Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing

2015 ◽  
Vol 28 (6) ◽  
pp. 1285-1301 ◽  
Author(s):  
Doriana M. D’Addona ◽  
A. M. M. Sharif Ullah ◽  
D. Matarazzo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Tool Wear ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Artificial Neural

TOOL WEAR PREDICTION FROM ACOUSTIC EMISSION AND SURFACE CHARACTERISTICS VIA AN ARTIFICIAL NEURAL NETWORK

1999 ◽  
Vol 13 (6) ◽  
pp. 955-966 ◽  
Author(s):  
P. WILKINSON ◽  
R.L. REUBEN ◽  
J.D.C. JONES ◽  
J.S. BARTON ◽  
D.P. HAND ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Characteristics ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Artificial Neural

Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling

2018 ◽  
Vol 12 (3) ◽  
pp. 275-281 ◽  
Author(s):  
Alessandra Caggiano ◽  
Luigi Nele ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Tool Wear ◽  
Sensor Fusion ◽  
Tool Wear Prediction ◽  
Multiple Sensor ◽  
Tool Replacement ◽  
Artificial Neural

An intelligent sensor monitoring procedure was implemented to monitor the drilling of carbon fiber reinforced plastic (CFRP)/CFRP stacks used in the assembly of aircraft fuselage panels; the signals from these sensors were then used to develop an artificial neural network-based cognitive paradigm to predict tool wear, which would allow on-line decision making regarding tool replacement. A multiple sensor system, capable of acquiring signals relative to thrust force, torque, and acoustic emission RMS, was employed during experimental drilling tests, under different rotational speed and feed conditions. Advanced sensor signal processing techniques, including signal conditioning and segmentation, as well as statistical feature extraction and data fusion, were implemented on the acquired signals. Selected statistical features extracted from the multiple sensor signals in the time domain were combined via sensor fusion techniques to construct sensor fusion pattern vectors. These were then fed to artificial neural networks for pattern recognition, with the goal of finding correlations which would allow the prediction of the corresponding tool wear. The tool wear prediction performed by the artificial neural network can be utilized to support decision making at the appropriate time for worn tool replacement, which is extremely useful for drilling automation, as well as for estimating the quality of the drilled holes.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Pattern Recognition of Vertical Strabismus Using an Artificial Neural Network (StrabNet©)

Strabismus ◽  
2009 ◽  
Vol 17 (4) ◽  
pp. 131-138 ◽  
Author(s):  
Arvind Chandna ◽  
Anthony C. Fisher ◽  
Ian Cunningham ◽  
Deborah Stone ◽  
Maureen Mitchell
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural
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