PSI - Issue 81

Viktor Kovalov et al. / Procedia Structural Integrity 81 (2026) 297–304

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account the probabilistic nature of loads and failures makes it difficult to predict actual reliability and optimise tool design from an economic point of view. Classic methods of assessing the stability of cutting tools do not adequately reflect the variable nature of cutting forces, fluctuations in the mechanical properties of the material, and the influence of design factors. Therefore, there is a need to create mathematical reliability models that allow not only to evaluate the failure-free operation of assembled tools, but also to determine their rational level of reliability, which ensures minimum total manufacturing and operating costs. Thus, research into the reliability of assembled turning tools for heavy machine tools is of great scientific and practical importance: it is aimed at improving the efficiency of cutting processes, justifying the design and maintenance parameters of tools, and ensuring the competitiveness of machine-building enterprises in the context of Industry 4.0. Research into the reliability of assembled turning tools for heavy-duty machine tools is an important area of modern mechanical engineering, since the efficiency of machining processes is determined by the durability and maintainability of cutting tools. Modern approaches consider reliability as a complex property that depends on random loads and the dispersion of material parameters. In this context, it is proposed to use an inverse Gaussian degradation model to assess tool reliability based on experimental wear data (Das, 2024). The Bayesian approach to assessing the service life of cutters allows for the a priori uncertainty of parameters and variation in processing conditions (Gao et al., 2022). Semi-Markov processes, which allow describing changes in operating states, are effective for analysing the readiness of the "cutter- machine" system (Świderski et al., 2020). Such models provide calcu lations of the readiness coefficient and the probability of failure-free operation, which is also used in this work. The problem of stochastic wear of cutters in harsh conditions is considered in experimental studies, which show the dependence of the degradation rate on cutting parameters (Bloul et al., 2024). For systems with multiple degradation characteristics (wear, chipping, vibration), multidimensional gamma processes have been proposed to describe the interdependence of damage over time (Liu et al., 2024). The material and coating of the cutting tool are of great importance for improving reliability. Modern multilayer coatings demonstrate increased resistance to oxidation and wear (Dalibon et al., 2024; Liu et al., 2023; Liu et al., 2025, Hutsaylyuk et al, 2019, Hutsaylyuk et al, 2020). To predict the remaining life of cutters, degradation models based on the inverse Gaussian process (Huang et al.,2021) and predictive maintenance methods that take into account surface quality and online sensor data (Lu et al., 2021) are actively used. A review of modern tool condition monitoring systems shows that combining machine learning with physically informed models significantly improves the accuracy of failure prediction (Tiwari et al., 2023). Comparative tests of different cooling methods for turning titanium alloys (Agrawal et al., 2021) confirm that cutting conditions significantly affect tool life and must be taken into account when calculating reliability levels. Tribological studies (Hwang et al., 2024) also confirm that the right choice of coating significantly reduces wear under heavy loads. Physically informed stochastic degradation models (He et al., 2025) allow wear processes to be described and the moment of failure to be predicted with high accuracy. In summary, the reliability of prefabricated cutters is improved by: probabilistic analysis of operational processes (Das, 2024; Gao et al., 2022); performance modelling through semi- Markov processes (Świderski et al., 2020); multidimensional degradation models (Liu et al., 2024); the use of highly effective coatings (Dalibon et al., 2024; Liu et al., 2023); application of PHM approaches and predictive maintenance (Lu et al., 2021); optimisation of cutting conditions taking into account wear resistance (Agrawal et al., 2021). Cutting processes on heavy machine tools are characterised by significant mechanical and thermal loads, which leads to intensive wear and premature failure of assembled cutters. A distinctive feature of such conditions is the stochastic nature of loads and the variability of the physical and mechanical properties of tool and workpiece materials. This causes uncertainty in the service life of the tool and complicates the prediction of the moment of failure. The high energy intensity of the process, fluctuations in cutting temperature and local stresses in the area of contact between the blade and the workpiece cause instability in stability indicators. As a result, there is a need to create models that take into account the random nature of the wear process and allow for a reasonable determination of the reliability of the assembled tool under severe machining conditions. The aim of the work is to develop a mathematical model of the reliability of a prefabricated turning tool for heavy-duty machines, taking into account the stochastic nature of loads and degradation processes, as well as to determine the rational (economically feasible) level of tool reliability. 2. Materials and methods 2.1. Analysis of operating conditions The operation of assembled turning tools for heavy-duty machines is characterised by complex thermomechanical loads that significantly affect their reliability and durability. When machining large-sized parts made of alloy structural steels, the cutting process is accompanied by variable forces, elevated temperatures and intermittent contact stresses. Such conditions cause stochastic wear, including abrasive wear, chipping and thermal cracking of carbide inserts. Experimental analysis of operating conditions has shown that most cutters used on heavy-duty lathes are equipped with replaceable carbide inserts of standard types and mounting patterns — P, S, D, W, H (Fig. 1). Their distribution in industrial practice is as follows: type S (screw fastening through a hole) — 41%, H (fastening from above by a shoulder) — 26%, type P (fastening through a hole) — 24%, type W (side fastening) — 10%, type D (fastening from above) — 9%.