WO-2026094379-A1 - SPRING FATIGUE LIFE PREDICTION DEVICE, SPRING FATIGUE LIFE PREDICTION METHOD, AND SPRING FATIGUE LIFE PREDICTION PROGRAM

WO2026094379A1WO 2026094379 A1WO2026094379 A1WO 2026094379A1WO-2026094379-A1

Abstract

A purpose of an embodiment of the present invention is to provide a spring fatigue life prediction system in which the prediction accuracy is improved. A spring fatigue life prediction device according to one embodiment of the present invention comprises: a database including at least the fatigue life of springs, and the peak residual stress of the springs and the arithmetic average roughness of the surface of the springs associated with the fatigue life of the springs; and a spring fatigue life prediction model generation unit that generates a spring fatigue life prediction model by machine learning using explanatory variables including at least the peak residual stress of a spring and the arithmetic average roughness of the surface of the spring, and the fatigue life of the spring as an objective variable. The spring fatigue life prediction model is used to predict the fatigue life of a spring on the basis of explanatory variables.

Inventors

ONUMA Kosuke
TAKAHASHI KEITA
KUMAI SHINTARO

Assignees

日本発條株式会社

Dates

Publication Date: 20260507
Application Date: 20250819
Priority Date: 20241031

Claims (12)

A database including at least the fatigue life of a spring, the peak residual stress of the spring associated with the fatigue life of the spring, and the arithmetic mean roughness of the spring surface, The system includes a spring fatigue life prediction model generation unit that generates a spring fatigue life prediction model by machine learning using explanatory variables that include at least the peak residual stress of the spring and the arithmetic mean roughness of the spring surface, and the fatigue life of the spring at a predetermined stress amplitude as the objective variable, A spring fatigue life prediction device that predicts the fatigue life of a spring based on the explanatory variables, using the spring fatigue life prediction model.
The database further includes the stress amplitude applied to the spring during durability testing, which is associated with the fatigue life of the spring. The spring fatigue life prediction device according to claim 1, wherein the explanatory variable further includes the stress amplitude applied to the spring during the durability test.
The database further includes the maximum cross-sectional height of the spring, the Rockwell hardness, and the mounting height of the spring during durability testing, which are associated with the fatigue life of the spring. The spring fatigue life prediction device according to claim 2, wherein the explanatory variables further include the maximum cross-sectional height of the spring, the Rockwell hardness, and the mounting height of the spring during the durability test.
The database comprises the fatigue life of the spring, the peak residual stress of the spring associated with the fatigue life of the spring, the maximum cross-sectional height of the spring, the arithmetic mean surface roughness of the spring, the stress amplitude applied to the spring during the durability test, the Rockwell hardness, and the mounting height of the spring during the durability test. The spring fatigue life prediction device according to claim 3, wherein the explanatory variables consist of the peak residual stress of the spring, the maximum cross-sectional height of the spring, the arithmetic mean roughness of the surface of the spring, the stress amplitude applied to the spring during the durability test, the Rockwell hardness, and the mounting height of the spring during the durability test.
The computer reads data from the database that includes at least the fatigue life of the spring, the peak residual stress of the spring associated with the fatigue life of the spring, and the arithmetic mean roughness of the spring surface. The computer generates a spring fatigue life prediction model by machine learning using explanatory variables that include at least the peak residual stress of the spring and the arithmetic mean roughness of the spring surface, and the fatigue life of the spring at a predetermined stress amplitude as the objective variable. A method for predicting the fatigue life of a spring, wherein the computer predicts the fatigue life of the spring based on the explanatory variables, using a fatigue life prediction model for the spring.
The computer further includes, from the database, the stress amplitude applied to the spring during a durability test associated with the fatigue life of the spring, The method for predicting the fatigue life of a spring according to claim 5, wherein the explanatory variable further includes the stress amplitude applied to the spring during the durability test.
The computer reads data from the database, further including the maximum cross-sectional height of the spring, the Rockwell hardness, and the mounting height of the spring during the durability test, which are associated with the fatigue life of the spring. The method for predicting the fatigue life of a spring according to claim 6, wherein the explanatory variables further include the maximum cross-sectional height of the spring, the Rockwell hardness, and the mounting height of the spring during the durability test.
The computer reads data from the database consisting of the fatigue life of the spring, the peak residual stress of the spring associated with the fatigue life of the spring, the maximum cross-sectional height of the spring, the arithmetic mean roughness of the surface of the spring, the stress amplitude applied to the spring during the durability test, the Rockwell hardness, and the mounting height of the spring during the durability test. The method for predicting the fatigue life of a spring according to claim 7, wherein the explanatory variables consist of the peak residual stress of the spring, the maximum cross-sectional height of the spring, the arithmetic mean roughness of the surface of the spring, the stress amplitude applied to the spring during the durability test, the Rockwell hardness, and the mounting height of the spring during the durability test.
The computer is loaded with data from the database, including at least the fatigue life of the spring, the peak residual stress of the spring associated with the fatigue life of the spring, and the arithmetic mean roughness of the spring surface. The system includes generating a spring fatigue life prediction model for a spring by machine learning using explanatory variables that include at least the peak residual stress of the spring and the arithmetic mean roughness of the spring's surface, and the fatigue life of the spring at a predetermined stress amplitude as the objective variable, A spring fatigue life prediction program that uses the spring fatigue life prediction model and causes the computer to predict the spring fatigue life based on the explanatory variables.
From the aforementioned database, the computer is given data that further includes the stress amplitude applied to the spring during durability testing, which is related to the fatigue life of the spring. The spring fatigue life prediction program according to claim 9, wherein the explanatory variable further includes the stress amplitude applied to the spring during the durability test.
From the aforementioned database, the computer is loaded with data including the maximum cross-sectional height of the spring, the Rockwell hardness, and the mounting height of the spring during the durability test, which are associated with the fatigue life of the spring. The spring fatigue life prediction program according to claim 10, wherein the explanatory variables further include the maximum cross-sectional height of the spring, the Rockwell hardness, and the mounting height of the spring during the durability test.
The computer is loaded with data from the database, consisting of the fatigue life of the spring, the peak residual stress of the spring associated with the fatigue life of the spring, the maximum cross-sectional height of the spring, the arithmetic mean roughness of the surface of the spring, the stress amplitude applied to the spring during the durability test, the Rockwell hardness, and the mounting height of the spring during the durability test. The spring fatigue life prediction program according to claim 11, wherein the explanatory variables consist of the peak residual stress of the spring, the maximum cross-sectional height of the spring, the arithmetic mean roughness of the surface of the spring, the stress amplitude applied to the spring during the durability test, the Rockwell hardness, and the mounting height of the spring during the durability test.

Description

Spring fatigue life prediction device, spring fatigue life prediction method, and spring fatigue life prediction program This invention relates to a spring fatigue life prediction system, or to a spring fatigue life prediction method, or to a method for detecting irregularly shaped structures contained in an image, or to a spring fatigue life prediction program. To manage the design and quality of springs, it is necessary to evaluate the fatigue life of springs. Traditionally, fatigue life was evaluated by conducting durability tests, but in recent years, techniques for predicting fatigue life using machine learning have been investigated. For example, Patent Document 1 discloses a technique for estimating fracture life with good accuracy by estimating the S-N curve using machine learning. This technique comprises a functional unit that reads the type of fatigue limit estimation and the mechanical properties to be used in the machine learning decision tree; a functional unit that reads fatigue data for a specified steel type from a fatigue data sheet; and a machine learning calculation unit that performs supervised machine learning on the read fatigue data for the steel type using the specified mechanical properties. The machine learning calculation unit, after performing supervised machine learning, outputs a calculation result for the fatigue limit estimation according to the specified type of fatigue limit estimation using the specified mechanical properties. Furthermore, Patent Document 2 discloses a technique for estimating the relationship between fatigue life and loaded stress, which improves the machine learning method to obtain accurate estimations when estimating S-N curves using machine learning. This technique comprises a functional unit that reads the type of fatigue limit estimation and the mechanical properties used in the machine learning decision tree; a functional unit that reads fatigue data of a specified metal structural material from a fatigue data sheet; and a machine learning calculation unit that performs supervised machine learning on the read fatigue data of the metal structural material using the specified mechanical properties to estimate the loaded stress corresponding to the fatigue life. The machine learning calculation unit, which performs the supervised machine learning, outputs the calculation result of the estimated loaded stress corresponding to the fatigue life according to the specified type of fatigue limit estimation, using the specified mechanical properties. This is a block diagram showing a spring fatigue life prediction device 100 according to one embodiment of the present invention.This is a flowchart illustrating a method for predicting the endurance fatigue of a spring according to one embodiment of the present invention.This figure shows the results of generating a spring fatigue life prediction model and predicting the spring's fatigue life.This table shows the results of predicting the fatigue life of a spring by generating a spring fatigue life prediction model using 17 explanatory variables that are highly correlated with the fatigue life of a spring, and using the fatigue life of the spring (number of fractures) as the dependent variable.This figure shows the results of predicting the fatigue life of the spring in Comparative Example 1.This figure shows the results of predicting the fatigue life of a spring according to one embodiment of the present invention.This figure shows the results of predicting the fatigue life of a spring by generating a spring fatigue life prediction model by combining the spring's peak residual stress, which has the highest contribution to the prediction accuracy of the spring's fatigue life, with explanatory variables in descending order of their contribution.This figure shows the results of predicting the fatigue life of a spring by generating a model for predicting the fatigue life of a spring by combining four explanatory variables—stress amplitude applied to the spring during durability testing, Rockwell hardness (HRC), spring mounting height during durability testing, and decarburization—and adding one explanatory variable at a time in order of their contribution to the test, in descending order of importance.This figure shows the results of predicting the fatigue life of a spring using a spring fatigue life prediction model generated by changing the machine learning algorithm from an artificial neural network (ANN) to a random forest, with the peak residual stress of the spring, the maximum cross-sectional height of the spring (Pt), the arithmetic mean roughness of the spring surface (Ra), the stress amplitude applied to the spring during the durability test, the Rockwell hardness (HRC), and the mounting height of the spring during the durability test as explanatory variables.This figure shows the results of predicting the fatigue life of a spring using a machine learning algorithm called Random Forest, with the stress