In modern mechanical engineering production, machining by cutting remains a key technological process due to its versatility and ability to process a wide range of materials. In the context of rapid advancements in information technologies, particularly the adoption of computer numerical control (CNC) machines and artificial intelligence systems, the optimization of cutting conditions has gained significant importance. This contributes to increased productivity, reduced financial and time costs, and ensures high processing quality. A crucial aspect is the integration of these technologies within the framework of CALS (Continuous Acquisition and Life-cycle Support), which involves creating a unified information model of a product across all stages of its life cycle—from design to disposal. CALS technologies enable data standardization, facilitating efficient infor-mation integration and data continuity in the processes of design, production, logistics, repair, and after-sales service.Research aimed at improving methods for predicting and optimizing cutting conditions is critical for enhancing the effi-ciency of mechanical engineering production in the context of digital transformation. Particular attention is given to the ma-chining of holes using axial cutting tools, where the accuracy of selecting cutting conditions directly impacts the quality and cost-effectiveness of the process. The study presents a generalized algorithm for the operation of software developed for the automated calculation of optimal cutting conditions. This algorithm takes into account technological parameters, material and tool characteristics, as well as requirements for accuracy and productivity. The implementation of such solutions enhances the competitiveness of production, reduces costs, and supports the sustainable development of the industry.
Vasylenko et al. (Fri,) studied this question.