Jacso, Adam and Tancsa, Viktor and Hurtado, Gerardo Hurtado (2026) Recent advancements in AI-assisted tool path generation for CNC machining. In: 3rd Mexican-Hungarian Workshop on Factory Automation and Material Sciences. Springer Cham. (In Press)
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Abstract
The manufacturing industry is facing new challenges necessitating continuous development. In CNC machining, there are increasingly strict requirements for accuracy and surface quality, along with a growing demand for difficult-to-machine materials. These challenges can be addressed by implementing and advancing Industry 4.0 technologies. Digitalisation, digital twin technology, and the integration of artificial intelligence methods are essential to CNC machining to improve efficiency and quality. The strict requirements, the complexity of machining processes, and the need to balance often conflicting objectives, such as minimising machining costs while maximising sustainability, generally lead to complex multi-objective optimisation challenges. Addressing these issues demands intelligent computational methods. Although AI tools offer significant potential for optimising CNC tool path generation, their current application is still limited. This paper aims to provide an overview of this topic, focusing specifically on turning, drilling, and milling technologies, and reviewing results from recent years. Although this study cannot be exhaustive, it strives to give a comprehensive overview of the AI algorithms and their various applications in this field. These applications include optimising operational sequences and machining strategies, improving machining quality, enhancing productivity, increasing energy efficiency, avoiding collisions, and employing adaptive tool path shapes. The paper also reviews the challenges and future trends of AI applications in CNC technology. While the results achieved so far are already impressive, we can anticipate even more significant advancements in the near future, as extensive research is being conducted in this area.
| Item Type: | Book Section |
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| Uncontrolled Keywords: | CNC Machining, Artificial Intelligence, Tool Path Optimisation, Productivity, Machining Quality, Energy Efficiency |
| Subjects: | T Technology / alkalmazott, műszaki tudományok > TJ Mechanical engineering and machinery / gépészmérnöki tudományok |
| Depositing User: | Dr. Ádám Jacsó |
| Date Deposited: | 26 Sep 2026 07:59 |
| Last Modified: | 26 Sep 2026 07:59 |
| URI: | https://real.mtak.hu/id/eprint/247720 |
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