Abstract
This research employs a multi-dimensional approach to the assessment of component quality in aerospace industry, utilizing data from Destructive Physical Analysis (DPA), Physics of Failure Analysis (PFA), retest screening, and Failure Analysis (FA). By formulating an innovative model for component quality situation assessment, we first construct a thorough and exhaustive evaluation metric for the quality of components integrated within the system. Then we develop a prediction method for it based on Long Short-Term Memory (LSTM) networks techniques. This investigation contributes to both the theoretical underpinnings and practical applications of component quality situation assessment, furnishing the industry with a dependable analytical instrument that enhances the quality control procedures of components in aerospace industry.
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Wu, C. et al. (2024). Component Quality Assessment and Prediction in Aerospace Industry. In: Chien, CF., Dou, R., Luo, L. (eds) Proceedings of Industrial Engineering and Management. SMILE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0194-0_48
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DOI: https://doi.org/10.1007/978-981-97-0194-0_48
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