Tool condition monitoring in drilling using vibration signature analysis

El-Wardany, Tahany; Gao, Dayong; Elbestawi, M.A.

doi:10.1016/0890-6955(95)00058-5

Cited by 160 publications

(89 citation statements)

References 8 publications

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“…Therefore tool flank wear land width is often used to characterize the tool life. Some work on tool wear focused on prediction of tool wear by empirical modeling methods [2], time series methods [3], frequency domain analysis [4], pattern recognition and statistical methods [5], and hidden markov model method [6] for tool wear monitoring. These studies have gained various degrees of success in tool wear modeling although lots of experimental data are needed.…”

Section: Introductionmentioning

confidence: 99%

Modeling flank wear of carbide tool insert in metal cutting

Luo

Cheng

Holt

et al. 2005

Wear

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In this paper theoretical and experimental studies are carried out to investigate the intrinsic relationship between tool flank wear and operational conditions in metal cutting processes using carbide cutting inserts. A new flank wear rate model, which combines cutting mechanics simulation and an empirical model, is developed to predict tool flank wear land width. A set of tool wear cutting tests using hard metal coated carbide cutting inserts are performed under different operational conditions. The wear of the cutting inset is evaluated and recorded using Zygo New View 5000 microscope. The results of the experimental studies indicate that cutting speed has a more dramatic effect on tool life than feed rate. The wear constants in the proposed wear rate model are determined based on the machining data and simulation results. A good agreements between the predicted and measured tool flank wear land width show that the developed tool wear model can accurately predict tool flank wear to some extent.

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Section: Introductionmentioning

confidence: 99%

Modeling flank wear of carbide tool insert in metal cutting

Luo

Cheng

Holt

et al. 2005

Wear

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“…Kurtosis defined as: (4) Where is average of force and vibration amplitude or mean, . In term of analysis, it found that kurtosis was rather sensitive to the occurrence of spikes or impulses in the time domain of the vibration signal [18]. Skewness (S) is a measure of the asymmetry of the data around the sample mean.…”

Section: Statistical Measurementmentioning

confidence: 99%

Statistical Analysis of Deep Drilling Process Conditions Using Vibrations and Force Signals

et al. 2016

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Abstract. Cooling systems is a key point for hot forming process of Ultra High Strength Steels (UHSS). Normally, cooling systems is made using deep drilling technique. Although deep twist drill is better than other drilling techniques in term of higher productivity however its main problem is premature tool breakage, which affects the production quality. In this paper, analysis of deep twist drill process parameters such as cutting speed, feed rate and depth of cut by using statistical analysis to identify the tool condition is presented. The comparisons between different two tool geometries are also studied. Measured data from vibrations and force sensors are being analyzed through several statistical parameters such as root mean square (RMS), mean, kurtosis, standard deviation and skewness. Result found that kurtosis and skewness value are the most appropriate parameters to represent the deep twist drill tool conditions behaviors from vibrations and forces data. The condition of the deep twist drill process been classified according to good, blunt and fracture. It also found that the different tool geometry parameters affect the performance of the tool drill. It believe the results of this study are useful in determining the suitable analysis method to be used for developing online tool condition monitoring system to identify the tertiary tool life stage and helps to avoid mature of tool fracture during drilling process..

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“…The literature on detecting tool wear has frequently mentioned that a tool's status is detected using vibration signals. ElWardany et al 3 analyzed the wear of drills and cracks by analyzing vibration signals in the time and frequency domains; Issam detected wear and categorized drills based on their vibration signals using a supervised neural network. 4 Chen et al 5 used vibration signals and back propagation in a neural network to diagnose the key parts of a machine, such as its principal axis.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel framework for diagnosing automatic tool changer and tool life based on cloud computing

Chen

Cheng

2016

Advances in Mechanical Engineering

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Tool change is one among the most frequently performed machining processes, and if there is improper percussion as the tool's position is changed, the spindle bearing can be damaged. A spindle malfunction can cause problems, such as a knife being dropped or bias in a machined hole. The measures currently taken to avoid such issues, which arose from the available machine tools, only involve determining whether the clapping knife's state is correct using a spindle and the air adhesion method, which is also used to satisfy the high precision required from mechanical components. Therefore, it cannot be used with any type of machine tool; in addition, improper tapping of the spindle during an automatic tool change cannot be detected. Therefore, this study proposes a new type of diagnostic framework that combines cloud computing and vibration sensors, among of which, tool change is automatically diagnosed using an architecture to identify abnormalities and thereby enhances the reliability and productivity of the machine and equipment.

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Tool condition monitoring in drilling using vibration signature analysis

Cited by 160 publications

References 8 publications

Modeling flank wear of carbide tool insert in metal cutting

Modeling flank wear of carbide tool insert in metal cutting

Statistical Analysis of Deep Drilling Process Conditions Using Vibrations and Force Signals

A novel framework for diagnosing automatic tool changer and tool life based on cloud computing

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