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Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants.
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Semiconductor manufacturing process is lengthy and technology intensive that contains several hund red process steps with advanced tools to fabricate integrated circuits (IC) on a silicon wafer in the wafer fabrication facility (fab). Semiconductor manufacturing is very capital intensive, in which building a modern 12 in. wafer fab with
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In this study, a two-stage attention-based variational long short-term memory (LSTM) that allows fault detection and diagnosis in electrolytic copper manufacturing processes is proposed.
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In these studies, knowledge-based quality diagnosis is a research result in the field of artificial intelligence . It can simulate the way of human thinking, reasoning, and analysis through accumulated knowledge or experience. ... Although the quality diagnosis method for manufacturing process based on product quality DNA proposed in this paper ...
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In this study, a two-stage attention-based variational long short-term memory (LSTM) that allows fault detection and diagnosis in electrolytic copper manufacturing processes is proposed. As the surface quality of electrolytic copper determines the yield and quality of the product, an automated surface inspection (ASI) system has been introduced at various …
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to variations in manufacturing process, the collected data are ... has been successfully and popularly used in fault diagnosis studies [17], [18]. Through learning domain-invariant features
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identify the variables that cause low-grade electrolytic copper. This is the first study to utilize feature data from an ASI sys-tem for fault detection and diagnosis in an electrolytic copper manufacturing process. Using a two-stage attention structure, important input and temporal features were extracted even though the noise ratio was high.
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diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems. 1. INTRODUCTION Manufacturing and process industries such as iron & steel,
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This diagnostic study was conducted between August and December 2020, and benefitted immensely ... The share of manufacturing has stagnated at around 6 per cent over the last decades. As highlighted in the literature and reiterated in policy circles, manufacturing is ... This diagnostic report is the first step in the PCP process. In the ...
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Request PDF | VSM a powerful diagnostic and planning tool for a successful Lean implementation: a Tunisian case study of an auto parts manufacturing firm | For the past few years, many ...
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Data-driven artificial intelligence models require explainability in intelligent manufacturing to streamline adoption and trust in modern industry. However, recently developed explainable artificial intelligence (XAI) techniques that estimate feature contributions on a model-agnostic level such as SHapley Additive exPlanations (SHAP) have not yet been evaluated for …
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In five of these studies, visual object detection, surface defect detection, machine production scheduling application, fault diagnosis and prediction, and monitoring of the manufacturing process ...
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This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and ...
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The manufacturing process of semiconductors has become complex due to the ultra-fine chemical process technologies, and a large amount of testing process is also needed to find out yield-reducing ...
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Aiming to solve the problems of the inaccurate dimension reduction of high-dimensional data and insufficient information utilization in traditional manufacturing process monitoring methods—in which mostly only the distance information of pairwise points is used as the similarity index for the data dimension reduction—this paper proposes a data-manifold …
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Effect of Dataset Size and Auxiliary Data in Bayesian Learning of Advanced Manufacturing: A Composite Autoclave Processing Diagnostic Study November 2022 DOI: 10.21203/rs.3.rs-2277713/v1
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In this paper, a data-driven monitoring method for online AM process failure diagnosis based on acoustic emission (AE) is proposed, and its application to the fused …
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report, represented as a diagnostic study for Electronic Clusters in Bangalore. We would like to express special thanks to Shri. Ram Mohan Mishra, IAS, (Additional Secretary and DC-MSME) for his proactive support and guidance to the team during the entire process. We would also like to express our gratitude to Shri. Piyush Srivastava (ADC), Shri.
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Motivated by the powerful ability of deep belief network (DBN) to extract the essential features of input data, this paper develops a real-time quality monitoring and …
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The purpose of this study is to examine the use of this sensor data in creating a deep learning model that leverages information generated within multi-stage manufacturing processes.
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It combines the local, full structural information (distance and angle information) and global feature analysis fusion local angle of the manifold, realizing highly efficient …
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This study aims to propose a multiple time-series convolutional neural network (MTS-CNN) model for fault detection and diagnosis in semiconductor manufacturing. This study incorporates data augmentation with sliding window to generate amounts of subsequences and thus to enhance the diversity and avoid over-fitting.
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In this paper, a unified framework with dual Hierarchical Bayesian Networks (HBNs) has been presented for simultaneous online process monitoring and fault diagnosis of a …
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Abstract: Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis …
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Ellume called on Bosch Australia Manufacturing Solutions (BAMS), a leading supplier of factory automation for the medical device industry, to automate the high-volume production of its COVID-19 diagnostic tests. This involves 27 new production lines in total – three new lines for Ellume's facility in Brisbane, Australia and 24 production lines for their facility in Maryland, U.S.
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The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep ...
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Fujitsu has analyzed the causes of rework in existing manufacturing processes and tested hypotheses from a technical perspective and from the viewpoint of users. The purpose is to …
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A novel monitoring and diagnosis method based on DBN model for manufacturing process profiles is proposed in this paper. The proposed method not only focuses on the monitoring of some particular manufacturing processes, but also can be applied to shift detection and fault diagnosis in common manufacturing processes.
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In this regard, a lot of efforts have been made to make an AM process more controllable. This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and closed-loop control of an AM process.
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1. Production Process Optimization. Generative AI can optimize manufacturing production processes. By analyzing historical process data and simulating different scenarios, generative AI can identify optimal process settings that improve efficiency, reduce waste, and enhance product quality.
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use multistage manufacturing processes such as assembly and ma-chining for automotive, aerospace, or appliance products. The complexity of a manufacturing process puts high …
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Each equipment in the semiconductor manufacturing process is often accompanied by a large amount of sensor readings, also called status variable identification (SVID). ... to create a graphical aid in FDC for the process engineer. An empirical study is conducted to validate the proposed data-driven framework for fault detection and diagnostic ...
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Related: Manufacturing Case Studies . Use of AI in Manufacturing – 5 Case Studies Case Study 1: Siemens AG Company Profile. Siemens AG stands as a dominant force worldwide in the fields of electronics and electrical …
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Correct and reliable diagnosis is considered as the most important challenge in PS process. In this study, the seven-step approach for complex and cross-functional diagnosis has been developed based on the first two steps of universal PS approach. ... [25], [24], [31], [41]. Since FCA has launched the world class manufacturing (WCM) model as a ...
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process diagnosis network (MP-DN), a model that uses equipment sensor data from multi-stage processes to assess the wafer's condition and interpret which process had the greatest
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The latest diagnostic tools allow developers and researchers to stay competitive, particularly as the demand for fast, reliable results increases. This comprehensive guide explores a portfolio of molecular and immunodiagnostics solutions to …
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Cluster Diagnostic Kannauj Page 4 Contents 1. Executive Summary 7 2. Introduction 8 3. Kannauj Attar and Essential Oil Industry 9 3.1 Manufacturing Process 10 4 Key Stakeholders and Institutional Framework 12 5 Approach and Methodology 14 6. Technology Centre – FFDC Overview 15 6.1 Production Process 16 6.2 Training Services 17
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