Sensing tool breakage in face milling with a neural network

Tarng, Y.S.; Hseih, Y. W.; Hwang, S.T.

doi:10.1016/0890-6955(94)90004-3

Cited by 51 publications

(18 citation statements)

References 6 publications

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“…There has been significant interest in the monitoring and study of face milling, and a wide range of approaches to TCM have been proposed and tested [8,[18][19][20][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Tcm Developments In Face Millingmentioning

confidence: 99%

“…The measurement of cutting forces in face milling is quite common, both for the purposes of TCM and for the detection, prevention and control of chatter [8,19,29,[33][34][35][36][37][38][39][40][41][42][43]. The force signals are most often collected by workpiece table dynamometers.…”

Section: Tcm Developments In Face Millingmentioning

confidence: 99%

“…Rather than using the instantaneous readout from a dynamometer, and incurring the inherent risk of multiple false alarms, the force over a tooth period is averaged in [29,41]. This also mitigates the difficulty of comparing two adjacent teeth, which is complicated by errors in tooth pitch and encoder spacing.…”

Section: Tcm Developments In Face Millingmentioning

confidence: 99%

“…There are numerous instances of cutting force measurement [8,[33][34][35][36][37][38][39][40][41], although much of this work does not involve advanced signal processing. There is not as much interest in time-frequency methodologies in end milling as there is in other areas.…”

Section: Conclusion On Research In Face Milling Tcmmentioning

confidence: 99%

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State-of-the-art methods and results in tool condition monitoring: a review

Rehorn

Jiang

Orban³

2005

Int J Adv Manuf Technol

337

118

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This paper presents a review of the state-of-the-art in sensors and signal processing methodologies used for tool condition monitoring (TCM) systems in industrial machining applications. The paper focuses on the technologies used in monitoring conventional cutting operations, including drilling, turning, end milling and face milling, and presents important findings related to each of these fields. Unlike existing reviews, which categorize results according to the methodology used, this paper presents results organized according to the type of machining operation carried out. By extensively reviewing and categorizing over one hundred important papers and articles, this paper is able to identify and comment on trends in TCM research, and to identify potential weaknesses in certain areas. The paper concludes with a list of recommendations for future research based on the trends and successful results observed, thus facilitating the cross-fertilization of ideas and techniques within the field of TCM research.

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Section: Tcm Developments In Face Millingmentioning

confidence: 99%

Section: Tcm Developments In Face Millingmentioning

confidence: 99%

Section: Tcm Developments In Face Millingmentioning

confidence: 99%

Section: Conclusion On Research In Face Milling Tcmmentioning

confidence: 99%

See 2 more Smart Citations

State-of-the-art methods and results in tool condition monitoring: a review

Rehorn

Jiang

Orban³

2005

Int J Adv Manuf Technol

337

118

View full text Add to dashboard Cite

show abstract

“…al. [15], Masory [16], Tansel et al [17], and Tarng et al [18] have employed BP or Adaptive Resonance Theory (ART2) on the neural networks for monitoring tool wear and breakage in turning or drilling processes. On the other hand, Tansel et al [19], and Lee et.…”

Section: Elk Asia Pacific Journals -Special Issue Isbn: 978-81-930411mentioning

confidence: 99%

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

2015

International Conference on Advancements and Recent Innovations in Mechanical, Production and Industrial Engineering

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Abstract-Electro discharge machining (EDM) is anunconventional electro thermal machining process used for manufacturing geometrically complex or hard material parts that are extremely difficult-tomachine by conventional machining process. Modelling of the EDM process provides a functional relationship between operating parameters and output parameters (process performance index) of EDM process. This is the scientific way to study quantitatively EDM process behaviour which helps to get the better understanding of the process. This paper reviews various research work carried out within past decades for the development of EDM process. The study is mainly focused on aspects related to the experimental modelling of EDM process using Artificial Neural Network (ANN) and Response Surface Methodology (RSM).

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