Predicting Machining Stability with a Quantum Regression Model

In this article, we propose a novel quantum regression model by extending the Real-Part Quantum SVM. We apply our model to the problem of stability limit prediction in milling processes, a key component in high-precision manufacturing. To train our model, we use a custom data set acquired by an extensive series of milling experiments using different spindle speeds, enhanced with a custom feature map. We show that the resulting model predicts the stability limits observed in our physical setup accurately, demonstrating that quantum computing is capable of deploying ML models for real-world applications.

  • Veröffentlicht in:
    arXiv
  • Typ:
    Article
  • Autoren:
    Mücke, Sascha; Finkeldey, Felix; Piatkowski, Nico; Siebrecht, Tobias; Wiederkehr, Petra
  • Jahr:
    2024
  • Source:
    https://arxiv.org/abs/2412.04048

Informationen zur Zitierung

Mücke, Sascha; Finkeldey, Felix; Piatkowski, Nico; Siebrecht, Tobias; Wiederkehr, Petra: Predicting Machining Stability with a Quantum Regression Model, arXiv, 2024, https://arxiv.org/abs/2412.04048, Muecke.etal.2024a,

Assoziierte Lamarr-ForscherInnen

lamarr institute person Wiederkehr Petra - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Petra Wiederkehr

Area Chair Industrielle Fertigung zum Profil
Portrait of Sascha Mücke.

Sascha Mücke

Autor zum Profil
LAMARR Person Finkeldey - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Dr. Felix Finkeldey

Scientific Coordinator Industrielle Fertigung zum Profil
Photo. Portrait of Nico Piatkowski

Dr. Nico Piatkowski

Autor zum Profil