A crystal plasticity approach to understand fatigue response with respect to pores in additive manufactured aluminium alloys

Cao, Mengzhen; Liu, Yang; Dunne, Fionn P.E.

doi:10.1016/j.ijfatigue.2022.106917

Cited by 37 publications

(13 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23 Structure-property models are usually developed to capture the microstructure-sensitivity of PBF-LB material. 24,25 Specifically, full-field crystal plasticity (CP) model, for example, CP finite element [26][27][28] and CP fast Fourier transform [29][30][31] models, are widely used to predict mechanical properties based on inherited microstructure, for example, from the PBF-LB process. However, the visible intragranular and intergranular mechanical fields from full-field CP also results in very high computational overhead and again, for a typically small-scale representative volume element of simulated material.…”

Section: Introductionmentioning

confidence: 99%

Process-structure-property modeling for postbuild heat treatment of powder bed fusion Ti-6Al-4V

Liu

Yang

Chai

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

Postbuild heat treatment is an important component in optimized manufacturing processing for laser beam powder bed fusion (PBF-LB) Ti-6Al-4V. The development of predictive modeling, based on the understanding of the relationships between process parameters, microstructure evolution, and mechanical properties, is a potentially key ingredient in this optimization process. In this paper, a process-structure-property (PSP) model is developed to predict the effect of postbuild heat treatment on yield strength, which is a key tensile property for PBF-LB Ti-6Al-4V. The process-structure part is developed with a focus on the prediction of solid-state phase transformation, especially dissolution of martensite during the heating phase. Subsequent tensile properties are quantified by a microstructure-sensitive yield strength model based on the predicted microstructure variables. The integrated PSP model is validated via experimentally measured phase fraction, α lath width and monotonic tensile tests on PBF-LB Ti-6Al-4V with different heat treatment temperatures, for identification of optimal process parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

Process-structure-property modeling for postbuild heat treatment of powder bed fusion Ti-6Al-4V

Liu

Yang

Chai

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

show abstract

“…It is well-known that the surface conditions, internal defects, and other microstructural features strongly affect the fatigue performance of AM alloys, but the understanding of the PMP relationship remains largely qualitative 12 , 13 . Both physics- 14 , 15 and machine learning (ML)-based approaches 16 , 17 were developed to resolve this issue, which demands reliable fatigue data for model verification and validation (V&V). Although the volume of data is much smaller than that reported for alloys produced by conventional techniques such as casting and forging, thousands of papers have been published on the fatigue performance of AM alloys, which provide a complete subset of data for analysis.…”

Section: Background and Summarymentioning

confidence: 99%

Fatigue database of additively manufactured alloys

Zhang

2023

Sci Data

View full text Add to dashboard Cite

Fatigue is a process of mechanical degradation that is usually assessed based on empirical rules and experimental data obtained from standardized tests. Fatigue data of engineering materials are commonly reported in S-N (the stress-life relation), ε-N (the strain-life relation), and da/dN-ΔK (the relation between the fatigue crack growth rate and the stress intensity factor range) data. Fatigue and static mechanical properties of additively manufactured (AM) alloys, as well as the types of materials, parameters of AM, processing, and testing are collected from thousands of scientific articles till the end of 2022 using natural language processing, machine learning, and computer vision techniques. The results show that the performance of AM alloys could reach that of conventional alloys although data dispersion and system deviation are present. The database (FatigueData-AM2022) is formatted in compact structures, hosted in an open repository, and analyzed to show their patterns and statistics. The quality of data collected from the literature is measured by defining rating scores for datasets reported in individual studies and through the fill rates of data entries across all the datasets. The database also serves as a high-quality training set for data processing using machine learning models. The procedures of data extraction and analysis are outlined and the tools are publicly released. A unified language of fatigue data is suggested to regulate data reporting for the fatigue performance of materials to facilitate data sharing and the development of open science.

show abstract

“…CP model was adopted to obtain the stress-strain responses around the internal defects such as pores, 211 inclusions, 212 and LoF. 213 Then life prediction methods combining stress-strain information with traditional fatigue models were conducted. In the work of Zhang et al, 211 three FIPs were used to obtain fatigue lives:…”

Section: Crystal Plasticity-based Modelsmentioning

confidence: 99%

“…F I G U R E 1 9 Framework of establishing a real microstructure-based FE model 213. Reproduced from Cao et al213 with permission from Elsevier.…”

mentioning

confidence: 99%

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.

show abstract

A crystal plasticity approach to understand fatigue response with respect to pores in additive manufactured aluminium alloys

Cited by 37 publications

References 54 publications

Process-structure-property modeling for postbuild heat treatment of powder bed fusion Ti-6Al-4V

Process-structure-property modeling for postbuild heat treatment of powder bed fusion Ti-6Al-4V

Fatigue database of additively manufactured alloys

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Contact Info

Product

Resources

About