.png){kind=icon}
返回
An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools
.png){kind=icon}
OA
AI
DOI：10.3390/s24082531.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.
Keyword:
sensor
thermal compensation
time-series model
edge computing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
分享收藏AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT边缘计算中的AI增强卸载: 当机器学习与工业物联网相遇时.png)
IEEE NETWORK
IEEE NETWORKIF6.3
PREAI
分享收藏Scientific Opinion on assessment of epidemiological data in relation to the health risks resulting from the presence of parasites in wild caught fish from fishing grounds in the Baltic SeaEFSA JournalOAAI
EFSA JournalIF0
OAAI分享收藏A Thermal Displacement Prediction System With an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network具有自动lrgtvac-pso优化的分支结构双向GRU神经网络的热位移预测系统IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
Sequence-to-sequence based LSTM network modeling and its application in thermal error control framework基于序列到序列的LSTM网络建模及其在热误差控制框架中的应用
PREAI
分享收藏
查看更多内容