Adaptive Network Fuzzy Inference System and support vector machine learning for tool wear estimation in high speed milling processes

Li, X.; Er, Meng Joo; Ge, Hancheng; Gan, Oon Peen; Huang, Shih-Chieh; Zhai, Lian-Yin; Linn, San; Torabi, Amin J.

doi:10.1109/iecon.2012.6389448

Cited by 29 publications

(32 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several ML approaches for tool-wear monitoring and tool-condition identification have been released during the last two decades [4,[16][17][18][19][20][21][22][23][24][25][26][27][28][29]. A monitoring strategy establishing a combination of four static and two dynamic NNs was presented by Scheffer et al [4].…”

Section: Methods For Assessing Rulmentioning

confidence: 99%

“…Experimental results have shown that the random forest algorithm can generate very accurate predictions. Amongst the most popular ML methods are the approaches that make use of fuzzy logic systems [20][21][22][23], such as the Adaptive Network Fuzzy Inference System (ANFIS) [20] or the Neuro-Fuzzy Network (NFN) [21]. Fuzzy logic systems appear to have great accuracy for tool-condition monitoring (TCM) in milling processes, but they are characterized by big computational times, affecting negatively their applicability for online monitoring.…”

Section: Methods For Assessing Rulmentioning

confidence: 99%

“…Fuzzy logic systems appear to have great accuracy for tool-condition monitoring (TCM) in milling processes, but they are characterized by big computational times, affecting negatively their applicability for online monitoring. Support Vector Machines (SVM) are also popular ML alternatives [20,[24][25][26][27]. SVMs exhibit more moderate accuracy compared to FL methods, but they are much faster and more suitable for online tool life prediction.…”

Section: Methods For Assessing Rulmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

View full text Add to dashboard Cite

The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.

show abstract

Section: Methods For Assessing Rulmentioning

confidence: 99%

Section: Methods For Assessing Rulmentioning

confidence: 99%

Section: Methods For Assessing Rulmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

View full text Add to dashboard Cite

show abstract

“…In order to overcome this difficulty we decided to follow the method of X.Li et al [6] Real-time/online tool degradation detection by using machine learning. In this study, profile projector and digital image processing methods are used for measurement of the tool geometry and tool wear [7].…”

Section: Introductionmentioning

confidence: 99%

Tool wear and tool life estimation based on linear regression learning

Karuppusamy

Lee

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

View full text Add to dashboard Cite

Tools have remained an integral part of the society without which stimulation of certain aspects of human evolution would not have been possible. In recent times the modern tools are used in the manufacturing of high precision components. We know that the accuracy and surface finish of these components can be achieved only by the usage of accurate tools. Sharp edged tools may loosen their sharpness due to repeated usage and machining parameters. Hence to address this issue we propose a system to monitor tool wear by using the captured image of cutting tool tip. We used vision system since it is the primitive method of predicting tool wear and two main machining parameters feed rate and depth of cut. The image of flank wear cutting edge at tool tip is captured by examining under profile projector. The system uses linear regression model to calculate tool wear which is mapped onto continuous 2-D coordinates with feed rate and depth of cut as axis from a captured digital image. Thus the proposed intelligent system uses profile projector and digital image processing methods to estimate tool wear continuously and predictively like humans rather than using strict rules. By estimating tool wear continuously the machine can better perform and machine components accurately by using the resultant values of feed rate and depth of cut as a threshold which are arrived as a result.

show abstract

“…Then, design features are manually extracted from the time domain, the frequency domain, the time-frequency domain, respectively, to reduce the dimensionality. Finally, a hidden Markov model (HMM), neural networks, or support vector machine (SVM) is used for the classification or regression purposes [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Xie

Huang

et al. 2019

Symmetry

View full text Add to dashboard Cite

To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.

show abstract

Adaptive Network Fuzzy Inference System and support vector machine learning for tool wear estimation in high speed milling processes

Cited by 29 publications

References 15 publications

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

Tool wear and tool life estimation based on linear regression learning

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Contact Info

Product

Resources

About