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The application of mechanistic cutting force models for robotic deburring

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Abstract

In this paper, the performances of two cutting force models are compared alongside two methods for identifying their empirical parameters. The models’ performances are evaluated with the aim of determining which model is suited to the prediction of forces generated during the operations with features sizes less than 1 mm. The models’ empirical parameters were then identified using a linear regression on collected force data and a simplex search-based optimization method that minimized the error between the model output and the measured data. The models’ predictions were compared to experimental results gathered from several shallow milling passes conducted at a variety of tool immersions. The models were used to produce force estimates based on measured milling parameters and depth of cut estimates based on the measured forces. The simplex search method was shown to be the most effective of the identification techniques. In full immersion tests, the linear model trained using the linear regression method performed best. However, in low material removal rate partial immersion tests, which more closely resemble deburring operations, the exponential model trained via simplex search outperformed the linear model by nearly two orders of magnitude.

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Funding

This work received support from the Natural Sciences and Engineering Research Council of Canada (NSERC) grants RGPIN-2017-06967, RGPIN-2015-04169, and CRDPJ 514258-17.

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Authors and Affiliations

  1. Carleton University Faculty of Engineering and Design, Ottawa, Ontario, Canada

    Grael Miller, Rishad A. Irani & Mojtaba Ahmadi

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  1. Grael Miller
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  2. Rishad A. Irani
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  3. Mojtaba Ahmadi
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Correspondence to Grael Miller.

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No ethical approval required for the experiments conducted, as no human or animal participants involved.

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The authors are unaware of any conflict of interest.

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Author contribution

Grael Miller: conceptualization, methodology, validation, writing original draft, software. Rishad Irani: review, conceptualization, methodology, validation, writing—review and editing. Mojtaba Ahmadi: review, conceptualization, methodology, validation, writing—review and editing.

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Raw data is protected under IP agreements associated with CRDPJ 514258-17.

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Miller, G., Irani, R.A. & Ahmadi, M. The application of mechanistic cutting force models for robotic deburring. Int J Adv Manuf Technol 115, 199–212 (2021). https://doi.org/10.1007/s00170-021-07070-x

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