Deep Learning‐Assisted Design of Mechanical Metamaterials
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +1 more source
Influence of Post-Processing on S-Phase Formation During Plasma Nitriding of Additively Manufactured Inconel 939. [PDF]
Maj P +7 more
europepmc +1 more source
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy +8 more
wiley +1 more source
A Multi-Response Investigation of Abrasive Waterjet Machining Parameters on the Surface Integrity of Twinning-Induced Plasticity (TWIP) Steel. [PDF]
Cavusoglu O.
europepmc +1 more source
Comparative evaluation of dry, wet, and minimum quantity lubrication (MQL) cooling strategies in the machining of GTD-450 martensitic stainless steel. [PDF]
Dastgerdi HM +5 more
europepmc +1 more source
Characteristics of Pulsed-Laser-Induced Layers with Cracks Prepared for SiC Grinding Processes. [PDF]
Li H, Jiang Y, Yang Y, Yang J, Zhu L.
europepmc +1 more source
Multi-criteria decision analysis approach on parametric optimization of abrasive waterjet pocket milling in Ti-6Al-4V alloy. [PDF]
Naresh Raj KL +4 more
europepmc +1 more source
Surface Roughness Uniformity Improvement of Additively Manufactured Channels' Internal Corners by Liquid Metal-Driven Abrasive Flow Polishing. [PDF]
Ma Y, Li K, Feng B, Zhang L.
europepmc +1 more source

