A method for calculating tensile strength of 42crmo for engineering machinery

Abstract

This invention relates to the field of steel tensile strength calculation technology, and in particular to a method for calculating the tensile strength of 42CrMo steel used in engineering machinery. The method involves establishing a dimensionless model through data fitting between tensile strength and quenching temperature X1, tempering temperature X2, and the mass percentage of Mn element X3: Y2 = 5404 - 2.12X1 - 4.952X2 + 540X3. This invention can accurately reflect the quantitative relationship between the heat treatment process parameters of 42CrMo steel, especially the tempering temperature, and tensile strength. It can predict tensile strength, enabling engineers to accurately predict the final properties of the material when formulating heat treatment processes, and to actively and accurately control the tensile strength of the material by adjusting process parameters.

Description

Technical Field

[0001] This invention relates to the field of steel tensile strength calculation technology, and in particular to a method for calculating the tensile strength of 42CrMo steel used in engineering machinery. Background Technology

[0002] Tensile strength, the maximum stress a material can withstand before breaking, is a core performance parameter for measuring a material's load-bearing capacity and fracture risk, and is widely used in component design, material selection, and quality acceptance. 42CrMo steel, due to its excellent strength, toughness, and hardenability, is widely used in key structural components of engineering machinery, such as high-strength bolts, hydraulic supports, and drive shafts. The service safety of these components is directly related to the tensile strength of the material.

[0003] Currently, the control of tensile strength of 42CrMo steel in engineering practice mainly relies on final performance testing, which is a "post-hoc" control method and has the following significant drawbacks: High quality risk: If the tensile strength is found to be substandard during testing, the entire batch of products may be scrapped, resulting in huge economic losses.

[0004] Process optimization is challenging: the tensile strength of a material is primarily determined by its chemical composition and heat treatment processes (quenching temperature, tempering temperature, etc.). Currently, there is a lack of a precise, quantitative model to establish the intrinsic relationship between "process parameters and performance indicators," and process development and optimization mainly rely on a "trial and error" approach, resulting in long development cycles and high costs.

[0005] Poor production stability: Due to the inability to predict and adjust the performance in real time during the heat treatment process, the performance of different batches of products fluctuates greatly, affecting the overall reliability and service life of the engineering machinery.

[0006] Therefore, there is an urgent need in this field for a calculation model that can accurately predict the tensile strength of 42CrMo steel based on heat treatment process parameters, so as to realize the transformation from "post-event detection" to "pre-event prediction and online control". Summary of the Invention

[0007] This invention provides a method for calculating the tensile strength of 42CrMo steel for engineering machinery. It can accurately reflect the quantitative relationship between the heat treatment process parameters of 42CrMo steel, especially the tempering temperature, and the tensile strength. It can predict the tensile strength, enabling engineers to accurately predict the final performance of the material when formulating the heat treatment process. This allows for proactive and precise control of the tensile strength of the material by adjusting the process parameters, meeting the differentiated performance requirements of different engineering machinery components, significantly shortening the R&D cycle of new products and processes, and reducing R&D costs and product quality risks.

[0008] To achieve the above objectives, the present invention employs the following technical solution: A method for calculating the tensile strength of 42CrMo used in engineering machinery establishes a dimensionless model relating tensile strength to quenching temperature, tempering temperature, and the mass percentage of Mn element through data fitting. Y2=5404-2.12X1-4.952X2+540X3; Where Y2 is the tensile strength, only the value is substituted, and the unit is MPa; X1 is the quenching temperature; only the value is substituted, and the unit is ℃. X2 is the tempering temperature; only the value is substituted, and the unit is ℃. X3 represents the mass percentage of Mn element; only the numerical value is provided, and the unit is [missing information].

[0009] Furthermore, after setting the target value for tensile strength, the method for determining the optimal values ​​of quenching temperature, tempering temperature, and the mass percentage of Mn element includes the following steps: S1. Prepare a standard 42CrMo steel sample. S2. Perform quenching treatment on the sample under fixed conditions; S3. Set multiple sets of different quenching temperatures X1 and different tempering temperatures X2, and perform tempering treatment on the quenched samples. S4. Determine the actual tensile strength of the specimen at each tempering temperature; S5. Using the multiple sets of (X1, X2, X3, Y2) data obtained in steps S3 and S4, the dimensionless model between tensile strength and quenching temperature, tempering temperature and mass percentage of Mn element is fitted by a multiple linear regression algorithm to determine the optimal values ​​of parameters X1, X2, and X3.

[0010] Furthermore, the ranges for the quenching temperature, tempering temperature, and the mass percentage of Mn are as follows: The quenching temperature X1 ranges from 850 to 865. The tempering temperature X2 ranges from 520 to 600. The mass percentage of Mn element ranges from 0.6 to 0.72.

[0011] Furthermore, the dimensionless model relating tensile strength to quenching temperature, tempering temperature, and the mass percentage of Mn is applicable to tensile strength prediction within a tempering temperature range of 520℃ to 600℃.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1) High prediction accuracy: The model can accurately capture the physical nature of the decrease in tensile strength of 42CrMo steel with increasing temperature during tempering. It has been verified that the average relative error between the predicted value and the measured value can be controlled within 20 MPa, which fully meets the engineering accuracy requirements. 2) Strong guidance: Engineering technicians can directly use the model for forward prediction (input process parameters and get performance results) and reverse design (input target performance and deduce the required process window), providing a scientific quantitative tool for the formulation and optimization of heat treatment processes; 3) Significant economic benefits: By replacing extensive trial production and testing with model prediction, the product development cycle is greatly shortened, and R&D costs and raw material waste are reduced. Simultaneously, precise process control improves the consistency and stability of product performance, enhancing the overall quality and reliability of the engineering machinery. 4) Easy to implement: The model is simple in form and the parameters are clearly defined. It can be easily integrated into the enterprise's production management software or industrial control system to realize the digital and intelligent prediction and control of tensile strength. Detailed Implementation

[0013] The specific embodiments of the present invention will be further described below: This invention discloses a method for calculating the tensile strength of 42CrMo used in engineering machinery. It establishes a functional relationship between the tensile strength Y2 and the quenching temperature (X1), tempering temperature X2, and Mn element X3. The dimensionless expression is as follows: Y2=5404-2.12X1-4.952X2+540X3; The goodness of fit was 91.94%; Where Y2 is the tensile strength, only the value is substituted, and the unit is MPa; X1 is the quenching temperature; only the value is substituted, and the unit is ℃. X2 is the tempering temperature; only the value is substituted, and the unit is ℃. X3 represents the mass percentage of Mn element; only the numerical value is provided, and the unit is % (%). X1, X2, and X3 are model parameters, and their value ranges are determined by data fitting on 42CrMo steel with specific chemical compositions. The quenching temperature X1 ranges from 850 to 865°C; the tempering temperature X2 ranges from 520 to 600°C; and the mass percentage of Mn element ranges from 0.6 to 0.72%.

[0014] The method for determining the optimal values ​​of X1, X2, and X3 includes the following steps: S1. Prepare a standard 42CrMo steel sample. S2. Perform quenching treatment on the sample under fixed conditions; S3. Set multiple sets of different quenching temperatures X1 and different tempering temperatures X2, and perform tempering treatment on the quenched samples. S4. Determine the actual tensile strength of the specimen at each tempering temperature; S5. Using the multiple sets of (X1, X2, X3, Y2) data obtained in steps S3 and S4, the dimensionless model between tensile strength and quenching temperature, tempering temperature and mass percentage of Mn element is fitted by a multiple linear regression algorithm to determine the optimal values ​​of parameters X1, X2, and X3.

[0015] As a preferred embodiment, a set of validated high-precision model parameters are: X1=865, X2=560, X3=0.72; this model is particularly suitable for predicting tensile strength in the tempering temperature range of 520℃ to 600℃.

[0016] The following embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments. Unless otherwise specified, the methods used in the following embodiments are conventional methods.

[0017] Example: Material preparation: Prepare a batch of 42CrMo steel bars with the following chemical composition (wt%): C: 0.39-0.45, Si: 0.17-0.37, Mn: 0.5-0.8, Cr: 0.9-1.2, Mo: 0.15-0.25, with the balance being Fe.

[0018] Heat treatment and data acquisition: Quenching: The sample was austenitized at 850-865℃ and held for 1 hour, then oil quenched; Tempering: Temper the quenched sample at 520-600℃ for 2 hours, and then air cool; Performance testing: In accordance with GB / T228.1 standard, tensile tests were performed on each quenched and tempered specimen, and the measured tensile strength values ​​were recorded. The results were then fitted to the parameters through model establishment.

[0019] The measured tensile strength Y2 corresponding to the obtained quenching temperature X1, tempering temperature X2, and Mn element mass percentage X3 is shown in Table 1: Table 1 X1 X2 X3 Y2 Y2 Prediction absolute value of difference 850 560 0.62 1161 1163.7 2.7 860 580 0.62 1028 1043.4 15.4 865 520 0.61 1334 1324.6 9.4 865 550 0.62 1178 1181.4 3.4 865 550 0.62 1183 1181.4 1.6 865 560 0.72 1187 1185.9 1.1 865 560 0.72 1187 1185.9 1.1 865 520 0.6 1304 1319.2 15.2 865 520 0.61 1340 1324.6 15.4 850 560 0.62 1160 1163.7 3.7 As can be seen from the examples in the table, the prediction error is <20 MPa, indicating high accuracy.

Claims

1. A method for calculating the tensile strength of 42CrMo used in engineering machinery, characterized in that, A dimensionless model was established based on data fitting to relate tensile strength to quenching temperature, tempering temperature, and the mass percentage of Mn. Y2=5404-2.12X1-4.952X2+540X3; Where Y2 is the tensile strength, only the value is substituted, and the unit is MPa; X1 is the quenching temperature; only the value is substituted, and the unit is ℃. X2 is the tempering temperature; only the value is substituted, and the unit is ℃. X3 represents the mass percentage of Mn element; only the numerical value is provided, and the unit is [missing information].

2. The method for calculating the tensile strength of 42CrMo for engineering machinery according to claim 1, characterized in that, After setting the target value for tensile strength, the method for determining the optimal values ​​for quenching temperature, tempering temperature, and the mass percentage of Mn element includes the following steps: S1. Prepare a standard 42CrMo steel sample. S2. Perform quenching treatment on the sample under fixed conditions; S3. Set multiple sets of different quenching temperatures X1 and different tempering temperatures X2, and perform tempering treatment on the quenched samples. S4. Determine the actual tensile strength of the specimen at each tempering temperature; S5. Using the multiple sets of (X1, X2, X3, Y2) data obtained in steps S3 and S4, the dimensionless model between tensile strength and quenching temperature, tempering temperature and mass percentage of Mn element is fitted by a multiple linear regression algorithm to determine the optimal values ​​of parameters X1, X2, and X3.

3. The method for calculating the tensile strength of 42CrMo for engineering machinery according to claim 1, characterized in that, The ranges for the quenching temperature, tempering temperature, and Mn element mass percentage are as follows: The quenching temperature X1 ranges from 850 to 865. The tempering temperature X2 ranges from 520 to 600. The mass percentage of Mn element ranges from 0.6 to 0.

72.

4. The method for calculating the tensile strength of 42CrMo for engineering machinery according to claim 1, characterized in that, The dimensionless model relating tensile strength to quenching temperature, tempering temperature, and the mass percentage of Mn is applicable to tensile strength prediction in tempering temperatures ranging from 520℃ to 600℃.