Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloRoundness prediction in centreless grinding using physics-enhanced machine learning techniques
Anno di pubblicazione2020
FormatoElettronico
Autore/iSafarzadeh, Hossein and Leonesio, Marco and Bianchi, Giacomo and Monno, Michele
Affiliazioni autoriPolitecnico di Milano, CNR-STIIMA, CNR-STIIMA, Politecnico di Milano
Autori CNR e affiliazioni
  • GIACOMO DAVIDE BIANCHI
  • MARCO LEONESIO
Lingua/e
  • inglese
AbstractThis work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.
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RivistaInternational journal, advanced manufacturing technology
Attiva dal 1985
Editore: Springer - London
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0268-3768
Titolo chiave: International journal, advanced manufacturing technology
Titolo proprio: International journal, advanced manufacturing technology.
Titolo abbreviato: Int. j. adv. manuf. technol.
Titoli alternativi:
  • Advanced manufacturing technology (London)
  • International journal of advanced manufacturing technology (Print)
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DOI10.1007/s00170-020-06407-2
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
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Parole chiaveCentreless grinding, Parameter optimization, Machine learning, Neural network, Support vector machine, Gaussian process regression
Link (URL, URI)https://doi.org/10.1007/s00170-020-06407-2
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Strutture CNR
  • STIIMA — Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD008.117.001 : Metodologie e strumenti per lo sviluppo di macchine avanzate
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