PSI - Issue 81

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

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to the wear-type failure (Fig. 2). The R(t) function curve is characterised by a smooth decline within the first 80 – 100 minutes of operation, followed by a sharp decrease in the probability of failure- free operation, which is consistent with the results of observations of the actual wear of the plates. Figure 1 also shows experimental points confirming the convergence of the theoretical curve with practical data, with an approximation error not exceeding 10%.

Fig. 2. Tool reliability function R(t) according to Weibull's law Figure 3 shows the dependence of the failure rate density f(t) for different types of plates. The maximum density value is observed at t=95 – 105 min, which corresponds to the average service life of the cutting edge . A comparative analysis showed that H-type plates have slightly higher stability (by 6 – 8 %) compared to S-type plates, which is explained by stiffer fastening and better heat removal from the working area. Based on the results of calculations and experiments, the readiness coefficient of the assembled cutter was determined to be KG=0.94 – 0.96, which corresponds to a high level of system performance with minimal downtime for plate replacement. The average time between failures of the tool is T(bezv)=110 – 120 min, and the average recovery time is T(vidn)=5 – 7 min. These indicators confirm the feasibility of using the developed probabilistic model for practical assessment of operational reliability.

Fig. 3. Failur time distribution density f(t) for different types of plates

3.2. Economic Optimization An economic assessment performed according to the criterion of minimum reduced costs showed that the optimal reliability level for the cutter body is 0.64, while for the replaceable carbide plate it is 0.75 – 0.82 (Fig. 4). Increasing reliability above the above values leads to an exponential increase in manufacturing costs, which is not offset by a reduction in the number of failures. Thus, the parameters given are economically feasible for industrial use. Figure 5 shows the dependence of the reduced costs on the reliability level C=f(H), which has a clearly defined minimum at H=0.64. The graph illustrates the impact of individual cost components: tool manufacturing costs increase exponentially with increasing reliability, since ensuring reliability requires the use of higher quality materials and more complex processing technologies. At the same time, operating costs decrease with increasing reliability, as the frequency of failures and equipment downtime decreases. The total curve of the reduced costs Ct(H) has a minimum at the point H opt =0.64 , which corresponds to the economically optimal level of reliability. This result is fully consistent with the data obtained using the stochastic model and confirms the possibility of practical use of the model to determine the appropriate level of reliability when designing prefabricated turning tools for heavy machine tools. Thus, analytical and graphical dependencies confirm the existence of an economic optimum that ensures minimum total costs throughout the tool's life cycle. The results obtained indicate a high degree of convergence between theoretical and experimental data, as well as the practical applicability of the developed model for analysing, forecasting and optimising the operating parameters of assembled turning tools. The developed methodology can be used to create systems for controlling the technical condition of the tool, determining the optimal intervals for scheduled replacements, and forming databases on the reliability of cutting tools in production conditions.