This notebook explores the concept of Stability Lobe Diagrams (SLDs) and how to generate them using Support Vector Machines (SVMs) and Neural Networks (NNs). The Stability Lobe Diagram is a graphical representation of the stability limits of a milling operation based on the combinations of cutting parameters, such as spindle speed and feed rate.
The dataset used in this notebook is the Milling Data for which is created though Analytical equations of milling [ Dataset ]. It contains sensor data from a milling operation, including spindle speed and feed rate.
The notebook starts with an exploratory data analysis of the milling data, followed by feature engineering and preprocessing. Next, the data is split into training and testing sets, and SVM and NN models are trained on the data.
The SVM model is used to generate the Stability Lobe Diagram, which is plotted based on the model's predictions for different combinations of spindle speed and feed rate. The NN model is used to predict tool wear during the milling operation.
The Stability Lobe Diagram generated by the SVM model shows the stability limits of the milling operation, which can be used to optimize the cutting parameters for maximum efficiency and tool life. The NN model also provides accurate predictions of tool wear, which can help detect and prevent tool failure during the operation.
In conclusion, this notebook demonstrates how to generate Stability Lobe Diagrams using SVMs and NNs, which can be a valuable tool for optimizing milling operations and improving tool life. The code and data used in this notebook are available in the associated Kaggle kernel.