| Title: | Large language models for G-code generation in CNC machining: A comparison of ChatGPT-3.5 and ChatGPT-4o |
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| Authors: | ID Šket, Kristijan (Author)
ID Potočnik, David (Author)
ID Brezočnik, Miran (Author)
ID Ficko, Mirko (Author)
ID Klančnik, Simon (Author) |
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| Files: |  APEM20-2_224-238.pdf (4,02 MB)
MD5: 0BF2DCD76F94FF3AF7A74455CBEC9FFE
 https://apem-journal.org/Archives/2025/Abstract-APEM20-2_224-238.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | generative artificial intelligence, intelligent manufacturing, large language models (LLM), ChatGPT, CNC machining, G-code programming |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 09.05.2025 |
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| Article acceptance date: | 19.06.2025 |
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| Publication date: | 29.07.2025 |
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| Publisher: | University of Maribor |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 224-238 |
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| Numbering: | Vol. 20, no. 2 |
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| PID: | 20.500.12556/DKUM-96065  |
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| UDC: | 621:681.5 |
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| ISSN on article: | 1855-6531 |
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| COBISS.SI-ID: | 256562947 |
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| DOI: | 10.14743/apem2025.2.537 |
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| Publication date in DKUM: | 28.11.2025 |
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| Views: | 392 |
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| Downloads: | 36 |
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| Metadata: |    |
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| Categories: | Misc.
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