机械加工
机床
刀具磨损
预测建模
鉴定(生物学)
计算机科学
工程类
刀具
人工智能
机器学习
机械工程
植物
生物
作者
Qianzhe Qiao,Jinjiang Wang,Lunkuan Ye,Robert X. Gao
出处
期刊:Procedia CIRP
[Elsevier]
日期:2019-01-01
卷期号:81: 1388-1393
被引量:103
标识
DOI:10.1016/j.procir.2019.04.049
摘要
Digital twin introduces new opportunities for predictive maintenance of manufacturing machines which can consider the influence of working condition on cutting tool and contribute to the understanding and application of the predicted results. This paper presents a data-driven model for digital twin, together with a hybrid model prediction method based on deep learning that creates a prediction technique for enhanced machining tool condition prediction. First, a five-dimensional digital twin model is introduced that highlights the performance of the data analytics in model construction. Next, a deep learning technique, termed Deep Stacked GRU (DSGRU), is demonstrated that enables system identification and prediction. Experimental studies using vibration data measured on milling machine tool have shown the effectiveness of the presented digital twin model for tool wear prediction.
科研通智能强力驱动
Strongly Powered by AbleSci AI