scholarly journals Image-based tool condition monitoring based on convolution neural network in turning process

2022 ◽  
Author(s):  
Rui Kou ◽  
Shi-wei Lian ◽  
Nan Xie ◽  
Bei-er Lu ◽  
Xue-mei Liu
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Convolution Neural Network ◽  
Turning Process ◽  
Tool Condition

scholarly journals A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning

2020 ◽  
Vol 10 (20) ◽  
pp. 7298 ◽  
Author(s):  
Harshavardhan Mamledesai ◽  
Mario A. Soriano ◽  
Rafiq Ahmad
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Convolution Neural Network ◽  
Quality Requirements ◽  
Tool Condition ◽  
Change Policy ◽  
Machine Shops ◽  
The World

Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks.

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 Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals

2020 ◽  
Vol 49 ◽  
pp. 105-111 ◽  
Author(s):  
Clayton Cooper ◽  
Peng Wang ◽  
Jianjing Zhang ◽  
Robert X. Gao ◽  
Travis Roney ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Acoustic Signals ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Milling Operations ◽  
Vertical Milling

Neural Network Sensor Fusion for Tool Condition Monitoring

CIRP Annals ◽  
1990 ◽  
Vol 39 (1) ◽  
pp. 101-105 ◽  
Author(s):  
David A. Dornfeld ◽  
M.F. DeVries
Keyword(s):  
Neural Network ◽  
Sensor Fusion ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Tool Condition
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