کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10156159 | 1666377 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting shim gaps in aircraft assembly with machine learning and sparse sensing
ترجمه فارسی عنوان
پیش بینی شکاف شیم در مونتاژ هواپیما با یادگیری ماشین و سنجش ضعیف
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کلمات کلیدی
مونتاژ پیش بینی شده، فراگیری ماشین، بهینه سازی انعطاف پذیر، حساس بودن اطلاعات بزرگ،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
In this work, we present an alternative strategy for predictive shimming, based on machine learning and sparse sensing to first learn gap distributions from historical data, and then design optimized sparse sensing strategies to streamline the collection and processing of data. This new approach is based on the assumption that patterns exist in shim distributions across aircraft, and that these patterns may be mined and used to reduce the burden of data collection and processing in future aircraft. Specifically, robust principal component analysis is used to extract low-dimensional patterns in the gap measurements while rejecting outliers. Next, optimized sparse sensors are obtained that are most informative about the dimensions of a new aircraft in these low-dimensional principal components. We demonstrate the success of the proposed approach, known within Boeing as PIXel Identification Despite Uncertainty in Sensor Technology (PIXI-DUST), on historical production data from 54 representative Boeing commercial aircraft. Our algorithm successfully predicts 99% of the shim gaps within the desired measurement tolerance using around 3% of the laser scan points that are typically required; all results are rigorously cross-validated.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Manufacturing Systems - Volume 48, Part C, July 2018, Pages 87-95
Journal: Journal of Manufacturing Systems - Volume 48, Part C, July 2018, Pages 87-95
نویسندگان
Krithika Manohar, Thomas Hogan, Jim Buttrick, Ashis G. Banerjee, J. Nathan Kutz, Steven L. Brunton,