I-KazTM-Based Analysis of Cutting Force Signals for Tool Condition Monitoring in Turning Process

2013 ◽  
Vol 471 ◽  
pp. 203-207
Author(s):  
Muhammad Rizal ◽  
Jaharah A. Ghani ◽  
Mohd Zaki Nuawi ◽  
Che Hassan Che Haron
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Machined Surface ◽  
Tool Condition ◽  
Feed Force ◽  
The Status ◽  
Force Signal

Cutting force is an important signal in machining process and has been widely used for tool condition monitoring. Monitoring the condition of the cutting tool in the machining process is very important to maintain the machined surface quality and consequently reduce inspection costs and increase productivity. This paper utilizes I-kaz-based analysis of cutting force signal to monitor the status of tool wear. The cutting force signals are measured by two channels of strain gauge that were mounted on the surface of tool holder. Experiments were carried out by turning hardened carbon steel and cutting force signals were analyzed using I-kazTM technique by integrating two component of signals (I-kaz 2D, Z2), I-kaz of cutting force (Z of Fy), and I-kaz of feed force (Z of Fx). The results show that I-kaz of feed force can be effectively used to monitor tool wear progression during turning operation.

A Comparative Study of I-kaz Based Signal Analysis Techniques: Application to Detect Tool Wear during Turning Process

2013 ◽  
Vol 66 (3) ◽  
Author(s):  
Muhammad Rizal ◽  
Jaharah A. Ghani ◽  
Mohd Zaki Nuawi ◽  
Mohamad Amir Shafiq r Mohd Tahir ◽  
Che Hassan Che Haron
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Flank Wear ◽  
Multilevel Methods ◽  
Machining Process ◽  
Turning Process ◽  
Feed Force ◽  
Machined Parts ◽  
Force Signal ◽  
Analysis Of Results

Detection of tool wear during in-progress machining process is a significant requirement to assure the quality of machined parts that helps to improve the productivity. The cutting force is one of the signals in machining process that has been widely used for tool wear monitoring. In the present paper three derived I-kazTM based methods explained and compared for monitoring tool wear changes during turning process. The aim of this work is to study the performance of I-kazTM, I-kaz 2D and I-kaz Multilevel techniques to detect flank wear width using the cutting force signal. The experiments were carried out by turning hardened carbon steel, and cutting force signals were measured by two channels of strain gauges that were mounted on the surface of tool holder. The analysis of results using I-kaz 2D, I-kazTM and also I-kaz Multilevel methods, revealed that all methods can applied to determine tool wear progression during turning process and feed force signal change is very significant due to flank wear.

scholarly journals Tool Wear Inspection and Classification by Artificial Neural Network Based on Vision System and Cutting Force Signal.

2021 ◽  
Author(s):  
Ahmed Abdeltawab ◽  
Xi Zhang ◽  
Longjia Zhang ◽  
Chuanjun Li
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Force ◽  
Condition Monitoring ◽  
Time Domain ◽  
Vision System ◽  
Tool Condition Monitoring ◽  
Ann Model ◽  
Tool Condition ◽  
Milling Tool

Abstract The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and flank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

scholarly journals Algorithm of the tool condition monitoring system based on many neural networks

Mechanik ◽  
2017 ◽  
Vol 90 (3) ◽  
pp. 220-223
Author(s):  
Sebastian Bombiński ◽  
Joanna Kossakowska
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Rbf Neural Networks ◽  
Standard Time ◽  
Tool Condition ◽  
Condition Monitoring System

Presented is a comparison of different methods of estimating tool wear – obtained for group of RBF neural networks, hierarchical methods and the standard time counting. The analysis of the signals from the machining process carried out for three different experiments, clearly demonstrating the effect of presented methods. The results obtained for group of RBF neural networks are similar to results obtained for hierarchical methods.

scholarly journals Acoustic Emission Studies on Carbon/Phenolic Ablative Composites using PCD and PCBN Tools

1999 ◽  
Vol 8 (3) ◽  
pp. 096369359900800 ◽  
Author(s):  
P. S. Sreejith ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
The Status ◽  
Emission Studies ◽  
Pcbn Tools ◽  
Cutting Speeds ◽  
Phenolic Composite

During manufacturing, the performance of a cutting tool is largely dependent on the conditions prevailing over the tool-work interface. This is mostly dependent on the status of the cutting tool and work material. Acoustic emission studies have been performed on carbon/phenolic composite using PCD and PCBN tools for tool condition monitoring. The studies have enabled to understand the tool behaviour at different cutting speeds.

Study on the cutting performance in machining assembled hardened steel workpiece with torus cutter

2019 ◽  
Vol 234 (4) ◽  
pp. 701-709
Author(s):  
Tao Chen ◽  
Weijie Gao ◽  
Guangyue Wang ◽  
Xianli Liu
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Surface Quality ◽  
Wear Mechanism ◽  
Hardened Steel ◽  
Cutting Performance ◽  
Machining Process ◽  
Machined Surface ◽  
Machined Surface Quality ◽  
Two Sides

Torus cutters are increasingly used in machining high-hardness materials because of high processing efficiency. However, due to the large hardness variation in assembled hardened steel workpiece, the tool wear occurs easily in machining process. This severely affects the machined surface quality. Here, we conduct a research on the tool wear and the machined surface quality in milling assembled hardened steel mold with a torus cutter. The experimental results show the abrasive wear mechanism dominates the initial tool wear stage of the torus cutter. As the tool wear intensifies, the adhesive wear gradually occurs due to the effect of alternating stress and impact load. Thus, the mixing effect of the abrasive and adhesive wears further accelerates tool wear, resulting in occurrence of obvious crater wear band on the rake face and coating tearing area on the flank face. Finally, the cutter is damaged by the fatigue wear mechanism, reducing seriously the cutting performance. With increase of flank wear, moreover, there are increasingly obvious differences in both the surface morphology and the cutting force at the two sides of the joint seam of the assembled hardened steel parts, including larger height difference at the two sides of the joint seam and sudden change of cutting force, as a result, leading to decreasing cutting stability and deteriorating seriously machined surface quality.

scholarly journals Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 6400-6410 ◽  
Author(s):  
Juan C. Jauregui ◽  
Juvenal R. Resendiz ◽  
Suresh Thenozhi ◽  
Tibor Szalay ◽  
Adam Jacso ◽  
...  
Keyword(s):  
Cutting Force ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Tool Condition Monitoring ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Tool Condition

Experimental Investigation to Predict Tool Wear and Vibration Displacement in Turning – A Base for Tool Condition Monitoring

2016 ◽  
Vol 16 (2) ◽  
pp. 103-114
Author(s):  
M. Prakash Babu ◽  
Balla Srinivasa Prasad
Keyword(s):  
Mathematical Model ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Cutting Forces ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Depth Of Cut ◽  
Vibration Displacement ◽  
Tool Condition ◽  
Tool Vibrations

AbstractIn the present work investigation primarily focuses on identifying the presence of cutting tool vibrations during face turning process. For this purpose an online non-contact vibration transducer i. e. laser Doppler Vibrometer is used as part of a novel approach. The revisions in the values of cutting forces, vibrations and acoustic optic emission signals with cutting tool wear are recorded and analyzed. This paper presents a mathematical model in an attempt to understand tool lifeunder vibratory cutting conditions. Tool wear and cutting force data are collected in the dry machiningof AISI 1040 steel at different vibrationinduced test conditions. Identifying the correlation among tool wear, cutting forces and displacement due to vibration is a critical task in the present study. These results are used to predict the evolution of displacement and tool wear in the experiment. Specifically, the research tasks include: to provide an appropriate experimental data to prove the mathematical model of tool wear based on the influence of cutting tool vibrations in turning.The modeling is focused on demonstrating the scientific relationship between the process variables such as vibration displacement, vibration amplitude, feedrate, depth of cut and spindle speed while getting into account machine dynamics effect and the effects such as surface roughness and tool wear generated in the operation. Present work also concentrates on the improvement in machinability during vibration assisted turning with different cutting tools. The effect of work piece displacement due to vibration on the tool wear is critically analyzed. Finally, tool wear is established on the basis of the maximum displacement that can be tolerated in a process for an effective tool condition monitoring system.

Tool Condition Monitoring Based on an Adaptive Neurofuzzy Architecture

2004 ◽  
Vol 471-472 ◽  
pp. 196-200 ◽  
Author(s):  
P. Fu ◽  
A.D. Hope ◽  
G.A. King
Keyword(s):  
Condition Monitoring ◽  
Metal Cutting ◽  
Production Efficiency ◽  
Process Condition ◽  
Recognition Algorithm ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Machining Accuracy ◽  
Knowledge Based ◽  
Tool Condition

Metal cutting operations constitute a large percentage of the manufacturing activity. One of the most important objectives of metal cutting research is to develop techniques that enable optimal utilization of machine tools, improved production efficiency, high machining accuracy and reduced machine downtime and tooling costs. Machining process condition monitoring is certainly the important monitoring requirement of unintended machining operations. A multi-purpose intelligent tool condition monitoring technique for metal cutting process will be introduced in this paper. The knowledge based intelligent pattern recognition algorithm is mainly composed of a fuzzy feature filter and algebraic neurofuzzy networks. It can carry out the fusion of multi-sensor information to enable the proposed intelligent architecture to recognize the tool condition successfully.

Sign in / Sign up

Export Citation Format

Share Document