Author: Steven John WilcoxPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Cutting Tool Condition Monitoring Using Multiple Sensors and Artificial Intelligence Techniques on a Computer Numerical Controlled Milling Machine
Author: Steven John WilcoxHandbook of Condition Monitoring
Author: B. K. N. RaoPublisher: Elsevier
ISBN: 9781856172349
Category : Business & Economics
Languages : en
Pages : 862
Book Description
Hardbound. The need to reduce costs has generated a greater interest in condition monitoring in recent years. The Handbook of Condition Monitoring gives an extensive description of available products and their usage making it a source of practical guidance supported by basic theory.This handbook has been designed to assist individuals within companies in the methods and devices used to monitor the condition of machinery and products.
Cutting Tool Condition Monitoring Using Artificial Intelligence
Author: Rui SilvaPublisher: LAP Lambert Academic Publishing
ISBN: 9783838372341
Category : Machinery
Languages : en
Pages : 208
Book Description
This work relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. It is used a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associated with the configuration of the proposed system. With the combination of sensor-based information and inference rules, the result is an on-line system that can learn from experience and can update the knowledge base pertaining to information associated with different cutting conditions. Two neural networks resolve the problem of interpreting the complex sensor inputs while the Expert System, keeping track of previous success, estimates which of the two neural networks is more reliable. In this study an on-line tool wear monitoring system for turning processes has been developed which can reliably estimate the tool wear under common workshop conditions.
Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics
Author: Patrick F. MuirPublisher: SPIE-International Society for Optical Engineering
ISBN:
Category : Computers
Languages : en
Pages : 328
Book Description
This collection of papers from SPIE's Intelligent Systems and Advanced Manufacturing Symposium includes articles on a variety of relevant issues and topics.
Cutting Tool Condition Monitoring of the Turning Process Using Artificial Intelligence
Author: R. G. SilvaManagement and Control of Production and Logistics 2004 (MCPL 2004)
Author: Gaston LefrancPublisher: Elsevier
ISBN: 9780080444840
Category : Business & Economics
Languages : en
Pages : 440
Book Description
On-line Tool Condition Monitoring in Milling
Author: Frank RichterMultisensor Fusion for Intelligent Tool Condition Monitoring (tcm) in End Milling Through Pattern Classification and Multiclass Machine Learning
Author: Sultan Hassan BinsaeidPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
In a fully automated manufacturing environment, instant detection of condition state of the cutting tool is essential to the improvement of productivity and cost effectiveness. In this paper, a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach was developed to investigate the effectiveness of multisensor fusion when machining 4340 steel with multi-layer coated and multi-flute carbide end mill cutter. Feature- and decision-level information fusion models utilizing assorted combinations of sensors were studied against selected ML algorithms and their majority vote ensemble to classify gradual and transient tool abnormalities. The criterion for selecting the best model does not only depend on classification accuracy but also on the simplicity of the implemented system where the number of features and sensors is kept to a minimum to enhance the efficiency of the online acquisition system. In this study, 135 different features were extracted from sensory signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing modules. Then, these features along with machining parameters were evaluated for significance by using different feature reduction techniques. Specifically, two feature extraction methods were investigated: independent component analysis (ICA), and principal component analysis (PCA) and two feature selection methods were studied, chi square and correlation-based feature selection (CFS). For various multi-sensor fusion models, an optimal feature subset is computed. Finally, ML algorithms using support vector machine (SVM), multilayer perceptron neural networks (MLP), radial basis function neural network (RBF) and their majority voting ensemble were studied for selected features to classify not only flank wear but also breakage and chipping. In this research, it has been found that utilizing the multisensor feature fusion technique under majority vote ensemble gives the highest classification performance. In addition, SVM outperformed other ML algorithms while CFS feature selection method surpassed other reduction techniques in improving classification performance and producing optimal feature sets for different models.
EyeDNA
Author: Deborah M. AjiloPublisher:
ISBN:
Category :
Languages : en
Pages : 84
Book Description
Tool wear is a major obstacle to realizing full automation in metal cutting operations. In this thesis, we designed and implemented a low cost Tool Condition Monitoring (TCM) system using off-the-shelf sensors and data acquisition methods . Peripheral end milling tests were done on a low carbon steel workpiece and the spindle vibration, cutting zone temperature and spindle motor current were recorded. Features from these data sources were used to train decision tree models in MATLAB with the aim of classifying the stages of tool wear. Results showed that the feature sets fusing information from all data sources performed the best, classifying the tool wear stage with up to 93% average accuracy.
On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence
Author: Antonio J. VallejoPublisher:
ISBN:
Category : Technology
Languages : en
Pages :
Book Description
On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence.