Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding

2019 ◽  
Vol 106 ◽  
pp. 1-13 ◽  
Author(s):  
Can Cheng ◽  
Jianyong Li ◽  
Yueming Liu ◽  
Meng Nie ◽  
Wenxi Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Deep Convolutional Neural Network ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Tool Condition

scholarly journals State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 335
Author(s):  
Wei Dai ◽  
Kui Liang ◽  
Bin Wang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Manufacturing Processes ◽  
State Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Aerospace Manufacturing

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

scholarly journals Condition Monitoring of Driver by Deep Convolutional Neural Network with Heterogeneous Learning

2017 ◽  
Vol 83 (12) ◽  
pp. 1101-1108
Author(s):  
Kaoruko OKUNO ◽  
Takayoshi YAMASHITA ◽  
Hiroshi FUKUI ◽  
Yuji YAMAUCHI ◽  
Hironobu FUJIYOSHI ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Deep Convolutional Neural Network ◽  
Heterogeneous Learning
Sign in / Sign up

Export Citation Format

Share Document