Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis

Chelladurai, H.; Jain, V.K.; Vyas, Nalinaksh S.

doi:10.1007/s00170-007-0986-z

Cited by 63 publications

(27 citation statements)

References 18 publications

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“…In order to avoid the signals problems from the strain gauge, a proper location for mounting the strain gauge was required to assure the accuracy of the signals obtained from the data acquisition system. 18 Conclusions A good agreement has been achieved between FEM simulation and experimental results. By the analysis of main cutting force, F y , and the feed force, F x , for each machining run measured by the strain gauges at locations I and II, it was found that when the strain gauges were mounted at a distance of approximately 47 mm or location II from the cutting point, the distance was the optimum one for sensing an accurate cutting tool deflection or force signals.…”

Section: Experimental Work For Validationsupporting

confidence: 65%

Determination of sensor location for cutting tool deflection using finite element method simulation

Ghani

Jye

Haron

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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The aim of this study was to determine the the optimum locations to mount the sensor for measuring the cutting tool deflection during turning process using finite element method simulation. In this study, stress analysis had been conducted using Autodesk Inventor Professional 2010 integrated with ANSYS software. The simulation results were validated using a strain gauge as the sensor for the detection of cutting force signal during the turning of hardened plain carbon steel JIS S45C using carbide tool. Two strain gauges were mounted on the tool holder at two defined locations I and II, at distances of 37 and 47 mm, respectively, from the cutting point. Only one set of cutting parameters was conducted at spindle speed, N ¼ 1000 rpm, feed, f ¼ 0.25 mm/rev and depth of cut, d ¼ 0.80 mm. The turning process was stopped and the insert discarded when the average flank wear, VB m , reached 0.30 mm. The main cutting force, F y , and the feed force, F x , for each machining run were measured, collected and analysed at locations I and II. It was found that when strain gauges were placed at a distance of approximately 43 mm from the cutting point, it was the optimum location for sensing the cutting force signals.

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Section: Experimental Work For Validationsupporting

confidence: 65%

Determination of sensor location for cutting tool deflection using finite element method simulation

Ghani

Jye

Haron

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…When the non-resistance sparkle circuit is used for machining, it reuses the energy originally bled; thus, it is known as energy-saving circuit, which is to overplay the energy bled by processing resistor with the original energy by conversion, so as to increase the original discharge energy and avoid considering the problem of cooling due to the heat energy produced by resistor [8,9].…”

Section: Description Of Non-resistance Sparkle Circuitmentioning

confidence: 99%

The application of gray theory on wire cut electric discharge machine non-resistance sparkle circuit prediction

Chen

Wang²

2017

Int J Adv Manuf Technol

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The development and research of the green manufacturing technology is relatively important. In order to improve processing efficiency of modern wire cut electric discharge machine (WEDM) and energy saving, a large number of researches and developments of the discharge circuit that is the major and most important technology of wire cutting machine are conducted. Currently, in the most discharge circuits of WEDM adopt the resistor for current-limiting which is the energy block when the resistor is used to turn off the discharge current, resulting in a lot of energies that are converted into heat energy from electrical energy, which are mainly generated by resistor. However, these heat energies will cause the increase on temperature in the discharge circuit, which affects the processing efficiency and energy. Thus, some cooling devices should be arranged around the discharge circuit in order to reduce the temperature. This study adopts the WEDM as laboratory equipment to design and improve the discharge circuit, uses the DC-DC and the electronic voltage regulation technology to convert the original energy bled by the resistor directly into the energy required in machining, and then applies the design of non-resistance sparkle circuit to realize the energy-saving effect. The experiment testing the power consumption differences of WEDM shows that the energy can be saved by 10 to 15% performance.

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“…In this study, the vibration signals are used for the fault categorization. The vibration signals from the semi-automatic lathe will be non-stationary due to the wear and tear occurred during machining [8]. Alonso et al studied the possibilities of vibration signature for monitoring the tool wear [9] Data modeling using machine learning approach are normally employed to solve such problems.…”

Section: Introductionmentioning

confidence: 99%

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

Shantisagar¹,

Jegadeeshwaran²,

Gandhi³

et al. 2019

Structural Durability &Amp; Health Monitoring

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The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques. The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm. The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis. The decision tree model produced the classification accuracy as 94.78% with five selected features. The developed fuzzy model produced the classification accuracy as 94.02% with five membership functions. Hence, the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

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Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis

Cited by 63 publications

References 18 publications

Determination of sensor location for cutting tool deflection using finite element method simulation

Determination of sensor location for cutting tool deflection using finite element method simulation

The application of gray theory on wire cut electric discharge machine non-resistance sparkle circuit prediction

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

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