Analysis of Sound Signal Generation Due to Flank Wear in Turning

2000 ◽  
Author(s):  
Ming-Chyuan Lu ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Tool Wear ◽  
Manufacturing Systems ◽  
Cutting Process ◽  
Reconfigurable Manufacturing ◽  
Monitoring Tool ◽  
Major Step ◽  
Part Quality ◽  
Audible Sound ◽  
The Individual ◽  
Flank Surface

Abstract Ramp-up is a major step in the implementation of manufacturing systems, and is even more critical in reconfigurable manufacturing systems. For a successful reduction in ramp-up time, it is essential to analyze and monitor both the overall manufacturing system and the individual machine tools/processes that comprise the system. Towards this end, we have addressed the issue of monitoring tool wear using audible sound to enable faulty conditions associated with wear to be identified during the process before the part quality gets out of specification. Audible sound generated from the cutting process is analyzed as a source for monitoring tool wear during turning, assuming adhesive wear as the predominant wear mechanism. The analysis incorporates the dynamics of the cutting process. In modeling the interaction on the flank surface, the asperities on the surfaces are represented as a trapezoidal series function with normal distribution. The effect of changing asperity height, size, spacing, and the stiffness of the asperity interaction is investigated and compared with experimental data.

Analysis of Sound Signal Generation Due to Flank Wear in Turning

2002 ◽  
Vol 124 (4) ◽  
pp. 799-808 ◽  
Author(s):  
Ming-Chyuan Lu ◽  
Elijah Kannatey-Asibu,
Keyword(s):  
Tool Wear ◽  
Flank Wear ◽  
Adhesive Wear ◽  
Cutting Process ◽  
Signal Generation ◽  
Asperity Height ◽  
Monitoring Tool ◽  
Main Effect ◽  
Audible Sound ◽  
Flank Surface

A new concept is introduced to model the main effect of tool wear on system dynamics during stable cutting. Audible sound generated from the cutting process is analyzed as a source for monitoring tool wear during turning, assuming adhesive wear as the predominant wear mechanism. The analysis incorporates the dynamics of the cutting process. In modeling the interaction on the flank surface, the asperities on the surfaces are represented as a trapezoidal series function with normal distribution. The effect of changing asperity height, size, spacing, and the stiffness of the asperity interaction is investigated and compared with experimental data.

Effects of Tool Wear on Surface Roughness and Cutting Force in Thermoplastics Turning

2018 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
János Farkas ◽  
Etele Csanády ◽  
Levente Csóka
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Force ◽  
Cutting Process ◽  
Turning Process ◽  
Monitoring Tool ◽  
Tool Breakage

This paper presents a study of the effects of tool wear on cutting force and surface roughness. The cutting force was measured using a piezoelectric force meter which was attached to the cutting machine's revolving head. The surface roughness was measured after the cutting process was complete using a mechanical touch method. A range of thermoplastic materials and cutting layouts were used to give a broader understanding of the topic. After the measurements were taken, the data were evaluated statistically and the effects of tool wear are illustrated graphically. Furthermore, to understand all of the types of wear which can occur during thermoplastics turning, worn turning inserts taken from industrial machines were examined under a microscope. The aim of the study was to define a method for monitoring tool wear during the turning process to avoid tool breakage and/or reduce the number of scrapped parts.

Gear Hobbing Cutting Process Simulation and Tool Wear Prediction Models

2001 ◽  
Vol 124 (1) ◽  
pp. 42-51 ◽  
Author(s):  
K.-D. Bouzakis ◽  
S. Kombogiannis ◽  
A. Antoniadis ◽  
N. Vidakis
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Prediction Models ◽  
Numerical Models ◽  
High Speed Steel ◽  
Cutting Process ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Gear Hobbing ◽  
The Individual

Gear hobbing is an efficient method to manufacture high quality and performance toothed wheels, although it is associated with complicated process kinematics, chip formation and tool wear mechanisms. The variant cutting contribution of each hob tooth to the gear gaps formation might lead to an uneven wear distribution on the successive cutting teeth and to an overall poor tool utilization. To study quantitatively the tool wear progress in gear hobbing, experimental-analytical methods have been established. Gear hobbing experiments and sophisticated numerical models are used to simulate the cutting process and to correlate the undeformed chip geometry and other process parameters to the expected tool wear. Herewith the wear development on the individual hob teeth can be predicted and the cutting process optimized, among others, through appropriate tool tangential shifts, in order to obtain a uniform wear distribution on the hob teeth. To determine the constants of the equations used in the tool wear calculations, fly hobbing experiments were conducted. Hereby, it was necessary to modify the fly hobbing kinematics, applying instead of a continuous tangential feed, a continuous axial one. The experimental data with uncoated and coated high speed steel (HSS) tools were evaluated, and correlated to analytical ones, elaborated with the aid of the numerical simulation of gear hobbing. By means of the procedures described in this paper, tool wear prediction as well as the optimization of various magnitudes, as the hob tangential shift parameters can be carried out.

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

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