Technological Supportof Parts’ Fatigue Life by Modeling Their Turning Process
DOI:
https://doi.org/10.20535/1810-0546.2018.2.129025Keywords:
Technological support, Fatigue strength, Fatigue life, Turning process, Multidimensional statistical analysisAbstract
Background. The issue of technological support of the required part’s material fatigue life by creating a mathematical model of the finishing turning process is considered. This model includes, as a target function, the maximum process productivity and the set of feed constraints and cutting speeds, permissible force and cutting power, machining precision, tool stability, surface roughness, part fatigue life, and cutting condition optimization.
Objective. The aim of the paper is to provide technological support for the required part fatigue life by determining the rational cutting conditions, taking into account the properties of the processed material, and to develop appropriate methodological recommendations.
Methods. The objective of the research is achieved by creating a mathematical model of the finishing turning process, determining the coefficients of the generalized materials’ characteristics of the classification group taking into account the properties of the processed material, further model optimization by the method of sliding admission and the definition of rational cutting conditions.
Results. The mathematical model of the finishing turning process, which takes into account the characteristics of the processed material and the properties of the technological processable system, was created. Relative coefficients of the materials’ generalized characteristics of the structural alloyed chromium steels group are calculated. A multivariate optimization method of the developed mathematical model is proposed.
Conclusions. The proposed methodological recommendation of technological support of a part’s material fatigue life based on a mathematical model of the finishing turning process, which includes the maximum process productivity and set of restrictions as the target function, which allows determining the rational cutting condition by the chosen method of nonlinear optimization.References
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