Optimisation of tool replacement time in the machining process based on tool condition monitoring using the stochastic approach

2018 ◽  
Vol 32 (2) ◽  
pp. 159-173 ◽  
Author(s):  
Arash Zaretalab ◽  
Hamidreza Shahabi Haghighi ◽  
Saeed Mansour ◽  
Mohsen S. Sajadieh
Keyword(s):  
Condition Monitoring ◽  
Stochastic Approach ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Tool Condition ◽  
Tool Replacement ◽  
Replacement Time

Tool Condition Monitoring Based on an Adaptive Neurofuzzy Architecture

2004 ◽  
Vol 471-472 ◽  
pp. 196-200 ◽  
Author(s):  
P. Fu ◽  
A.D. Hope ◽  
G.A. King
Keyword(s):  
Condition Monitoring ◽  
Metal Cutting ◽  
Production Efficiency ◽  
Process Condition ◽  
Recognition Algorithm ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Machining Accuracy ◽  
Knowledge Based ◽  
Tool Condition

Metal cutting operations constitute a large percentage of the manufacturing activity. One of the most important objectives of metal cutting research is to develop techniques that enable optimal utilization of machine tools, improved production efficiency, high machining accuracy and reduced machine downtime and tooling costs. Machining process condition monitoring is certainly the important monitoring requirement of unintended machining operations. A multi-purpose intelligent tool condition monitoring technique for metal cutting process will be introduced in this paper. The knowledge based intelligent pattern recognition algorithm is mainly composed of a fuzzy feature filter and algebraic neurofuzzy networks. It can carry out the fusion of multi-sensor information to enable the proposed intelligent architecture to recognize the tool condition successfully.

I-KazTM-Based Analysis of Cutting Force Signals for Tool Condition Monitoring in Turning Process

2013 ◽  
Vol 471 ◽  
pp. 203-207
Author(s):  
Muhammad Rizal ◽  
Jaharah A. Ghani ◽  
Mohd Zaki Nuawi ◽  
Che Hassan Che Haron
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Machined Surface ◽  
Tool Condition ◽  
Feed Force ◽  
The Status ◽  
Force Signal

Cutting force is an important signal in machining process and has been widely used for tool condition monitoring. Monitoring the condition of the cutting tool in the machining process is very important to maintain the machined surface quality and consequently reduce inspection costs and increase productivity. This paper utilizes I-kaz-based analysis of cutting force signal to monitor the status of tool wear. The cutting force signals are measured by two channels of strain gauge that were mounted on the surface of tool holder. Experiments were carried out by turning hardened carbon steel and cutting force signals were analyzed using I-kazTM technique by integrating two component of signals (I-kaz 2D, Z2), I-kaz of cutting force (Z of Fy), and I-kaz of feed force (Z of Fx). The results show that I-kaz of feed force can be effectively used to monitor tool wear progression during turning operation.

FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process

Measurement ◽  
2015 ◽  
Vol 64 ◽  
pp. 81-88 ◽  
Author(s):  
P.Y. Sevilla-Camacho ◽  
J.B. Robles-Ocampo ◽  
J.C. Jauregui-Correa ◽  
D. Jimenez-Villalobos
Keyword(s):  
Condition Monitoring ◽  
High Speed ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
High Speed Machining ◽  
Tool Condition ◽  
Reconfigurable System

A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management

2004 ◽  
Vol 471-472 ◽  
pp. 865-870 ◽  
Author(s):  
Ying Xue Yao ◽  
Y. Lu ◽  
Zhe Jun Yuan ◽  
J.Y. Hu
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Condition Monitoring ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Fuzzy Classification ◽  
Validity And Reliability ◽  
Tool Management ◽  
Tool Condition ◽  
Tool Replacement

This paper introduces a new hybrid model for tool condition monitoring (TCM) and optimal tool management (OTM) in end milling operation. The model includes a wavelet fuzzy neural network with acoustic emission (AE) and a model of fuzzy classification of tool wear state with the detected cutting parameters supported by cutting database. The results estimated by cutting conditions and detected signals are fused by artificial neural network (ANN) so as to facilitate effective tool replacement at a proper state or time. The validity and reliability of the method are verified by experimental results.

scholarly journals Tool condition monitoring and machining process optimisation - A review

2001 ◽  
Vol 20 (2) ◽  
pp. 35-44
Author(s):  
C. Scheffer ◽  
P. S. Heyns
Keyword(s):  
South African ◽  
Condition Monitoring ◽  
Manufacturing Industry ◽  
New Technology ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Sensor Technology ◽  
Tool Condition ◽  
Recent Developments ◽  
And Control

New developments in sensor technology, signal processing and control systems can result in more efficient manufacturing. Not all new process monitoring and optimisation schemes have yet been implemented. In this article, an extensive survey of recent developments in the field is presented, with the focus on tool condition monitoring of numerically controlled machines. The purpose of this review is to create an awareness of progress in the international arena and consequently contribute to a sharper focus in South African research efforts, and also to contribute to the implementation of new technology which can improve the competitiveness of the South African manufacturing industry.

scholarly journals Algorithm of the tool condition monitoring system based on many neural networks

Mechanik ◽  
2017 ◽  
Vol 90 (3) ◽  
pp. 220-223
Author(s):  
Sebastian Bombiński ◽  
Joanna Kossakowska
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Rbf Neural Networks ◽  
Standard Time ◽  
Tool Condition ◽  
Condition Monitoring System

Presented is a comparison of different methods of estimating tool wear – obtained for group of RBF neural networks, hierarchical methods and the standard time counting. The analysis of the signals from the machining process carried out for three different experiments, clearly demonstrating the effect of presented methods. The results obtained for group of RBF neural networks are similar to results obtained for hierarchical methods.

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