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Analysis of institutional authors

Perez-Bernabeu, EAuthorSelles, MaAuthor

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October 30, 2024
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Proceedings Paper
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A neural network approach for chatter prediction in turning

Publicated to: Procedia Manufacturing. 34 885-892 - 2019-01-01 34(), DOI: 10.1016/j.promfg.2019.06.159

Authors:

Cherukuri, Harish; Perez-Bernabeu, E; Perez-Bernabeu, E; Selles, M A; Selles, M A; Schmitz, Tony L; Schmitz, Tony L
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Affiliations

Univ N Carolina, Mech Engn & Engn Sci - Author
Univ Politecn Valencia - Author

Abstract

Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit self-excited vibration. In this paper, an artificial neural network (ANN) is applied to model turning stability. The analytical stability limit is used to generate a data set that trains the ANN. It is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training. (C) 2019 The Authors. Published by Elsevier B.V.
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Keywords

ChatterChatter predictionsClassificationIndustrial researchMachine learningMachiningMachining processManufactureMaterial removal rateModelNeural networkNeural networksOperationSelf-excited vibrationsSpindle speedStabilityStability analysisStability behaviorStability limitToolTraining pointsTurningVibrationWorkpiece

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Procedia Manufacturing due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.09, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-02, the following number of citations:

  • WoS: 25
  • Scopus: 32
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-02:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 86 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/10251/201888
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.

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Awards linked to the item

The authors gratefully acknowledge financial support from the UNC ROI program. Elena Perez-Bernabeu and Miguel Selles also acknowledge support from Universitat Politenica de Valencia (PAID-00-17). Additionally, some of the neural net figures and the 10-fold cross validation figures are based on the TikZ codes provided on StackExchange-TeX by various users. Harish Cherukuri would like to thank them for their valuable advice.
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