Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system

Aliustaoğlu, Cüneyt; Ertunç, H. Metin; Ocak, Hasan

doi:10.1016/j.ymssp.2008.02.010

Cited by 146 publications

(53 citation statements)

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“…For effective TCM the chosen parameters are very important and an incorrect choice could lead to a poor response [2]. It was emphasized that a single sensor would not be able to provide a reliable result [2,6,8]. During machining, if the tool is progressing towards sudden wear, this results…”

Section: Introductionmentioning

confidence: 99%

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

Nikhare

Conklin

Loker

2017

JMMP

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Abstract:Machining is one of the major manufacturing techniques where the material is removed to prepare the complete or sub-part. In general, this is also referred to as subtractive manufacturing. Due to solid-to-solid contact between the cutting tool and the work-piece, the machine dynamics get influenced by various operating parameters. This generates force and vibration, and thus noise. Over time the cutting tool reaches its end-of-life which increases the force to cut, and thus produces more vibration and noise. The noise parameter was considered in this work. A 32-element spherical microphone array acoustic camera system was used to record and analyze the sound that was emitted during the machining processes. The startup, idle, and load operating characteristics for various industrial machining equipment were monitored with the acoustic beam former microphone system. The industrial applications included a bench grinder, surface grinder, vertical band saw, lathe machine, and vertical milling machine. Analysis of the acoustic noise generated from these processes could demonstrate the similarities between the cyclical patterns of resonating sound.

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Section: Introductionmentioning

confidence: 99%

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

Nikhare

Conklin

Loker

2017

JMMP

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“…State-Of-Health evaluations and assessments of the actual wear state are important advanced features of modern condition monitoring systems [1][2][3][4][5][6][7][8][9]. Evaluations of the hazard rate/probability of failure (load-dependent) and the actual remaining lifetime are goals in the development of advanced methods and implementations [6,10].…”

Section: Introductionmentioning

confidence: 99%

“…Statistical features are used for feature extraction, e.g., root mean square, skewness, kurtosis, and standard deviation. In previous publications, statistical features were also used for condition monitoring of tool wear [2,5] or of diesel engines [13]. More than one filtering method may also be used, as described in [6], where 25 features from the time and frequency domains were classified with a machine learning classification model.…”

Section: Introductionmentioning

confidence: 99%

New Approaches for Supervision of Systems with Sliding Wear: Fundamental Problems and Experimental Results Using Different Approaches

Söffker

Rothe

2017

Applied Sciences

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Reliability and availability of technically complex and safety-critical systems are of increasing importance. Besides the degree of wear, the quality of mechanical systems is significant for the system reliability. The focus of this contribution is the development and application of readily applicable and easily interpretable algorithms for industrial data obtained from technical systems during operation. The methods are within the focus of the production-oriented automation programs (Industrial Internet, Automation 4.0, China 2025). In this contribution as example a hydraulically driven machine in which parts slide over each other is chosen as sliding wear example. Monitoring is applied to distinguish normal and abnormal operation as well as to define end of useful lifetime. In this contribution four different methods will be introduced and experimentally compared without the availability of objective information about the wear state. The approaches differ with respect to the used measurements and data preparation. As measurements Acoustic Emission and the hydraulic pressure of the driving machine are used. For processing the accumulation of damage related values, a machine learning algorithm, and a sensitivity matrix are used. For comparison the experimental validation is based on identical data sets. Different operational states of the system denoted as actual system state are defined and classified. The comparison shows that the four introduced methods provide similar classification results although the underlying measurements are based on different physical principles. The newly introduced approaches allow online evaluation of the actual system state and can serve within improved maintenance strategies.

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“…They employed singular spectrum analysis (SSA) and cluster analysis for analysis of the tool vibration signals.SSA was non-parametric technique, of time series analysis that decomposes the acquired tool vibration signals and Cluster analysis was used to group the SSA decomposition in order to obtain several independent components in the frequency domain and that are apply to feed forward back-propagation (FFBP) neural network to determine the tool flank wear. Aliustaoglu, C. et al [5], studied tool wear condition monitoring using a sensor fusion model based on fuzzy inference system.They mainly concentrated on the drilling and milling operation. They used two stage fuzzy logic schemes for developing the advanced tool condition monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation to Minimize Resultant Vibration Signal in CNC Turing Operation of Hard AISI M2 Tool Steel

Pawar¹

2017

IJRASET

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Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system

Cited by 146 publications

References 1 publication

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

New Approaches for Supervision of Systems with Sliding Wear: Fundamental Problems and Experimental Results Using Different Approaches

Experimental Investigation to Minimize Resultant Vibration Signal in CNC Turing Operation of Hard AISI M2 Tool Steel

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