Audio signal analysis for tool wear monitoring in sheet metal stamping

2017 ◽  
Vol 85 ◽  
pp. 809-826 ◽  
Author(s):  
Indivarie Ubhayaratne ◽  
Michael P. Pereira ◽  
Yong Xiang ◽  
Bernard F. Rolfe
Keyword(s):  
Tool Wear ◽  
Sheet Metal ◽  
Signal Analysis ◽  
Audio Signal ◽  
Tool Wear Monitoring ◽  
Sheet Metal Stamping ◽  
Metal Stamping ◽  
Wear Monitoring ◽  
Audio Signal Analysis

Optimisation of Die Radius Geometry in Sheet Metal Stamping

2011 ◽  
Vol 337 ◽  
pp. 350-353 ◽  
Author(s):  
Xuan Zhi Wang ◽  
S.H. Masood
Keyword(s):  
Tool Wear ◽  
Sheet Metal ◽  
High Strength Steels ◽  
High Strength ◽  
Sheet Metal Stamping ◽  
Advanced High Strength Steels ◽  
Metal Stamping ◽  
Contact Pressures ◽  
Abaqus Software

Advanced high strength steels (AHSS) are increasingly utilised in sheet metal stamping in the automotive manufacture. In comparison with conventional steels, AHSS stampings produce higher contact pressures at the interface between the tool-workpiece interface, leading to more severe wear conditions, particularly at the draw die radius. To minimise tool wear using this approach it would be necessary to optimise the shape for a particular combination of circular and high elliptical profiles. This paper presents a methodology to optimise a die radius profile. For this, a specialised software routine is developed and compiled for optimisation of die radius profiles to minimise or achieve uniform contact pressure (wear distribution) using Python computer programming language supported by Abaqus software. A detailed algorithm for the optimisation is explained. A case study based on the algorithm is also discussed.

514 Tool-Wear Monitoring for Sheet Metal Punching Based on Wavelet Analysis of Die Acceleration Response

2014 ◽  
Vol 2014.52 (0) ◽  
pp. _514-1_-_514-2_
Author(s):  
Arata MASUDA ◽  
Yuki TAKAHASHI ◽  
Takashi IIZUKA ◽  
Morimasa NAKAMURA ◽  
Daisuke IBA ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Tool Wear ◽  
Sheet Metal ◽  
Tool Wear Monitoring ◽  
Acceleration Response ◽  
Wear Monitoring

A Study on Tool Wear of Sheet Metal Stamping Die Using Numerical Method

2010 ◽  
Vol 654-656 ◽  
pp. 346-349
Author(s):  
Xuan Zhi Wang ◽  
Syed H. Masood
Keyword(s):  
Tool Wear ◽  
Sheet Metal ◽  
High Strength Steels ◽  
High Strength ◽  
Control Parameters ◽  
Sheet Metal Stamping ◽  
Advanced High Strength Steels ◽  
Stamping Die ◽  
Metal Stamping ◽  
Metal Blank

Advanced high strength steels (AHSS) are increasingly used in sheet metal stamping in the automotive industry. In comparison with conventional steels, advanced high strength steel (AHSS) stampings produce higher contact pressures at the interface between draw die and sheet metal blank, resulting in more severe wear conditions, particularly at the draw die radius. The prediction of tool wear patterns for sheet metal stamping die is a highly challenging task as there are many control parameters involved in the production. This paper presents a numerical simulation methodology to analyse the influences of various control parameters on tool wear patterns of a sheet metal stamping die with different die radius arc profiles. The results of tool wear patterns provide informative guidelines for on-site production.

scholarly journals Time series analysis of tool wear in sheet metal stamping using acoustic emission

2017 ◽  
Vol 896 ◽  
pp. 012030 ◽  
Author(s):  
V. Vignesh Shanbhag ◽  
P. Michael Pereira ◽  
F. Bernard Rolfe ◽  
N Arunachalam
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Sheet Metal ◽  
Sheet Metal Stamping ◽  
Series Analysis ◽  
Metal Stamping

scholarly journals Tool Flank Wear Estimation using Emitted Sound Signal Analysis by PCA – SER Based Peak to Peak Measurements

2019 ◽  
Vol 8 (6) ◽  
pp. 5212-5216
Keyword(s):  
Tool Wear ◽  
Signal Analysis ◽  
Industry 4.0 ◽  
Flank Wear ◽  
Optimization Techniques ◽  
Tool Wear Monitoring ◽  
Tool Flank Wear ◽  
Wear Monitoring ◽  
Novel Method ◽  
Selection Of

The higher levels degrees of automation for industry 4.0 standards require optimization techniques in production activities including tool wear monitoring. The unmonitored tool may spoil the product if it is worn out more than the permitted levels or micro broken or cracked internally. A novel method suggested in this work utilizes neither extra ordinary calculation nor complex mathematical transformations in tool wear monitoring. This method follows no video capturing and image processing rather follows a simple sound wave monitoring captured at the time conversion process by a microphone. The SER a PCA variant technique with the purpose of used in selecting simply the higher velocity of principal components (PCs) in quantifying the feature extracted while separating noise from sound signals. A SER method is used for the selection of suitable PCs for consideration. The best methods of normalization suitable for the SER method is found and implemented the PCA-SER on signals after filter the signals by butter worth filter to remove noise. This proposed procedure resulted in wide differences and proper annotation in differentiating the degree of tool wear in fresh, slight and severely worn categories.

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