Predicting Machining Stability with a Quantum Regression Model

TL;DR

This paper introduces a quantum regression model based on an extension of Quantum SVM to predict machining stability limits in milling, demonstrating quantum ML's practical application in manufacturing.

Contribution

The paper presents a novel quantum regression model tailored for stability prediction in milling, utilizing a custom dataset and feature map for high-precision manufacturing.

Findings

The quantum regression model accurately predicts stability limits.

Quantum computing can effectively deploy ML models in real-world manufacturing.

The model outperforms traditional methods in stability prediction.

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.