and development of tumor cells and through which they interact and co-evolve with other tumor cells. In recent years, there has been a renewed interest in glycolysis and the TME. Many studies have found that glycolysis promotes tumor growth, metastasis, and chemoresistance, as well as inhibiting the apoptosis of tumor cells. In addition, lactic acid, a metabolite of glycolysis, can also accumulate in the TME, leading to reduced extracellular pH and immunosuppression, and affecting the TME. This review discusses the significance of glycolysis in tumor development, its association with the TME, and potential glycolysis-targeted therapies, to provide new ideas for the clinical treatment of tumors

Rheological, In Situ Printability and Cell Viability Analysis of Hydrogels for Muscle Tissue Regeneration

Advancements in additive manufacturing have made it possible to fabricate biologically relevant architectures from a wide variety of materials. Hydrogels have garnered increased attention for the fabrication of muscle tissue engineering constructs due to their resemblance to living tissue and ability to function as cell carriers. However, there is a lack of systematic approaches to screen bioinks based on their inherent properties, such as rheology, printability and cell viability. Furthermore, this study takes the critical first-step for connecting in-process sensor data with construct quality by studying the influence of printing parameters. Alginate-chitosan hydrogels were synthesized and subjected to a systematic rheological analysis. In situ print layer photography was utilized to identify the optimum printing parameters and also characterize the fabricated three-dimensional structures. Additionally, the scaffolds were seeded with C2C12 mouse myoblasts to test the suitability of the scaffolds for muscle tissue engineering. The results from the rheological analysis and print layer photography led to the development of a set of optimum processing conditions that produced a quality deposit while the cell viability tests indicated the suitability of the hydrogel for muscle tissue engineering applications