Automatic Identification of Most Suitable Sensors and Health Indicators for Cutting Tool Wear Prediction in Smart Manufacturing Systems

2021 ◽  
Author(s):  
Pradeep Kundu ◽  
Xichun Luo ◽  
Yi Qin
Keyword(s):  
Tool Wear ◽  
Manufacturing Systems ◽  
Cutting Tool ◽  
Health Indicators ◽  
Smart Manufacturing ◽  
Automatic Identification ◽  
Wear Prediction ◽  
Cutting Tool Wear ◽  
Tool Wear Prediction ◽  
Smart Manufacturing Systems

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

An accurate cutting tool wear prediction method under different cutting conditions based on continual learning

2021 ◽  
pp. 095440542199369
Author(s):  
Jiaqi Hua ◽  
Yingguang Li ◽  
Wenping Mou ◽  
Changqing Liu
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Prediction Method ◽  
Cutting Conditions ◽  
Wear Prediction ◽  
Cutting Tool Wear ◽  
Task Distribution ◽  
Tool Wear Prediction ◽  
Meta Learning ◽  
Continual Learning

Cutting tool wear prediction plays an important role in the machining of complex aerospace parts, and it is still a challenge under varying cutting conditions. To overcome the limitations of the existing methods in generalization ability when dealing with cutting conditions changing largely, this paper proposed a novel cutting tool wear prediction method based on continual learning. A meta-LSTM model is firstly trained for specific cutting conditions and can be easily fine-tuned with very small number of samples to adapt to new cutting conditions. Specifically, the meta-model could be continuously updated as machining data increase by using an orthogonal weights modification method. The experiment results show that the proposed method can realize accurate prediction of tool wear under different cutting conditions. Compared with existing methods including meta-learning methods, the range of adapted cutting conditions could be expanded as the task distribution of new cutting conditions is continuously learned by the prediction model.

CUTTING TOOL WEAR PREDICTION BY USING THE GROUP METHOD OF DATA HANDLING (GMDH)

Mechanika ◽  
2012 ◽  
Vol 18 (5) ◽  
Author(s):  
D. Kara Ali ◽  
M. E. A. Ghernaout ◽  
S. Galiz ◽  
A. Liazid
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Group Method ◽  
Data Handling ◽  
Wear Prediction ◽  
Cutting Tool Wear ◽  
Tool Wear Prediction

scholarly journals A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert X. Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Tool Wear Prediction

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

Meta domain generalization for smart manufacturing: Tool wear prediction with small data

2022 ◽  
Vol 62 ◽  
pp. 441-449
Author(s):  
Dongdong Wang ◽  
Qingyang Liu ◽  
Dazhong Wu ◽  
Liqiang Wang
Keyword(s):  
Tool Wear ◽  
Smart Manufacturing ◽  
Small Data ◽  
Wear Prediction ◽  
Tool Wear Prediction

Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear

2017 ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Support Vector ◽  
Model Based ◽  
Smart Manufacturing Systems

Manufacturers have faced an increasing need for the development of predictive models that help predict mechanical failures and remaining useful life of a manufacturing system or its system components. Model-based or physics-based prognostics develops mathematical models based on physical laws or probability distributions, while an in-depth physical understanding of system behaviors is required. In practice, however, some of the distributional assumptions do not hold true. To overcome the limitations of model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While earlier work demonstrated the effectiveness of data-driven approaches, most of these methods applied to prognostics and health management (PHM) in manufacturing are based on artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to explore the ability of random forests (RFs) to predict tool wear in milling operations. The performance of ANNs, SVR, and RFs are compared using an experimental dataset. The experimental results have shown that RFs can generate more accurate predictions than ANNs and SVR in this experiment.

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