Variation Pattern Recognition of the BIW OCMM Online Measurement Data Based on LSTM NN
Autor: | Sun Jin, Changhui Liu, Yuan Qu, Kun Chen |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
General Computer Science
Artificial neural network business.industry Computer science Autocorrelation Use of time bodyin-white (BIW) General Engineering deep learning Pattern recognition Variation (game tree) Statistical process control long short-term memory neural network (LSTM NN) Backpropagation Pattern recognition (psychology) online measurement data Factory (object-oriented programming) General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Variation pattern recognition lcsh:TK1-9971 |
Zdroj: | IEEE Access, Vol 7, Pp 69007-69014 (2019) |
ISSN: | 2169-3536 |
Popis: | An accurate recognition of a dimensional variation pattern is very important for producing high-quality body-in-white (BIW). The wide application of optical coordination measurement machines (OCMM) in vehicle factory provided massive online dimensional data for the variation pattern recognition. However, the massive serially correlated or autocorrelated and 100% measurement data generated from the OCMM challenge the traditional statistical process control (SPC) technology and the common variation recognition approaches. This paper presents a novel deep-learning method, long short-term memory neural network (LSTM NN), to recognize the variation pattern of the BIW OCMM online measurement data. A comparative study between the backpropagation neural network (BP NN) and the LSTM NN was implemented, and the practicability of the proposed intelligent method was demonstrated by a case study. With the efficient use of time series information, the LSTM NN has a good performance in variation patterns' recognition and high practicability in improving the quality of the BIW. |
Databáze: | OpenAIRE |
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