In the development process of domestic aerospace components, the life enhancement test, as the test item that takes the longest time and consumes the most samples, is one of the core links to find out the reliability level of domestic components. This paper focuses on the analysis of data relevant to domestic aerospace components, both before and after the life test. The objective is to investigate the potential correlation between the quality fluctuation observed in the data before the life test and the degradation behavior of parameters observed after the life enhancement test. To achieve this target, various statistical methods were leveraged, including principal component analysis, cluster analysis, and association analysis. The proposed approach was validated through a case study involving typical domestic integrated circuits. Based on the outcomes of the case study, the method flow was refined and finalized. In the development process of domestic aerospace components, it is imperative to conduct research on the quality stability of these components and identify potential risks related to quality fluctuations promptly via employing modern statistical analysis methods. It aims to bridge the gap between classical quality and reliability theory. This study provides a theoretical basis for the control of key quality characteristics. By leveraging this method, it is possible to identify the core performance parameters that play a significant role in the quality control stage.
KEYWORDS: Machine learning, Field effect transistors, Data modeling, Windows, Quality management, Performance modeling, 3D modeling, Gallium nitride, Technology, Education and training
Digital twin is a key technology for realizing the mapping of digital model from physical system to information space. This paper studies the application of digital twin in component quality management, and proposes a digital twin system dedicated to component quality management. Model the appearance of components, state information management, fault prediction and detection, and space-time synchronization detection. Among them, the general system model library stores the appearance model, mechanism model and mathematical model of the components, and uses the relational database to access the data. By determining the nominal area and inputting the benchmark clustering, on this basis, the Fisher's quasi-measurement is used to calculate the abnormal probability, and the abnormal probability is used to realize the prediction of the failure of the components and equipment.
With the development of Industrial Internet of Things, the types and functions of components are increasing, the application environment is becoming more and more complex. Also, the quality management of components is becoming more and more important. In order to understand the knowledge related to component quality management more conveniently and build an intelligent system for component quality management, this paper proposes a method to construct component quality management knowledge graph based on BERT word embedding model and entity relationship joint extraction method based on annotation strategy. Combining entity extraction and relationship extraction parts into one not only reduces the consumption of computing resources, but also reduces the propagation of wrong entities. In this paper, the sequence to sequence model of Bert-BilSTm-CRF is adopted. Through the BERT word embedding layer, the context information can be better utilized and the accuracy of extraction can be improved. Experimental results show that compared with other classical deep learning term extraction models, this model has a significant improvement in accuracy, recall rate and F1 value.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.