Quality and Inspection of Machining Operations: Tool Condition Monitoring

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
John T. Roth ◽  
Dragan Djurdjanovic ◽  
Xiaoping Yang ◽  
Laine Mears ◽  
Thomas Kurfess
Keyword(s):  
Condition Monitoring ◽  
Lower Cost ◽  
Production Systems ◽  
Tool Condition Monitoring ◽  
Rapid Expansion ◽  
Condition Based Maintenance ◽  
Manufacturing Processes ◽  
Tool Condition ◽  
Cutting Processes ◽  
Machining Operations

Tool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems.

A Novel Generalized Approach for Real-Time Tool Condition Monitoring

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Mahmoud Hassan ◽  
Ahmad Sadek ◽  
M. H. Attia ◽  
Vincent Thomson
Keyword(s):  
Statistical Analysis ◽  
Condition Monitoring ◽  
High Speed ◽  
Threshold Level ◽  
Processing Technique ◽  
Spindle Motor ◽  
Tool Condition Monitoring ◽  
Important Principle ◽  
Tool Condition ◽  
Cutting Processes

In high-speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial demands. Several studies for TCM were carried out to capture tool failure using complex conventional and artificial intelligence (AI) techniques. However, these studies suffer from the absence of standardization and generalization. Hence, this paper presents a robust and reliable processing technique for the cutting process signals to extract generalized features in time and frequency domains. The proposed technique masks the effects of the cutting conditions on the extracted features and accentuates the tool condition effect. Characterization and statistical analysis of the processed features were performed to examine their sensitivity to the tool condition. The results revealed the processing technique capability to separate the features extracted from the spindle motor current signals into two mutually exclusive clusters according to their tool condition. The statistical analysis results were employed to optimize the tool condition detection approach using linear discrimination analysis (LDA) model. The results indicate the capability of the processing technique to minimize the system learning effort and to detect tool wear above the threshold level with accuracy above 90%.

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.

scholarly journals Remote acoustic analysis for tool condition monitoring

2019 ◽  
Vol 38 ◽  
pp. 840-847
Author(s):  
James Coady ◽  
Daniel Toal ◽  
Thomas Newe ◽  
Gerard Dooly
Keyword(s):  
Condition Monitoring ◽  
Acoustic Analysis ◽  
Tool Condition Monitoring ◽  
Tool Condition

Application of spindle power signals in tool condition monitoring based on HHT algorithm

2019 ◽  
Vol 106 (3-4) ◽  
pp. 1385-1395
Author(s):  
Bin Shen ◽  
Yufei Gui ◽  
Biao Chen ◽  
Zichao Lin ◽  
Qi Liu ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Spindle Power

scholarly journals Acoustic Emission Studies on Carbon/Phenolic Ablative Composites using PCD and PCBN Tools

1999 ◽  
Vol 8 (3) ◽  
pp. 096369359900800 ◽  
Author(s):  
P. S. Sreejith ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
The Status ◽  
Emission Studies ◽  
Pcbn Tools ◽  
Cutting Speeds ◽  
Phenolic Composite

During manufacturing, the performance of a cutting tool is largely dependent on the conditions prevailing over the tool-work interface. This is mostly dependent on the status of the cutting tool and work material. Acoustic emission studies have been performed on carbon/phenolic composite using PCD and PCBN tools for tool condition monitoring. The studies have enabled to understand the tool behaviour at different cutting speeds.

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