CSAM Project

North Carolina Consortium for Self-Aware Machining and Metrology (CSAM)

Funded by the University of North Carolina General Administration

Link to main CSAM collaboration website

About the project

The overall goal of manufacturing is to provide high quality products in the most cost-effective way possible. The presence of chatter (instability) has shown to decrease the surface quality of a product, ultimately making the process more expensive for the manufacturer. Tool chatter is a function of force and cutting angle. It can either be minor or major and can severly impact the surface quality of a product. Chatter can also contribute to wear and tear on tool pieces themselves. The goal of this project is to predict, given a particular cutting angle and force, whether the tool will be in the stable region or not.

We have a mathematical hypothesis of what that curve looks like, which can be seen in Fig. 1, below. This mathematical hypotheis is called a stability lobe diagram (SLD). One of the bigger problems encountered in real-world applications is that it is expensive and time consuming to test the tools to generate the force and cutting angle data that we need to make our predictions. While we wait for the engineers to generate real-world data, we have generated simulated samples, and will use those to make predictions in the interim. Fig. 2 is an example of our simulated random data.We have been investigating several machine learning methods in an effort to make our predictions.

  • Artifical Neural Network
  • Support Vector Machine
  • Random Forest
  • XgBoost
  • AdaBoost


Figure 1: Hypothetical SLD Curve showing the stable region in teal, and the unstable region in white.


Figure 2: simulated random data points