Research of Tool Wear Condition Recognition Diagnosis System Based on the Machined Workpiece Surface Texture Image

2011 ◽  
Vol 130-134 ◽  
pp. 2508-2512
Author(s):  
Wei Dong Xu ◽  
Xiao Hong Ren ◽  
Li Jie Li ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Surface Texture ◽  
Texture Feature ◽  
Texture Characteristic ◽  
Workpiece Surface ◽  
Diagnosis System ◽  
Diagnosis Rate ◽  
Wear Condition ◽  
Condition Recognition

Aiming at the machined workpiece surface texture images,some technology about image pre-processing and the texture feature extraction based on gray level co-occurrence matrix are researched. Then it is time for the analysis of the texture characteristic parameters based on BP neural network and the identification and diagnosis of tool wear state, Finally the recognition diagnosis system interface is designed by Matlab-GUI.System simulation shows that the interface fusion of image processing and neural network is a good way to ensure the realization of tool wear condition recognition,what’more, the identification diagnosis rate is profect.

scholarly journals Analysis of influencing factors for the dressing process in double side face grinding

2021 ◽  
Author(s):  
Eckart Uhlmann ◽  
Linus Lichtschlag
Keyword(s):  
Grain Size ◽  
Tool Wear ◽  
Influencing Factors ◽  
Scientific Data ◽  
Speed Ratio ◽  
Workpiece Surface ◽  
Principal Influence ◽  
Side Face ◽  
Face Grinding ◽  
Wear Condition

AbstractIn grinding, the design of the dressing process is an essential part of work preparation and restoration of the grinding wheel’s profile and cutting ability. In contrast to most grinding processes, the choice of dressing parameters in double face grinding with planetary kinematics has so far only been experience-based. As a consequence, the dressing process causes a higher degree of tool wear than the machining of the workpieces. A focused design of the dressing process based on a scientific data could help to improve the ecological and the economic efficiency by reducing tool wear and the amount of dressing tools used. In this paper, methods for determining the wear condition and the result of the dressing process, including macro- and microscopic characteristic are presented. This includes a correlation analysis between parameters of wear characteristics and workpiece surface quality. Furthermore, technological investigations are carried out in order to systematically limit the main influencing factors on the dressing process. As a result, the parameters dresser grain size dgd, rotational speed ratio nld and the machined dresser height ∆hd are identified as significant for dressing. The knowledge about their principal influence on the dressing result could provide the basis for further research.

Research on Tool Wear Based on Texture Fractal Dimension

2011 ◽  
Vol 66-68 ◽  
pp. 1163-1166
Author(s):  
Mao Jun Chen ◽  
Zhong Jin Ni ◽  
Liang Fang
Keyword(s):  
Fractal Dimension ◽  
Tool Wear ◽  
Surface Texture ◽  
Manufacturing Systems ◽  
Experimental Results ◽  
Machining Process ◽  
Work Piece ◽  
Automated Manufacturing ◽  
Automated Manufacturing Systems ◽  
Wear Condition

In automated manufacturing systems, one of the most important issues is the detection of tool wear during the machining process. The Hausdorff-Besicovitch (HB) dimension is used to analyze the feature of the surface texture of work-piece in this paper. The value of the fractal dimension of the work-piece surface texture tends to decrease with the machining process, due to the texture becoming more complex and irregular, and the tool wear is also becoming more and more serious. That can describe the inherent relationship between work-piece surface texture and tool wear. The experimental results demonstrate the probability of using the fractal dimension of work-piece surface texture to monitor the tool wear condition.

Identification of Tool Wear Condition Based on Generalized Fractal Dimensions and BP Neural Network Optimized with Genetic Algorithm

2013 ◽  
Vol 318 ◽  
pp. 71-75 ◽  
Author(s):  
Kai Feng Zhang ◽  
Hui Qun Yuan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Fractal Theory ◽  
Fractal Dimensions ◽  
Cutting Process ◽  
Generalized Dimensions ◽  
Ae Signals ◽  
Wear Condition

Based on multi-fractal theory, the generalized fractal dimensions of acoustic emission (AE) signals during cutting process were calculated using improved box-counting method. The generalized dimension spectrums of AE signals for different tool wear condition were gained, and the relation between tool wear condition and generalized dimensions was analyzed. Together with cutting process parameters, the generalized fractal dimensions were taken as the input vectors of BP neural network after normalization. The initial weight and bias values of BP neural network which was used to classify the tool wear condition were optimized with Genetic Algorithm. The test results showed that the method can be used effectively for the identification of tool wear condition.

A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture

2010 ◽  
Vol 154-155 ◽  
pp. 412-416 ◽  
Author(s):  
Zhong Ren Wang ◽  
Yu Feng Zou ◽  
Fan Zhang
Keyword(s):  
Tool Wear ◽  
Machine Vision ◽  
Vision System ◽  
Texture Feature ◽  
Tool Wear Monitoring ◽  
Machined Surface ◽  
Workpiece Surface ◽  
Tool Condition ◽  
Wear Monitoring ◽  
Feature Information

Machine vision technique is an advanced method for tool wear monitoring. In this article, a holding system has been designed and fabricated to realize the combination of machine tools and machine vision system. On-machine experiments were carried out to test the effect of this method. Experimental results indicate that tool condition monitoring can be successfully accomplished by analyzing texture feature information extracted from the machined surface.

Quantification of Tool Wear Condition from the Sound Recorded During the Process Using Neural Network

2020 ◽  
pp. 309-315
Author(s):  
Andrews Samraj ◽  
P Ragupathi. ◽  
T Nandhakumar. ◽  
T Kamala raj.
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Wear Condition

A Multifeature Approach to Tool Wear Estimation Using 3D Workpiece Surface Texture Parameters

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
Yi Liao ◽  
David A. Stephenson ◽  
Jun Ni
Keyword(s):  
Tool Wear ◽  
Surface Texture ◽  
Polycrystalline Diamond ◽  
Texture Features ◽  
Surface Measurement ◽  
Warning Sign ◽  
Workpiece Surface ◽  
3D Surface ◽  
Texture Parameters ◽  
3D Surface Texture

This work presents a new way to determine the condition of a cutting tool based on 3D texture parameters of workpiece surface. Recently, a laser holographic interferometer has been developed to rapidly measure a large workpiece surface and generate a 3D surface height map with micron level accuracy. This technique enables online surface measurement for machined workpieces. By measuring and analyzing workpiece surface texture, the interaction between the tool’s cutting edges and the workpiece surface can be extracted as a spatial signature. It can then be used as a warning sign for tool change because the workpiece produced by a heavily worn tool exhibits more irregularities in its surface texture than that produced by a normal tool. Multiple texture parameters such as image intensity histogram distribution parameter, 3D peak-to-valley height, and 3D surface waviness parameter are employed to indicate the onset of severe tool wear. In this work, aluminum (Al308) and compacted graphite iron parts were machined by a polycrystalline diamond insert and a multiphase coated tungsten carbide insert, respectively. After that, multiple 3D surface texture features of workpieces samples under different phases of tool wear were analyzed in order to assess tool wear conditions. The experimental results verify that these surface texture features can be used as good indicators for online tool wear monitoring.

scholarly journals Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9026
Author(s):  
Weihang Dong ◽  
Xianqing Xiong ◽  
Ying Ma ◽  
Xinyi Yue
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Arithmetic Average ◽  
Filtering Method ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Wear Condition

In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.

Research on the Fault Diagnosis System for ICM

2013 ◽  
Vol 712-715 ◽  
pp. 2055-2058
Author(s):  
Zhong Nian Li ◽  
Lei Zhou
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Fault Diagnosis ◽  
Processing Speed ◽  
Fault Tolerant ◽  
Principal Factor ◽  
Diagnosis System ◽  
Principal Factor Analysis ◽  
Diagnosis Rate ◽  
Fault Diagnosis System

In the paper, researched a fault diagnosis system which is used in the ICM (Intelligent Coiling Machine)successfully, it is a kind of fault diagnosis system that uses compactwavelet neural network wavelet neural network as the intelligent core and has the PFA(Principal Factor Analysis) pretreatment function. Through innovative designing and carefully plotted monitoring ways and methods in the system, as a result the fault diagnosis rate of accuracy is high, fault-tolerant ability is strong, the processing speed is quick, and the system work safely and reliably.It has achieved the anticipated goal and the effect.

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