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.

A Neuro-Fuzzy System for Tool Condition Monitoring in Metal Cutting

2000 ◽  
Vol 123 (2) ◽  
pp. 312-318 ◽  
Author(s):  
Omez S. Mesina ◽  
Reza Langari
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Fuzzy System ◽  
Tool Condition Monitoring ◽  
True Nature ◽  
Tool Condition ◽  
Neuro Fuzzy ◽  
The Relationship

A neuro-fuzzy system is used to predict the condition of the tool in a milling process. Specifically the relationship between the sensor readings and tool wear state is first captured via a neural network and is subsequently reflected in linguistic form in terms of a fuzzy logic based diagnostic algorithm. In this approach, the neural network serves as an interpolative mechanism for the generation of data that is consistent with the behavior of the process, whereas fuzzy logic provides a transparent view of the relationship between the measured variables and the tool wear state. The methodology used in this paper incorporates an error-based, density-driven adaptation scheme in conjunction with a neural network based reference model to adapt the fuzzy membership functions associated with the tool condition monitoring algorithm to ensure that the rule set reflects the true nature of the inter-relationship between the sensor readings and the tool condition. Experimental results show that the proposed fuzzy mechanism correctly predicts the condition of the tool in 97 percent of the cases where it is applied.

scholarly journals Tool Wear Inspection and Classification by Artificial Neural Network Based on Vision System and Cutting Force Signal.

2021 ◽  
Author(s):  
Ahmed Abdeltawab ◽  
Xi Zhang ◽  
Longjia Zhang ◽  
Chuanjun Li
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Force ◽  
Condition Monitoring ◽  
Time Domain ◽  
Vision System ◽  
Tool Condition Monitoring ◽  
Ann Model ◽  
Tool Condition ◽  
Milling Tool

Abstract The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and flank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

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.

Experimental Investigation to Predict Tool Wear and Vibration Displacement in Turning – A Base for Tool Condition Monitoring

2016 ◽  
Vol 16 (2) ◽  
pp. 103-114
Author(s):  
M. Prakash Babu ◽  
Balla Srinivasa Prasad
Keyword(s):  
Mathematical Model ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Cutting Forces ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Depth Of Cut ◽  
Vibration Displacement ◽  
Tool Condition ◽  
Tool Vibrations

AbstractIn the present work investigation primarily focuses on identifying the presence of cutting tool vibrations during face turning process. For this purpose an online non-contact vibration transducer i. e. laser Doppler Vibrometer is used as part of a novel approach. The revisions in the values of cutting forces, vibrations and acoustic optic emission signals with cutting tool wear are recorded and analyzed. This paper presents a mathematical model in an attempt to understand tool lifeunder vibratory cutting conditions. Tool wear and cutting force data are collected in the dry machiningof AISI 1040 steel at different vibrationinduced test conditions. Identifying the correlation among tool wear, cutting forces and displacement due to vibration is a critical task in the present study. These results are used to predict the evolution of displacement and tool wear in the experiment. Specifically, the research tasks include: to provide an appropriate experimental data to prove the mathematical model of tool wear based on the influence of cutting tool vibrations in turning.The modeling is focused on demonstrating the scientific relationship between the process variables such as vibration displacement, vibration amplitude, feedrate, depth of cut and spindle speed while getting into account machine dynamics effect and the effects such as surface roughness and tool wear generated in the operation. Present work also concentrates on the improvement in machinability during vibration assisted turning with different cutting tools. The effect of work piece displacement due to vibration on the tool wear is critically analyzed. Finally, tool wear is established on the basis of the maximum displacement that can be tolerated in a process for an effective tool condition monitoring system.

Sign in / Sign up

Export Citation Format

Share Document