Abstract:
The use of G-Codes in 3D printing slicing software is investigated in this thesis. To enable an in-depth study of G-Codes usage, the research focuses on automating the creation of G-Codes for various slicers and printer/firmware combinations. The study also examines how G-Code commands are used and how slicer outputs vary. Additionally, it attempts to identify the slicers from G-Code files using various machine learning algorithms, including Naive Bayes, Decision Tree, Random Forest, and Neural Network. The results enhance our knowledge of G-Codes and offer insightful information about how various slicers use them.