Scripta Materialia最新文献

An extraordinary elongation of over 156 % was achieved in single crystal Fe-15Mn-10Cr-8Ni-4Si alloy, which shows γ → ε → α’ transformation. Tensile tests were conducted along the 〈414〉, 〈111〉, and 〈100〉 directions, and the deformed microstructure was characterized with electron-backscattering diffraction (EBSD). Elongation in 〈414〉 orientation (156 %) was highest as ever recorded in metallic alloy—approximately twice that of conventional TWIP steels. Mechanical behavior in 〈414〉 is characterized with slow work hardening, with upward curvature in the stress–strain curve. EBSD analysis revealed 45 % of ε-martensite and significant γ-twin microstructure components in 〈414〉. In initial stage, deformation was marked by planar slip and a sluggish γ → ε transformation, activating a single ε-martensite variant with a Schmid factor of 0.5. Later stages witnessed crystal rotation towards 〈112〉, generating multiple shear bands and distinct ε variants. Fractures predominantly occurred along the 〈011〉 direction, with the fracture surface exhibiting a ductile nature.

Topochemical fluorination of oxides recently gained interest as it opens the way to substantial structural modifications and drastic changes in physical properties. We present a study of La₄Co₃O₁₀F₂, synthesized via low-temperature topochemical methods using polyvinylidene difluoride (PVDF). Using synchrotron X-ray powder diffraction, we find the compound to crystallize in a monoclinic cell (A2/a, with a = 5.32969(9) Å, b = 5.37555(1) Å, c = 30.5958(6) Å and β = 90.994(1) °), where the fluoride anions occupy the rock-salt layer's interstitial sites, inducing a tilting of the CoO₆ octahedra. The physical properties of this novel compound are compared with those of the parent compound La₄Co₃O₁₀, evidencing changes in resistivity and magnetism, highlighting the possibility of tuning magnetic interactions and electronic correlations through PVDF fluorination.

Machine learning (ML) has generated great interest in additive manufacturing (AM) thanks to its ability to predict complex patterns and behaviors through data. Examples include design optimization, process control, and cost minimization. In this paper, we develop a novel framework based on reinforcement learning (RL) for porosity prediction in metal laser-powder bed fusion (L-PBF). The novelty of this approach is twofold: it is the first approach that integrates RL in L-PBF for porosity prediction where the state space consists of permutations of three parameters (laser power, scan speed, and hatch spacing) for optimal parameter combinations; furthermore, through an appropriately formulated reward function, we embed physics-informed principles based on the Eagar-Tsai thermal model for training. The proposed framework has been experimentally validated on L-PBF high-strength A205 Al alloy. The experimental results demonstrated high fidelity with the predicted optimal parameters, despite few outliers, demonstrating the potential of this approach.

Optimization of mechanical properties in La₂Zr₂O₇ (LZO) ceramics, composites and coatings is an on-going requirement for their practical application. Herein, the contribution of monoclinic (La, Gd)NbO₄ (m-LGNO) enhancement of fracture toughness by ∼56% reveals its capability to be a prominent toughening agent. Due to the ferroelastic nature of LGNO, ferroelastic switching takes place within the stress concentrated regions, giving rise to significant strain energy relaxation. Atomic-scale evidence reveals that ferroelastic 94°/86° domain switching can occur, yielding merged 94° domains and newly formed 86° domains. The relevant strains induced by ferroelastic domain switching are quantified up to 8.06% and 6.20% in shear and normal strain, respectively. Such domain switching strains highlight their contribution to accommodate external mechanical loading for the 50 mol% GNO-LZO composite. The results indicate that the unique ferroelastic nature and 94°/86° ferroelastic domain switching in m-LGNO cooperatively provide a significant toughening effect.