Takayuki Shiraiwa

Fatigue Performance Prediction of Structural Materials by Multi-scale Modeling and Machine Learning

Structural materials having higher performance in strength, toughness, and fatigue resistance are strongly required. In the conventional materials development, many fatigue tests need to be conducted to validate statistical behavior of fatigue failure. Accordingly the evaluation of fatigue properties with shorter time becomes quite essential. Based on such background, we are developing fatigue prediction methods for wide range of structural materials by multi-scale finite element analysis (FEA) and machine learning in the Materials Integration (MI) system. The multi-scale FEA consists of the following procedures: (i) mechanical and thermal properties are estimated by using commercially available software and database; (ii) temperature field, residual stress and distortion generated during a manufacturing process is calculated on the macroscopic model by thermo-mechanical FEA; (iii) macroscopic stress field under cyclic loading condition is calculated with a hardening constitutive model; (iv) the microscopic stress field is derived by finite element model with the polycrystalline structures and the cycles for a fatigue crack initiation is analyzed by strain energy accumulation on the slip plane; (v) the cycles for fatigue crack propagation is analyzed by extended finite element method (X-FEM) and the total number of cycles to the failure is obtained. The second approach is to use machine learning techniques to obtain empirical prediction formula. The database was prepared from published resources and experiments. Deterministic machine learning techniques such as multivariate linear regression and artificial neural network provided accurate equations to predict fatigue strength from materials and process parameters. Additionally, the concept of model-based machine learning was adopted to incorporate prior knowledge of microstructures and properties, and to account for uncertainty on fatigue life. The results showed that model-based machine learning was a promising tool for predicting fatigue performance in structural materials. The features and limitations of our prediction methods will be discussed.

Prediction of Fatigue Life Induced by Defects Considering Crack Initiation

Defects such as inclusions and void can be the origin of fatigue failure particularly in welding and casting materials. The fatigue life is influenced by the size, direction, shape and location of the defects. Therefore, many fatigue tests are necessary to obtain the fatigue properties. On the other hand, the prediction of fatigue life by representing characteristic variations of defects with probability distribution functions has been investigated by using several physical models and empirical formulae. However, most of the prediction methods of fatigue life arising from defects have not included the crack initiation. In the present study, the prediction was conducted by dividing the process into crack initiation and crack propagation. Voids, hard inclusions (Al2O3) and soft inclusions (MnS) were supposed as defects and two prediction models were proposed. Only the life of crack propagation was predicted by Paris law in one model (model A) while the life of crack initiation as well as propagation was predicted by Tanaka and Mura model in the other model (model B). The stress intensity factor using √area (projected square root area of defects) proposed by Murakami et al. was applied to Paris law in both models. The stress concentration and Taylor factor were applied to Tanaka and Mura model in the model B. In case of casting materials including voids, the fatigue life predicted by both models was within the range of the experimental scattering. Although the fatigue life predicted by model A was not consistent with the experimental results under high and low stress in case of high strength steel including MnS, the fatigue life predicted by model B mostly showed a good agreement with experimental results. Therefore, the present result suggested that the fatigue life prediction considering crack initiation showed higher precision than the prediction without crack initiation.

Uncertainty Quantification of Fatigue Life Prediction in Welded Structures Using Microstructure-Based Simulations

A framework for uncertainty quantification of fatigue life prediction in welded structures has been developed based on multi-scale finite element analysis (FEA) considering microstructure. The multi-scale FEA consists of the following procedures: (i) mechanical and thermal properties are estimated by using commercially available software and database; (ii) temperature field, residual stress and distortion generated during a welding process is calculated on the global model by thermo-mechanical FEA; (iii) macroscopic stress field under cyclic loading condition is calculated with a hardening constitutive model; (iv) the mesoscopic stress field is derived by crystal plasticity finite element analysis with sub-model technique and the cycles for a fatigue crack initiation is analyzed by strain energy accumulation on the slip plane; (v) the cycles for fatigue crack propagation is analyzed by extended finite element method and the total number of cycles to the failure is obtained. The possible uncertainties in the current model include physical variability (weld shape, residual stress, loading and microstructure), data uncertainty due to measurement error and modeling uncertainty in numerical approximations and finite element discretization. A sensitivity analysis was performed by changing the input parameters in the proposed simulation. Additionally, fatigue database of weld joint were aggregated from public databases, published papers and academic resources, and compared with simulation results to quantify the contribution of each source of uncertainty. The effects of residual stress, toe radius and microstructure on the fatigue life of welded joints were evaluated by the proposed methodology. It was shown that the fatigue life increased with the toe radius, and the scattering in high cycle region appeared due to considering microstructure. The features and limitations of our methods will be discussed.

Fatigue crack behavior of thin copper sheet and its application for smart stress-memory patch

A new sensor called “smart stress-memory patch” which can measure the stress amplitude and the number of fatigue cycles and the maximum stress related to a structure subjected to fatigue loading for efficient structural health monitoring has been proposed. However, there are two problems in the real applications of smart patch. Firstly, when the sensor is adhered to the structure, the sensing property of smart patch may change because the restrained condition of the sensor would be intricately changed. Secondly, high-precision crack measurement of smart patch by optical microscope is too cumbersome in the real field. In this study, thin electrodeposited (ED) Cu specimen was prepared and attached to steel bar as a sensor of smart patch. The fatigue test was performed to examine the fatigue crack growth of the sensor attached to the structure. Furthermore, wireless devices using low power wireless modules have been developed for efficient crack measurements, and wireless devices results give close agreement with the results by microscope.