Tool Wear Classification Using Decision Treesin Stone Drilling Applications: A Preliminary Study

Klaić, Miho; Staroveški, Tomislav; Udiljak, Toma

doi:10.1016/j.proeng.2014.03.125

Cited by 26 publications

(12 citation statements)

References 19 publications

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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%

“…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]. In [11] a new approach is developed to define the useful lifetime of components and systems using only operation data in combination with the information of the end of lifetime (of systems failed used for training of the model used for test).…”

Section: Introductionmentioning

confidence: 99%

“…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. A review of sensor technologies, signal processing, and decision-making strategies was provided in [7], including filtering techniques such as principal component analysis (which is also used in [20]), the fast Fourier transform (FFT), and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Although reaming is finishing technology there can be demand for more precise holes (lower than IT 5 and Ra 0.4), then it is necessary to use the other technological operations like grinding, rolling, honing, etc. [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Influence of Selected Technological Factors on the Hole Quality During Reaming

Fulemová¹,

Kutlwašer²,

Gombár³

et al. 2017

DAAAM Proceedings

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Quality of reamed hole is influenced by many factors. Some of them have higher impact on accuracy and quality of reamed hole and the others have higher impact on tool life of a reamer. This article deals with influence of cutting speed, feed rate, reverse feed rate and eccentricity of the tool tip on cylindricity, surface roughness Ra and diameter of reamed hole. Design of experiment was carried out with CCD (Central Composite Design) with orthogonal blocking. As a tool was used cermet reamer with 6 brazed cutting edges. Results showed not only valuable data but also weaknesses of experiment preparation. The article also point out weaknesses and how to handle with them.

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“…In high-speed processing, the high-speed rotating tools accumulate a large amount of energy and under the great centrifugal force, which will make the tool worn or broken sharply. In order to prevent the loss of workpiece surface quality caused by the excessive cutting-tool wear, avoid the faults such as workpiece scrapped or machine damage caused by tool breakage, the active monitoring technology must be taken in the work cycle for high-speed cutting tools and the wear level must be online recognize and control to ensure completion high-speed machining tasks with high quality, high efficiency and low consumption [4][5][6].…”

Section: Introductionmentioning

confidence: 99%