Sensor-Based Monitoring and Inspection of Surface Morphology in Ultraprecision Manufacturing Processes

This research proposes approaches for monitoring and inspection of surface morphology with respect to two ultraprecision/nanomanufacturing processes, namely, ultraprecision machining (UPM) and chemical mechanical planarization (CMP). The methods illustrated in this dissertation are motivated from the compelling need for in situ process monitoring in nanomanufacturing and invoke concepts from diverse scientific backgrounds, such as artificial neural networks, Bayesian learning, and algebraic graph theory. From an engineering perspective, this work has the following contributions:1. A combined neural network and Bayesian learning approach for early detection of UPM process anomalies by integrating data from multiple heterogeneous in situ sensors (force, vibration, and acoustic emission) is developed. The approach captures process drifts in UPM of aluminum 6061 discs within 15 milliseconds of their inception and is therefore valuable for minimizing yield losses.2. CMP process dynamics are mathematically represented using a deterministic multi-scale hierarchical nonlinear differential equation model. This process-machine inter-action (PMI) model is evocative of the various physio-mechanical aspects in CMP and closely emulates experimentally acquired vibration signal patterns, including complex nonlinear dynamics manifest in the process. By combining the PMI model predictions with features gathered from wirelessly acquired CMP vibration signal patterns, CMP process anomalies, such as pad wear, and drifts in polishing were identified in their nascent stage with high fidelity (R2 ~ 75%).3. An algebraic graph theoretic approach for quantifying nano-surface morphology from optical micrograph images is developed. The approach enables a parsimonious representation of the topological relationships between heterogeneous nano-surface fea-tures, which are enshrined in graph theoretic entities, namely, the similarity, degree, and Laplacian matrices. Topological invariant measures (e.g., Fiedler number, Kirchoff index) extracted from these matrices are shown to be sensitive to evolving nano-surface morphology. For instance, we observed that prominent nanoscale morphological changes on CMP processed Cu wafers, although discernible visually, could not be tractably quantified using statistical metrology parameters, such as arithmetic average roughness (Sa), root mean square roughness (Sq), etc. In contrast, CMP induced nanoscale surface variations were captured on invoking graph theoretic topological invariants. Consequently, the graph theoretic approach can enable timely, non-contact, and in situ metrology of semiconductor wafers by obviating the need for reticent profile mapping techniques (e.g., AFM, SEM, etc.), and thereby prevent the propagation of yield losses over long production runs.Industrial Engineering & Managemen

Multi-Objective Process Optimization of Additive Manufacturing: A Case Study on Geometry Accuracy Optimization

Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods.Mechanical Engineerin

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

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

UID/00667/2020 (UNIDEMI). J. P. Oliveira acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 Prahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DE-SC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which partially supported the doctoral graduate work of Mr. Benjamin Bevans at University of Nebraska-Lincoln Benjamin, Aniruddha, and Ziyad Smoqi were further supported by the NSF grants CMMI 1752069 (CAREER) and ECCS 2020246. Detecting flaw formation in metal AM using in-situ sensing and graph theory-based algorithms was a major component of CMMI 1752069 (program office: Kevin Chou). Developing machine learning alogirthms for advanced manufacturing applications was the goal of ECCS 2020246 (Program officer: Donald Wunsch). The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. The acquisition of the XCT scanner at University of Nebraska was funded through CMMI 1920245 (Program officer: Wendy Crone). Publisher Copyright: © 2022 The AuthorsThe goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.publishersversionpublishe

Six-Sigma Quality Management of Additive Manufacturing

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps—define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM

ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale

Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge. Motivated by these challenges, in this paper we use a cloud based framework for production systems to demonstrate insights from privacy preserving incremental learning for automatic speech recognition (ILASR). By privacy preserving, we mean, usage of ephemeral data which are not human annotated. This system is a step forward for production levelASR models for incremental/continual learning that offers near real-time test-bed for experimentation in the cloud for end-to-end ASR, while adhering to privacy-preserving policies. We show that the proposed system can improve the production models significantly(3%) over a new time period of six months even in the absence of human annotated labels with varying levels of weak supervision and large batch sizes in incremental learning. This improvement is 20% over test sets with new words and phrases in the new time period. We demonstrate the effectiveness of model building in a privacy-preserving incremental fashion for ASR while further exploring the utility of having an effective teacher model and use of large batch sizes.Comment: 9 page

Beyond communication:The role of standardized protocols in a changing health care environment

Background: Communication errors have grave consequences in health care settings. The situationYbackgroundY assessmentYrecommendation (SBAR) protocol has been theorized to improve communication by creating a common language between nurses and physicians in acute care situations. This practice is gaining acceptance across the health care field. However, as yet, there has been little investigation of the ways in which SBAR may have an impact on how health care professionals operate beyond the creation of a common language. Purpose: The purposes of the study were to explore the implementation of the SBAR protocol and investigate the potential impact of SBAR on the day-to-day experiences of nurses. Methods: We performed a qualitative case study of 2 hospitals that were implementing the SBAR protocol. We collected data from 80 semistructured interviews with nurses, nurse manager, and physicians; observation of nursing and other hospital activities; and documents that pertained to the implementation of the SBAR protocol. Data were analyzed using a thematic approach. Findings: Our analysis revealed 4 dimensions of impact that SBAR has beyond its use as a communication tool: schema formation, development of legitimacy, development of social capital, and reinforcement of dominant logics

The mediating effect of green innovation on the relationship between green supply chain management and environmental performance

The emerging environmental awareness of the public, as well as the implementation of governmental regulations, force organisations to employ corporate environmental practices such as green supply chain management (GSCM) and green innovation. Accordingly, both practices are crucial to achieve professional improvement in the environmental performance of these organisations. However, research on the relationship of GSCM, green innovation, and environmental performance is relatively rare. Therefore, this study is aimed to provide empirical evidence showing that GSCM and green innovation practices significantly improve environmental performance in order to encourage organisations to implement these practices. In addition, this study investigates the relationship between GSCM and green innovation practices and the influence of these practices on the environmental performance in 123 manufacturing organisations with ISO 14001 certification. The results of PLS-SEM revealed that there is a significant and positive relationship between GSCM and green innovation, and the environmental performance. Moreover, green innovation had a positive effect on the environmental performance. Furthermore, green innovation had a mediating relationship between GSCM and environmental performance. Therefore, the present paper confirmed the significant influence of GSCM on boosting the green innovation of organisations and on the manufacturing establishments, which eventually improve the environment. In brief, the outcomes of this study provide enhanced understanding about the significant role of green innovation in the manufacturers for improving their GSCM and organisational environmental performance

Mapping product and service innovation: A bibliometric analysis and a typology

Research conducted in the innovation field lags behind organizations’ general technological development and innovativeness. Literature that previously depicted innovation types in developed markets is markedly different from progressively publicized emerging market innovation types. While capital-abundant firms tend to engage in respective pioneering and incremental innovation loops, resource-constrained firms and firms in emerging countries may partially free-ride on existing products and services through innovations such as copycat and frugal. To date, there have been no attempts to holistically consolidate product and service innovation types into one overarching typology. Using novel methods of text mining and co-citation analysis, this study systematically maps three decades of product and service innovation scholarship to provide a typology of eight major product and service innovation types. This is further supported by case study analysis to demonstrate how these innovation types fit into the cost vs market novelty matrix. This study is unique in its methodological proposition to systematically review the innovation scholarship of more than 1,400 articles through comprehensive, quantified, and objective methods that offer transparent and reproducible results. The study provides some clarity regarding the classifications and characteristics of the innovation typology

Rho-kinase-dependent F-actin rearrangement is involved in the inhibition of PI3-kinase/Akt during ischemia–reperfusion-induced endothelial cell apoptosis

Activation of cytoskeleton regulator Rho-kinase during ischemia–reperfusion (I/R) plays a major role in I/R injury and apoptosis. Since Rho-kinase is a negative regulator of the pro-survival phosphatidylinositol 3-kinase (PI3-kinase)/Akt pathway, we hypothesized that inhibition of Rho-kinase can prevent I/R-induced endothelial cell apoptosis by maintaining PI3-kinase/Akt activity and that protective effects of Rho-kinase inhibition are facilitated by prevention of F-actin rearrangement. Human umbilical vein endothelial cells were subjected to 1 h of simulated ischemia and 1 or 24 h of simulated reperfusion after treatment with Rho-kinase inhibitor Y-27632, PI3-kinase inhibitor wortmannin, F-actin depolymerizers cytochalasinD and latrunculinA and F-actin stabilizer jasplakinolide. Intracellular ATP levels decreased following I/R. Y-27632 treatment reduced I/R-induced apoptosis by 31% (P < 0.01) and maintained Akt activity. Both effects were blocked by co-treatment with wortmannin. Y-27632 treatment prevented the formation of F-actin bundles during I/R. Similar results were observed with cytochalasinD treatment. In contrast, latrunculinA and jasplakinolide treatment did not prevent the formation of F-actin bundles during I/R and had no effect on I/R-induced apoptosis. Apoptosis and Akt activity were inversely correlated (R2 = 0.68, P < 0.05). In conclusion, prevention of F-actin rearrangement by Rho-kinase inhibition or by cytochalasinD treatment attenuated I/R-induced endothelial cell apoptosis by maintaining PI3-kinase and Akt activity