Results 151 to 160 of about 13,278 (264)

Dynamic job shop scheduling under multiple order disturbances using deep reinforcement learning. [PDF]

Sci Prog
Sun Z, Han W, Gao L, Zhu C, Lyu Q.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

An improved scatter search algorithm for solving job shop scheduling problems with parallel batch processing machine. [PDF]

Sci Rep
Wang H, Xiong H, Zuo W, Shi S.
europepmc   +1 more source

Order Management and Completion Date Prediction of Manufacturing Job-Shop Based on Deep Learning.

Comput Intell Neurosci, 2022
Wang M.
europepmc   +1 more source

Innovative Processing of Compacted Waste Aluminum Alloy Powders via Controlled Remelting and Solidification

Advanced Engineering Materials, EarlyView.
This study demonstrates an efficient recycling route for out‐of‐spec AlSi10Mg atomized powders through compaction and arc remelting followed by suction casting. By correlating compaction load, cooling rate, and resulting microstructure, we show that intermediate pressures (50–80 kN) and rapid cooling refine dendrites, reduce porosity, and enhance ...
Mila Christy de Oliveira, Marcella Gaute Cavalcante Xavier, Abdoul‐Aziz Bogno, Piter Gargarella, José Eduardo Spinelli   +4 more
wiley   +1 more source

Data-driven automated job shop scheduling optimization considering AGV obstacle avoidance. [PDF]

Sci Rep
Tang Q, Wang H.
europepmc   +1 more source

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model. [PDF]

PLoS One, 2020
Tian X, Liu X, Zhang H, Sun M, Zhao Y.
europepmc   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Dual-self-learning co-evolutionary algorithm for energy-efficient flexible job shop scheduling problem with processing- transportation composite robots. [PDF]

Sci Rep
Zhang M, Zhou M, Zhang L, Zhang Z.
europepmc   +1 more source

Tailored Hierarchical Porous Copper Architectures via Three Dimensional Printing and Pressure‐less Sintering for Next‐Generation Lithium‐Metal Batteries

Advanced Engineering Materials, EarlyView.
A hierarchical porous copper current collector is fabricated via three‐dimensional printing combined with pressureless sintering to stabilize lithium metal anodes. The interconnected architecture lowers local current density, guides uniform Li deposition within pores, and suppresses dendrite growth.
Alok Kumar Mishra, Mukul Shukla
wiley   +1 more source