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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One such strategy is the Zero-Defect Manufacturing (ZDM) which is based on compensating defects from a previous process in preceding step in the current or following step [1]. Modern-day optimization efforts of multi-stage production systems focus on optimization of a single process step indepen- dent of preceding or succeeding steps.
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In the production process of hot rolled strip steel plate, the Industrial Internet of Things (IIoT) improves the quality and efficiency of production and manufacturing products, but …
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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 monitoring, as a further extension of our previous work in FFF machine condition and process monitoring where support vector machine [35], hidden semi-hidden Markov model [36 ...
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Due to the lengthy and complicated characteristics of semiconductor manufacturing process, many variables influence the model, which also may affect each other. In future research, we can focus on the process of interaction between the front-end and back-end to study the diagnosis of cross-site interaction impact factor.
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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 manufacturing process includes the design and writing of the code, as well as testing and demonstrating the software. ... subject to a predominant use study ...
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We can provide process capability studies as a single service or use multiple inspection methods for your manufacturing processes. Work With MicronPA for Process Capability Analysis. With over 30 years of experience in the industry, MicronPA offers the expertise and precision you need for process capability studies.
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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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At present production systems in car manufacturing processes are under high demand requirements and maintenance plans are of great importance in order to achieve the production objectives. The main goal of the main tenance is to increase the operativity of the plant and the machines involved in the manufacturing process, avoiding all unexpected
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This manufacturing process is labor intensive and time consuming. To improve the operation process specially in reducing the total inspection time and cost, this study proposes a multi-stage prediction and classification model to make a decision whether a chip or die or wafer needs to be checked before moving to the next process or stage.
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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 integration of AI-powered technologies, these firms have …
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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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The company will then assemble a dedicated diagnostic team with expertise in digital and analytics approaches, in the organization's manufacturing processes, and the business objectives. For the duration of the diagnostic, that team works together in one space, following a clear timeline for analysis, review, and debrief activities.
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When new assays or diagnostic tests are ready for commercialization, there can be tremendous hurdles to overcome, including significant investment in the infrastructure for automated manufacturing and test equipment and hiring and retaining highly trained personnel, as well as implementing high-end quality management systems. In addition, FDA, ISO, GMP, and other …
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At the machine level, convolutional neural networks (CNNs) have been reported to process optical images for defect recognition in quality inspection [1] or automatic characterization of tool flank wear in milling process [2], with higher recognition accuracy than the traditional machine learning methods.At the process level, DL techniques have been leveraged to …
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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 ...
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Process manufacturing (PM) is a core activity in chemical, biochemical, and related engineering disciplines, as it involves the conversion of selected raw materials into products needed by modern society [1].PM encompasses various process engineering problems, such as continuous or batch operations, quality control, operability, safety, and hazards, for a …
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The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are …
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In this paper, they modeled the manufacturing process by using PCA and evaluated the common multivariate statistics (i.e., Hotelling's T 2 and Q) by using adaptive thresholds. To …
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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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Manufacturers need standards and guidelines to effectively enable, integrate, verify, and validate monitoring, diagnostic, and prognostic technologies to enhance factory floor-level …
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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 process monitoring only needs to study the characteristics of process data and the related processing methods, without too much background knowledge needed; thus, the data-driven ...
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This study concerns a case study carried out in an automotive company which aims at improving its flows within the production workshop by pulling the physical flow and minimizing the different wastes.
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This chapter provides an overview of diagnosis in health care, including the committee's conceptual model of the diagnostic process and a review of clinical reasoning. Diagnosis has important implications for patient care, research, and policy. Diagnosis has been described as both a process and a classification scheme, or a "pre-existing set of categories …
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For example, WHO has established a prequalification process for in vitro diagnostic tests for diseases with a high individual or public health risk – the process has a particular focus on ensuring that tests for HIV, malaria and hepatitis B and C infections are affordable in resource-constrained settings. 31 The prequalification process has ...
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Over the past years, the rapidly increasing global industry competition has highly raised the standards in modern manufacturing. CNC machine tools convert raw metal workpieces into finished products, by removing sequentially extra material by applying turning, drilling, and milling operations, driven by specifically coded instructions [1].They incorporate highly complex …
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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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The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various …
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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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Initial Process Studies are important element of Production Part Approval Process (PPAP). These studies shows you manufacturing process is capable to produce parts meeting customer specifications or not. It is a …
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Abstract. Objective: Medical device development, from the product's conception to release to market, is very complex and relies significantly on the application of exact processes. This paper aims to provide an analysis and summary of current research in the field of medical device development methodologies, discuss its phases, and evaluate the associated legislative and …
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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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Using disposable equipment and process aids, prepackaged materials, closed process equipment, and contract or shared cGMP manufacturing and testing facilities can all help ensure that a pharmaceutical company complies with cGMP guidelines for …
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