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In this paper, a data mining model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, an Apriori learning rules developed for …
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The analysis of such data is pivotal for ensuring production safety, a critical factor in monitoring the health status of manufacturing apparatus. Conventional defect detection …
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The American Society for Testing and Materials (ASTM) defines additive manufacturing (AM) as a process that builds physical solid models layer by layer, utilizing …
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The process monitoring method based on knowledge or mechanism model needs to have a deeper understanding of the knowledge and process mechanism, while data-driven …
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Autoclave Processing Diagnostic Study ... Process Failure Diagnosis Posted Date: November 29th, 2022 ... the most ubiquitous inverse modelling applications in manufacturing …
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diagnosis, cure, mitigation, treatment, or prevention of disease ... – GMP manufacturing process to support late stage development ... phase-3-studies-chemistry-manufacturing-and-controls ...
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Fault detection and diagnosis (FDD) constitute a critical area of research that underpins the efficient and safe operation of modern industrial processes. The field integrates data analytics ...
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priority sectors. The diagnostics study shows that bottlenecks are hampering a full development of key PCP focus areas in manufacturing sectors. On the basis of micro data and consultations, …
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A diagnostic study is a systematic and scientific process aimed at identifying the presence, nature, and extent of a specific problem or condition. It is a critical step in the …
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The diagnostic process i s carried out in three s tages. The first phase is fa ult detection, which determines the presence or absence of a fault and identifies the time of its …
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The overall objective of this study is to investigate the patterns of typical FFF process failures, and develop a data-driven failure diagnostic method for online process …
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This process relies on monitoring system variables, comparing them with a model or baseline, and employing algorithms to find discrepancies indicating faults. In industries like …
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This study offers an exhaustive analysis of different techniques used to process the data collected from gearboxes, including vibration, wear debris, and oil degradation. It also …
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The manufacturing process ends when the product has the same physical properties as when sold or transferred by the manufacturer to another, including any packaging. For software, the …
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The case studies of Siemens, General Electric, Toyota, Boeing, and Intel illustrate AI's transformative potential across various manufacturing industry sectors. Through the …
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Expert discussions were held with engineers and programmers to address the challenges (C_{1}) and (C_{2}) and resulted in six requirements for the creation of a …
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this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed-effect model. Fault diagnosability is defined in a general way that does not
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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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Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This …
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With regard to SPSP, several use cases show selective support of process steps including e.g. manufacturing process monitoring (problem detection) and identification of root …
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The manufacturing of an aircraft is a complex process, requiring exceptional precision and adherence to strict quality standards for every component. This process …
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Explore more on process mining in manufacturing and logistics. Check out for other process mining use cases and their real life examples which are process mining case studies. …
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As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like …
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The trap temperature is then set to 35 °C for the rest of the chemical detection process. Due to the hand-built manufacturing process, individual traps were found to contain …
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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 …
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Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a re-view of recent fault diagnosis …
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Part B is a diagnostic study of the current process control (a seven-step product assessment process) that results in determining the adequacy of the current control strategy. ... The residual risks and the need for establishing …
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Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even …
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2015. One of the key elements for the next generation of Intelligent Manufacturing is the capability of self-diagnosis, where the machinery used can itself report any breakdown or malfunction …
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