Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Choi, YoungRok; Narayanaswami, Ranga; Chandra, Abhijit

doi:10.1007/s00170-003-1898-1

Cited by 33 publications

(11 citation statements)

References 17 publications

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“…Recent studies deliver quantifiable evidence that wear leads to an increase in energy consumption to perform the same task [28]. To monitor the process of wear, among other things technologies from aviation industries are applied [29].…”

Section: C) Wearmentioning

confidence: 99%

An energy wastage identification and prevention approach for the manufacturing industry

Cremer¹,

Bleider²,

Scheibmayer³

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Avoiding energy wastage is a key element to stay competitive for manufacturing companies in the future. This paper presents an energy wastage identification and prevention approach for manufacturing environments. The approach describes the occurring types of energy wastage within manufacturing and specific indicators to identify each of them. Including additional external information, the significance of an identified on-going wastage can be determined and based on the type of wastage and the significance, concrete recommendations for measures to prevent the wastage are delivered. The approach provides a basis for a detailed energy efficiency concept adaptable to a specific company. The information processing procedures described in this approach are designed to be implemented with complex event processing technology enabling real-time information processing and quick reactions to prevent energy wastage.

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Section: C) Wearmentioning

confidence: 99%

An energy wastage identification and prevention approach for the manufacturing industry

Cremer¹,

Bleider²,

Scheibmayer³

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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show abstract

“…There are studies that deliver quantifiable evidence that wear leads to an increase in energy consumption while producing similar parts [25]. Energy monitoring is applied in aerospace industries.…”

Section: Wearmentioning

confidence: 99%

An energy monitoring based approach to predict energy wastage in the manufacturing industry

Stich

Schmitz

Nienke

et al. 2015

eChallenges E-2015 Conference

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Increasing the energy efficiency and meanwhile avoiding unplanned maintenance breaks are keys for manufacturing companies to stay competitive in the future. This paper presents an energy saving and maintenance cost reducing approach for manufacturing environments. The approach describes first occurring types of energy wastage within manufacturing and characterizes them in more detail. Including additional external information, the significance of an identified on-going wastage can be determined. Based on the type of wastage and the significance; concrete recommendations for measures to prevent the wastage are delivered. The identified wastage facilitates detecting inefficient operating mode as well as wearing and malfunctioning at machines. By using complex event processing technologies realtime information can forwarded directly to the responsible persons to enable quick reactions to prevent energy wastage and unplanned downtimes. The paper presents an approach to identify detection and propose concepts for manufacturing enterprises. The information processing procedure is used for the implementation of two Use Cases.

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“…Du [13] et al recognized the tool wear state through fuzzy decision trees, fuzzy linear function, fuzzy C-means algorithm and BP neural network, the identification rates of various methods showed that the fuzzy decision tree and fuzzy linear function were superiors. Choi [14] et al characterized the trend of cutting force using the approximate amount in the db5 wavelet analysis, and assessed tool wear state through the trend of cutting force. Kuljanic and Sortino [15] defined the normalized cutting force (NCF) indicator and the torque-force distance (TFD) indicator by cutting forces, and characterized the tool wear state through these two indicators.…”

Section: Introductionmentioning

confidence: 99%

Tool flank wear recognition based on the variation of milling force vector in end milling

Hou

Zhang

Luo

et al. 2014

2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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In the manufacturing process, the cutting tool wear is an important affecting factor of the product quality. Tool wear condition monitoring is an effective means to ensure the workpiece quality, and to improve the tool life. Tool flank wear will directly lead to the variation of milling force. Therefore, a flank wear recognition approach of flat end milling tool based on the influence of the tool wear on the milling force vector is proposed in this paper. In this approach, the friction effect force and the cutting force of milling tool are treated separately, the milling force model of flat end milling tool is established, and it is believed that the milling forces of the tool without flank wear are not influenced by the friction effect. The influence of the milling tool flank wear on the milling force vector variation is investigated, and this influence relationship is adopted to recognize the flank wear of flat end milling tool. Finally, the superalloy material is used to perform the wear milling experiment on the CNC machine tool. The experiment results show that, this approach can recognize the milling tool flank wear efficiently and accurately.

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Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Cited by 33 publications

References 17 publications

An energy wastage identification and prevention approach for the manufacturing industry

An energy wastage identification and prevention approach for the manufacturing industry

An energy monitoring based approach to predict energy wastage in the manufacturing industry

Tool flank wear recognition based on the variation of milling force vector in end milling

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