Atomic simulations of melting behaviours for TiAl alloy nanoparticles during heating

Xiangyang, Yin; Yao, Qi; Liu, Junjun; Zhang, Lin

doi:10.1007/s12034-020-02193-5

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Process parameters are of interest as ML models can help predict and optimize these with respect to various quality metrics. As a result, many researchers have focused on parameter prediction or optimization using ML techniques [92][93][94]. Process parameters related to deposition [95], material [40,96], and energy source [39,54,97] have been a common target of ML models at this stage of AM lifecycle.…”

Section: Process Parameters and Process Statesmentioning

confidence: 99%

“…In one application, spectrum data of acoustic signals is used to predict geometric defects in PBF printed parts [74]. In this regard, ML models have been employed for a range of tasks including pore detection [34,42,44,68,[141][142][143][144], pore classification [50,57,67,145,146], and pore size prediction [93]. Some ML applications also deal with the classification of specific microstructure types [51].…”

Section: Macro Levelmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

et al. 2022

View full text Add to dashboard Cite

Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications on AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods and suggestions are given.

show abstract

Section: Process Parameters and Process Statesmentioning

confidence: 99%

Section: Macro Levelmentioning

confidence: 99%

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

et al. 2022

View full text Add to dashboard Cite

show abstract

Computational Modeling of Additive Manufacturing—Overview, Principles, and Simulations in Different Scales

Sivaraj,

Nitheesh Kumar,

Karthik

2024

Additive Manufacturing With Novel Materials

View full text Add to dashboard Cite

Effects of heating rate and sintering temperature on the tensile properties of sintered γ-Ti/Al nanoparticle chains

Zhang,

Jiang,

Han

et al. 2024

J. Phys. D: Appl. Phys.

View full text Add to dashboard Cite

This paper presents the results of molecular dynamics simulations that were performed to numerically study the laser sintering process and mechanical behavior of γ-Ti/Al bimetallic alloy nanoparticles (NPs). The study systematically investigates the effects of heating rate and sintering temperature on the resultant uniaxial tensile performances of the sintered NPs. A chain model was formed by connecting three pre-equilibrated Ti/Al NPs via necks during solid-state sintering. The solid-state sintered chain samples were heated to 1798 K using four different heating rates (0.04, 0.2, 0.5, and 1.0 K/ps). After high-temperature relaxation of selected sintering temperature cases (e.g., 398 K, 598 K, etc. with a 200 K interval) for 10 ns, the heat sintered chain samples underwent a solidification process with a cooling rate of 0.08 K/ps and maintained at 298 K for an additional 1 ns. The resulting sintered chain products were then subjected to uniaxial tension at a strain rate of 0.0001/ps. The thermodynamic properties and crystallographic deformation were investigated during the sintering and subsequent tension processes. Analysis of the yield strengths obtained from the tension tests revealed a statistically significant correlation between the tensile strength of the sintered NPs and the pre-established sintering temperatures at each temperature. This observation indicates that higher sintering temperatures strengthen the neck connections within the NP-chains, leading to greater tensile strength. The higher sintering temperatures can reinforce the neck during high-temperature relaxation. It is worth noting that the effect of heating rates on mechanical properties was less pronounced when the sintering temperature was constant.

show abstract

Atomic simulations of melting behaviours for TiAl alloy nanoparticles during heating

Cited by 4 publications

References 32 publications

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

Computational Modeling of Additive Manufacturing—Overview, Principles, and Simulations in Different Scales

Effects of heating rate and sintering temperature on the tensile properties of sintered γ-Ti/Al nanoparticle chains

Contact Info

Product

Resources

About