Manufacturing Quality Prediction Based on Deep Learning in Conjunction with Gramian Angular and Markov Transition Fields

Jehn Ruey Jiang, Hsueh Chih Chen

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

A method to predict the surface roughness of wire electrical discharge machining (WEDM) workpieces is proposed in this paper. It first utilizes the Gramian angular field (GAF) and the Markov transition field (MTF) to encode the time series data into 2D images, which are then fed into the CBR long short-term memory (CBR-LSTM) neural network to predict the WEDM workpiece surface roughness, where CBR represents the combination of the convolution, batch normalization, and rectified linear unit (ReLU) layers. The Keras hyperband tuner is used to optimize CBR-LSTM network hyperparameters. The proposed method is shown to have the best prediction when compared with related ones.

原文???core.languages.en_GB???
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面439-440
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態已出版 - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
持續時間: 17 7月 202319 7月 2023

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區Taiwan
城市Pingtung
期間17/07/2319/07/23

指紋

深入研究「Manufacturing Quality Prediction Based on Deep Learning in Conjunction with Gramian Angular and Markov Transition Fields」主題。共同形成了獨特的指紋。

引用此