Predicting Machining Stability with a Quantum Regression Model

• URL: http://arxiv.org/abs/2412.04048v1
• Date: Thu, 05 Dec 2024 10:38:57 GMT
• Title: Predicting Machining Stability with a Quantum Regression Model
• Authors: Sascha Mücke, Felix Finkeldey, Nico Piatkowski, Tobias Siebrecht, Petra Wiederkehr,
• Abstract summary: We propose a novel quantum regression model by extending the Real-Part Quantum SVM. We show that the resulting model predicts the stability limits observed in our physical setup accurately.
• Score: 1.5833466939207899
• License:
• Abstract: 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.

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