Results 161 to 170 of about 4,171 (266)

Lapping of Soft-Brittle Lithium Niobate Crystal with Fixed Abrasive Pad. [PDF]

Materials (Basel)
Zhu N, Gao X, Tang C, Chen J, Zhu Y.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

A 3D-Printed Compliant Polishing Tool for High-Efficiency Finishing of P20 Mold Steel. [PDF]

Materials (Basel)
Wang K, Liu X, Zhu M, Tang C, Su J, Chen J, Zhu Y.   +6 more
europepmc   +1 more source

Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

AIChE Journal, EarlyView.
Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate ...
Huitian Yu, Jiewen Zhao, Heiko Briesen
wiley   +1 more source

Atomic-Scale Mechanisms of Nanoscale Material Removal in FeCrNiCoCu High-Entropy Alloys: Coupled Effects of Crystallography, Grain Size, and Composition. [PDF]

Nanomaterials (Basel)
Ling X, Fu P, Li Y, Zhou Z, Li Z.
europepmc   +1 more source

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
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, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Towards Ultra-Precision Manufacturing: Advancements and Future Trends in Energy Field-Assisted Jet Machining. [PDF]

Micromachines (Basel)
He Y, Chen T, Man X, Su T.
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Comparative Analysis of AWJM Performance in FFF-Printed PLA and PLA-CF: Influence of Process Parameters and Cutting Regions. [PDF]

Polymers (Basel)
Mayuet Ares PF, Rodríguez-Parada L, de la Rosa S, Batista M.   +3 more
europepmc   +1 more source