PSI - Issue 65

M.A. Skotnikova et al. / Procedia Structural Integrity 65 (2024) 248–254 2 M.A. Skotnikova, A.Y. Ryabikin, A.D. Shestakov, L.D. Tuptei, A.D. Novokshenov / Structural Integrity Procedia 00 (2024) 000–000

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smaller geometric dimensions. In physical modeling, a relationship is revealed between the output characteristics of the tribosystem, for example, torque and coefficient of friction, and some generalized value of tribo-tension, for example, wear and wear resistance. The exciting forces in the FMS over time have continuous periodic impact characteristics, for example, the rotational speeds of crankshafts or engine rotors, fluctuations from the imbalance of rotating wheels. There is always a spectrum of random vibrations, which are difficult to describe by simple continuous functions. The most convenient for modern calculation methods is to set the amplitude-frequency characteristic of the spectrum of exciting forces, Shabanov et al. (2016). Increasing the wear resistance of metals and alloys is one of the main tasks of modern materials science and engineering. The destruction of the surface during friction and wear disables a large number of machines and structures, therefore it is extremely important to analyze the interactions of the surfaces of materials during these processes, as well as to work on expanding the methods of this analysis. Numerous studies show that many processes occur in the contact zone: friction, wear, adhesion, deformation, and destruction. These processes are influenced by various material characteristics and friction conditions: physico-mechanical, chemical, thermal properties, contact stresses, lubricant, surface geometry, etc. Processing and identification of relationships and dependencies between such a large number of parameters is impossible without the use of modern automated equipment, as well as innovative methods for analyzing large amounts of data, Skotnikova (2016), Syundyukov (2023), Tsvetkova (2017), Skotnikova (2020), Filippov (2021), Abdukakhkhorov (2016), Strelnikova (2020). Therefore, the purpose of this work was to develop a physical model of a frictional mechanical system to identify the relationship between the moment of friction and the wear resistance of friction pairs according to the "Steel roller – Abrasive roller" scheme using a machine learning computer program in Python.

2. Methods and materials

The initial data for the analysis were the results of tribotechnical tests of 3 steels: two equally hard wear-resistant steels of the martensitic class Hardox 450, Quard 450 and ferrite-pearlite steel 09G2S. Table 1 shows the chemical composition of the studied steels.

Table 1. Chemical composition of the studied steels

Steel grade

C

Si

Mn

P

S

Cr

Al

Mo

B

09G2S

0.099 0.086 0.102

0.518 0.248 0.175

1.31 1.15

0.021

0.008 0.004 0.004

0.075 0.083 0.921

0.003 0.026 0.056

0.017 0.004 0.036

<0.001 <0.001 <0.001

Quard 450 Hardox 450

0.02 0.01

0.655

Tribotechnical tests were carried out on a standard SMC-2 friction machine (Fig. 1, a) for 2.5 hours on samples according to the "roller–roller" scheme. The upper abrasive roller was stationary under a load of 45 N, the lower roller made of the studied steels rotated at a frequency of 500 rpm. The measurement of metal microhardness using the Vickers method was carried out on an automatic FUTURE TECH hardness tester (Japan), (Fig. 1, b) at a load of 50 g. The author's Python program and a set of libraries were used as software for data analysis. a b

Fig. 1. Testing equipment: a) SMC-2 friction machine; b) FUTURE-TECH FM-300 automatic microhardness meter