scholarly journals Mathematical modelling of dominant features identification for tool wear monitoring in hard turning by using Acoustic emission

2021 ◽  
Vol 12 (2) ◽  
pp. 935-943
Author(s):  
D. Kondala Rao, Et. al.
Keyword(s):  
Mathematical Modeling ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Simulated Data ◽  
Information Criterion ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Wear Monitoring ◽  
Grey Relational ◽  
Coated Carbide

In machining processes generally tool wear will be obtained with varying proportions. In the present work, the number of dominant features, which affect the tool wear, are studied and computed on Inconel 718 as work material with varying hardness (51, 53&55HRC) levels. The condition monitoring was done on three tools namely uncoated carbide, coated carbide and ceramic tools. By using L9 Taguchi’s orthogonal array, speed, feed, depth of cut (DOC) and hardness are considered as input operating parameters. By indirect method of Acoustic emission (AE) technique, signals were collected using Lab VIEW software and dominating features were calculated using the MATLAB. The features were trained in neural network and got the relation between tool wear, surface roughness, temperature and features. The simulated data was analyzed by Grey relational analysis (GRA) and the dominating features ranking sequence was obtained   for all the three tools and same ranking was also observed with ANOVA. Since there are no common influencing features among these three tools and hence further investigation continued with statistical mathematical modeling. With Akaike information criterion a mathematical model is developed to find the dominant features. By mathematical modeling the sequence in evaluating tool wear was found to be Kurtosis, Frequency, Variance, Mean and RMS and also a relation between tool wear and dominant features was developed which can be readily used by layman for calculating the tool wear.

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 574-577
Author(s):  
Lu Zhang ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Xiao Liang Feng
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Time Frequency ◽  
Svm Model ◽  
The Status ◽  
Wear Monitoring

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

Tool Wear Monitoring at Turning

1994 ◽  
Vol 116 (4) ◽  
pp. 521-524 ◽  
Author(s):  
E. Waschkies ◽  
C. Sklarczyk ◽  
K. Hepp
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Noise Signal ◽  
Depth Of Cut ◽  
Tool Wear Monitoring ◽  
Small Depth ◽  
Wear Monitoring ◽  
Flank Face ◽  
Automatic Tool ◽  
Finishing Processes

A new method for automatic tool wear monitoring at turning has been developed based on the analysis of the continuous acoustic emission (machining noise) generated by the tool during machining. Different wear types (wear of tool flank face and tool chipping) result in changes in the different characteristic values of the noise signal. In case of a uniform abrasion of the insert, e.g., flank face or crater wear, an increased mean signal level is observed, whereas for microbreakage at the edge, an increase of the crest factor with nearly constant mean signal level is found. The burst-like signals from collision between chip and tool and from chip breakage have to be eliminated from analysis to avoid the distortion of the signal parameters of the continuous acoustic emission. This method should be well suited especially for monitoring of finishing processes (small depth of cut).

scholarly journals Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis

2019 ◽  
Vol 69 (4) ◽  
pp. 1-8
Author(s):  
Dasari Kondala Rao ◽  
Kolla Srinivas
Keyword(s):  
Tool Wear ◽  
Inconel 718 ◽  
Hard Turning ◽  
High Hardness ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Absolute Deviation ◽  
Vibration Signals ◽  
Wear Process ◽  
Coated Carbide

AbstractIn 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 carbide, coated carbide and ceramic 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.

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

scholarly journals Investigation of AlCrN-Coated Inserts on Cryogenic Turning of Ti-6Al-4V Alloy

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1338
Author(s):  
Lakshmanan Selvam ◽  
Pradeep Kumar Murugesan ◽  
Dhananchezian Mani ◽  
Yuvaraj Natarajan
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Physical Vapor Deposition ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machined Surface ◽  
Coated Carbide ◽  
Cryogenic Conditions

Over the past decade, the focus of the metal cutting industry has been on the improvement of tool life for achieving higher productivity and better finish. Researchers are attempting to reduce tool failure in several ways such as modified coating characteristics of a cutting tool, conventional coolant, cryogenic coolant, and cryogenic treated insert. In this study, a single layer coating was made on cutting carbide inserts with newly determined thickness. Coating thickness, presence of coating materials, and coated insert hardness were observed. This investigation also dealt with the effect of machining parameters on the cutting force, surface finish, and tool wear when turning Ti-6Al-4V alloy without coating and Physical Vapor Deposition (PVD)-AlCrN coated carbide cutting inserts under cryogenic conditions. The experimental results showed that AlCrN-based coated tools with cryogenic conditions developed reduced tool wear and surface roughness on the machined surface, and cutting force reductions were observed when a comparison was made with the uncoated carbide insert. The best optimal parameters of a cutting speed (Vc) of 215 m/min, feed rate (f) of 0.102 mm/rev, and depth of cut (doc) of 0.5 mm are recommended for turning titanium alloy using the multi-response TOPSIS technique.

A Study on Edge Milling Operation of NEMA G11 GFRP Composites Based on Grey-Taguchi Method

2014 ◽  
Vol 592-594 ◽  
pp. 18-22
Author(s):  
Hari Vasudevan ◽  
Ramesh Rajguru ◽  
Naresh Deshpande
Keyword(s):  
Taguchi Method ◽  
Feed Rate ◽  
Spindle Speed ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Milling Parameters ◽  
Response Table ◽  
Milling Operation ◽  
Grey Relational

Milling is one of the most practical machining processes for removing excess material to produce high quality surfaces. However, milling of composite materials is a rather complex task, owing to its heterogeneity and poor surface finish, which includes fibre pullout, matrix delamination, sub-surface damage and matrix polymer interface failure. In this study, an attempt has been made to optimize milling parameters with multiple performance characteristics in the edge milling operation, based on the Grey Relational Analysis coupled with Taguchi method. Taguchi’s L18 orthogonal array was used for the milling experiment. Milling parameters such as milling strategy, spindle speed, feed rate and depth of cut are optimised along with multiple performance characteristics, such as machining forces and delamination. Response table of grey relational grade for four process parameters is used for the analysis to produce the best output; the optimal combination of the parameters. From the response table of the average GRG, it is found that the largest value of the GRG is for down milling, spindle speed of 1000 rpm, feed rate of 150 mm/min and depth of cut 0.4 mm.

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

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