Real Time Tool Wear Condition Monitoring in Hard Turning of Inconel 718 Using Sensor Fusion System

2017 ◽  
Vol 4 (8) ◽  
pp. 8605-8612 ◽  
Author(s):  
Rahul Mali ◽  
M.T. Telsang ◽  
T.V.K. Gupta
Keyword(s):  
Tool Wear ◽  
Real Time ◽  
Sensor Fusion ◽  
Condition Monitoring ◽  
Inconel 718 ◽  
Hard Turning ◽  
Fusion System ◽  
Wear Condition

scholarly journals Tool Condition Monitoring in Hard Turning of Inconel 718 by using Vibration Technique

2019 ◽  
Vol 8 (11) ◽  
pp. 1143-1147
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Inconel 718 ◽  
Hard Turning ◽  
High Hardness ◽  
Tool Condition Monitoring ◽  
Depth Of Cut ◽  
Vibration Signals ◽  
Tool Condition ◽  
Coated Carbide

In various machining processes, the vibration signals are studied for tool condition monitoring often referred as wear monitoring. It is essential to overcome unpredicted machining trouble and to improvise the efficiency of the machine. Tool wear is a vital problem in materials such as nickel based alloys as they have high hardness ranges. Though they have high hardness, a nickel based alloy Inconel 718 with varying HRC (51, 53, and 55), is opted as work material for hard turning process in this work. Uncoated and coated carbide tools are employed as cutting tools. Taguchi’s L9 orthogonal array is considered by taking hardness, speed, feed and depth of cut as four input parameters, the number of experiments and the combinations of parameters for every run is obtained. The vibration signals are recorded at various stages of cutting, till the tool failure is observed. Taking this vibration signal data as input to ANOVA and Grey relation analysis (GRA) which categorizes the optimal and utmost dominant features such as Root Mean Square (RMS), Crest Factor (CF), Skewness (Sk), Kurtosis (Ku), Absolute Deviation (AD), Mean, Standard Deviation (SD), Variance, peak, Frequency and Time in the tool wear process

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

2014 ◽  
Vol 541-542 ◽  
pp. 1419-1423 ◽  
Author(s):  
Min Zhang ◽  
Hong Qi Liu ◽  
Bin Li
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Shannon Entropy ◽  
Spindle Motor ◽  
Face Milling ◽  
Motor Current ◽  
Milling Tool ◽  
Spindle Motor Current ◽  
Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

Online tool wear condition monitoring using binocular vision

2017 ◽  
Vol 59 (4) ◽  
pp. 203-210 ◽  
Author(s):  
Aibin Zhu ◽  
Dayong He ◽  
Jianwei Zhao ◽  
Hongling Wu
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Binocular Vision ◽  
Online Tool ◽  
Wear Condition
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