20.500.12556/DKUM-96065 Large language models for G-code generation in CNC machining: A comparison of ChatGPT-3.5 and ChatGPT-4o This 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. generative artificial intelligence intelligent manufacturing large language models (LLM) ChatGPT CNC machining G-code programming generativna umetna inteligenca pametna proizvodnja veliki jezikovni modeli CNC obdelava programiranje z G-kodo true false true University of Maribor Angleški jezik Slovenski jezik https://apem-journal.org/Archives/2025/Abstract-APEM20-2_224-238.html Članek v reviji 2025-11-28 13:57:29 2025-11-28 13:57:29 2025-11-29 03:15:08 0000-00-00 00:00:00 2025 0 0 str. 224-238 no. 2 Vol. 20 June 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-09 2025-06-19 2025-07-29 621:681.5 256562947 10.14743/apem2025.2.537 1855-6531 APEM20-2_224-238.pdf APEM20-2_224-238.pdf 1 0BF2DCD76F94FF3AF7A74455CBEC9FFE f71858a8e46bd9928b6f228e88224dae3d3571bab768edfba28cd0a087c2200f 069ce406-cc5a-11f0-b6ed-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=200953 https://apem-journal.org/Archives/2025/Abstract-APEM20-2_224-238.html 0 0c22dbd4-cc5a-11f0-b6ed-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=200954 Fakulteta za strojništvo Advances in production engineering & management 0 0 0