scholarly journals An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haicheng Yu ◽  
Kun Wang ◽  
Ruhai Zhang ◽  
Xiaojun Wu ◽  
Yulin Tong ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Tool Wear ◽  
Surface Texture ◽  
High Reliability ◽  
Machining Process ◽  
Fast Fourier Transformation ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
Workpiece Surface ◽  
Wear Monitoring

Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.

Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM

2021 ◽  
pp. 1-18
Author(s):  
Xiangyu Zhang ◽  
Lilan Liu ◽  
Xiang Wan ◽  
Bowen Feng
Keyword(s):  
Artificial Intelligence ◽  
Tool Wear ◽  
Real Time ◽  
Online Monitoring ◽  
Machining Process ◽  
Time Behavior ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
The Real ◽  
Wear Monitoring

Abstract The real-time requirements of tool wear states monitoring are getting higher and higher, at the same time, tool wear monitoring lacks a modeling data comprehensive carrier, which hinders its application in the actual machining process. In order to solve this problem, combining the high fidelity real-time behavior simulation characteristics of digital twin(DT) and the powerful data mining capabilities of artificial intelligence, an online tool wear monitoring method based on DT and Stack Sparse Auto-Encoder-Parallel Hidden Markov Model(SSAE-PHMM) was proposed. Firstly, a DT which can reflect the real state of the tool was established, and the tool wear state was predicted by visual display and analysis in the virtual space; Secondly, a tool wear state recognition model based on SSAE-PHMM was established, which can adaptively complete time domain feature extraction. And for each tool wear state, multiple HMM models were combined into a PHMM model to realize accurate recognition of tool wear state. PHMM overcome the defects of poor convergence and long training time of artificial neural network, and greatly improved the performance of classifier. Through the deep integration of DT and artificial intelligence, real-time data-driven tool wear qualitative and quantitative online monitoring was realized, and the effectiveness of this method was verified by experiments.

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.

Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques

2017 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Patricia Iglesias Victoria ◽  
Rui Liu
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Learning Algorithm ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Process Conditions ◽  
Tool Wear Monitoring ◽  
Audible Sound ◽  
Wear Monitoring

The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.

A Tool Wear Monitoring System for Steel and Aluminium Alloys Based on the same Sensor Signals and Decision Strategy

2014 ◽  
Vol 797 ◽  
pp. 17-22 ◽  
Author(s):  
D.R. Salgado ◽  
I. Cambero ◽  
J.M. Herrera ◽  
J. García-Sanz-Calcedo ◽  
Alfonso González González ◽  
...  
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Aluminium Alloys ◽  
Machining Process ◽  
Decision Strategy ◽  
Tool Wear Monitoring ◽  
Cutting Vibrations ◽  
Tool Wear Estimation ◽  
Sensor Signals ◽  
Wear Monitoring

This paper presents a tool wear monitoring system that uses the same signals and prediction strategy for monitoring the machining process of different materials, i.e., a steel and an aluminium alloy. It is an important requirement for a monitoring system to be applied in real applications. Experiments have been performed on a lathe over a range of different cutting conditions, and TiN coated tools were used. The monitoring signals used are the AC feed drive motor current and the cutting vibrations. The geometry tool parameters used as inputs are the tool angle and the radius. The performance of the proposed system was validated against different experiments. In particular, different tests were performed using different numbers of experiments obtaining a rmse for tool wear estimation of 17.63 μm and 13.45 μm for steel and aluminium alloys respectively.

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