Sintering, a well -established technique in powder metallurgy, plays a critical role in the processing of high melting point materials. A comprehensive understanding of structural changes during the sintering process is essential for effective product assessment. The phase -field method stands out for its unique ability to simulate these structural transformations. Despite its widespread application, there is a notable absence of literature reviews focused on its usage in sintering simulations. Therefore, this paper addresses this gap by reviewing the latest advancements in phase -field sintering models, covering approaches based on energy, grand potential, and entropy increase. The characteristics of various models are extensively discussed, with a specific emphasis on energy -based models incorporating considerations such as interface energy anisotropy, tensor -form diffusion mechanisms, and various forms of rigid particle motion during sintering. Furthermore, the paper offers a concise summary of phase -field sintering models that integrate with other physical fields, including stress/strain fields, viscous flow, temperature field, and external electric fields. In conclusion, the paper provides a succinct overview of the entire content and delineates potential avenues for future research.

Fatigue properties of materials by Additive Manufacturing(AM)depend on many fac-tors such as AM processing parameter,microstructure,residual stress,surface roughness,porosi-ties,post-treatments,etc.Their evaluation inevitably requires these factors combined as many as possible,thus resulting in low efficiency and high cost.In recent years,their assessment by leverag-ing the power of Machine Learning(ML)has gained increasing attentions.A comprehensive over-view on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials,as well as their dependence on AM processing and post-processing parameters such as laser power,scanning speed,layer height,hatch distance,built direction,post-heat temperature,etc.,were presented.A few attempts in employing Feedforward Neural Network(FNN),Convolu-tional Neural Network(CNN),Adaptive Network-Based Fuzzy Inference System(ANFIS),Sup-port Vector Machine(SVM)and Random Forest(RF)to predict fatigue life and RF to predict fatigue crack growth rate are summarized.The ML models for predicting AM materials'fatigue properties are found intrinsically similar to the commonly used ones,but are modified to involve AM features.Finally,an outlook for challenges(i.e.,small dataset,multifarious features,overfitting,low interpretability,and unable extension from AM material data to structure life)and potential solutions for the ML prediction of AM materials'fatigue properties is provided.

Magnetic field-assisted selective laser sintering (MFA-SLS) is an innovative technology of additive manufacturing that combines a laser beam with an external magnetic field to enable more degrees of freedom for tailoring microstructure. However, there still lacks a mesoscopic model to predict the microstructure evolution during MFA-SLS process. Herein, we develop a thermodynamically consistent non-isothermal phase-field model (PFM) to investigate the microstructure evolution during MFA-SLS process. The PFM is consistently derived from a thermodynamic framework invoking the microforce theory and Coleman-Noll procedure. The SLS temperature gradient effect is incorporated not only in the temperature-dependent parameters of PFM free energy, but also in the modified Cahn-Hilliard equation with an explicit temperature-gradient term. A magnetic field-related energy term for PFM is constructed to consider the interaction of differently oriented grains with the external magnetic field. PFM simulations of grain boundary (GB) migration in a prototypical non-magnetic material bismuth under a magnetic field are carried out. It is found that GB migration direction and velocity is significantly affected by the magnetic field and temperature gradient. It is also revealed that the external magnetic field in MFA-SLS can readily control the grain size distribution of the powder bed. The proposed PFM could provide a basis for the mesoscopic simulation of microstructure evolution in MFA-SLS.

Sintering, as a thermal process at elevated temperature below the melting point, is widely used to bond contacting particles into engineering products such as ceramics, metals, polymers, and cemented carbides. Modelling and simulation as important complement to experiments are essential for understanding the sintering mechanisms and for the optimization and design of sintering process. We share in this article a state-to-the-art review on the major methods and models for the simulation of sintering process at various length scales. It starts with molecular dynamics simulations deciphering atomistic diffusion process, and then moves to microstructure-level approaches such as discrete element method, Monte–Carlo method, and phase-field models, which can reveal subtle mechanisms like grain coalescence, grain rotation, densification, grain coarsening, etc. Phenomenological/empirical models on the macroscopic scales for estimating densification, porosity and average grain size are also summarized. The features, merits, drawbacks, and applicability of these models and simulation technologies are expounded. In particular, the latest progress on the modelling and simulation of selective and direct-metal laser sintering based additive manufacturing is also reviewed. Finally, a summary and concluding remarks on the challenges and opportunities are given for the modelling and simulations of sintering process.

The nonlinear elasticity and strain-tunable magnetocaloric effect (MCE) of antiferromagnetic monolayer MnPS3 are demonstrated by multiscale modeling and simulations. A continuum constitutive model expanding the elastic strain energy density by a Taylor series is formulated to describe the nonlinear and large-deformation elasticity of monolayer MnPS3. Fourteen independent elastic constants of the model are determined using the ab initio results. The nonlinear behavior is found to initiate at about 7% strain and nearly isotropic before the rupture. MCE (adiabatic temperature change increment T and isothermal entropy change - increment S) of monolayer MnPS3 is found anisotropic due to the anisotropy in the temperature derivative of magnetic susceptibility, i.e. stronger in-plane MCE. An in-plane biaxial strain could improve MCE by a factor of 1.5-2. The maximum - increment S and increment T occur at around 10 K and are evaluated as 2.8 mu J m-2 K-1 and 2.2 K, respectively, suggesting monolayer MnPS3 as a potential candidate for low-temperature (< 20 K) magnetic refrigeration. (c) 2022 Elsevier Ltd. All rights reserved.

Sintering, as a thermal process at elevated temperature below the melting point, is widely used to bond contacting particles into engineering products such as ceramics, metals, polymers, and cemented carbides. Modelling and simulation as important complement to experiments are essential for understanding the sintering mechanisms and for the optimization and design of sintering process. We share in this article a state-to-the-art review on the major methods and models for the simulation of sintering process at various length scales. It starts with molecular dynamics simulations deciphering atomistic diffusion process, and then moves to microstructure-level approaches such as discrete element method, Monte--Carlo method, and phase-field models, which can reveal subtle mechanisms like grain coalescence, grain rotation, densification, grain coarsening, etc. Phenomenological/empirical models on the macroscopic scales for estimating densification, porosity and average grain size are also summarized. The features, merits, drawbacks, and applicability of these models and simulation technologies are expounded. In particular, the latest progress on the modelling and simulation of selective and direct-metal laser sintering based additive manufacturing is also reviewed. Finally, a summary and concluding remarks on the challenges and opportunities are given for the modelling and simulations of sintering process.