Large language models for G-code generation in CNC machining: A comparison of ChatGPT-3.5 and ChatGPT-4oŠket, Kristijan (Avtor) Potočnik, David (Avtor) Brezočnik, Miran (Avtor) Ficko, Mirko (Avtor) Klančnik, Simon (Avtor) generative artificial intelligenceintelligent manufacturinglarge language models (LLM)ChatGPTCNC machiningG-code programmingThis research explores the viability of producing ISO G-code for 3-axis machining with OpenAI's Chat Generative Pre-Trained Transformer models, particularly ChatGPT-3.5 and the newer GPT-4o. G-code (RS-274-D, ISO 6983) converts human directives into commands that machines can understand, controlling toolpaths, spindle velocities, and feed rates to produce particular aspects of an object. Previously, G-code was generated either by hand or through the use of computer-aided manufacturing (CAM) software along with machine-specific post-processors, both of which may require considerable time and expense. This research aimed to assess the practicality and effectiveness of specific large language models (LLMs) in generating G-code. The assessment took place in three distinct phases on a sample component that required 3-axis machining. These phases included: (1) the self-generated production of G-code for the sample component, (2) the examination of the independently generated G-code in the CAM application, and (3) the recognition and justification of mistakes in the G-code. The outcomes indicated varying abilities with promising findings. This method could accelerate and possibly enhance manufacturing workflows by decreasing reliance on expensive CAM software and specialized knowledge.University of Maribor20252025-11-28 13:57:29Članek v reviji96065UDK: 621:681.5COBISS_ID: 256562947DOI: 10.14743/apem2025.2.537ISSN pri članku: 1855-6531sl