Results 271 to 280 of about 161,061 (357)
Ni‐base superalloys produced using additive manufacturing (AM) have a different response to heat treatments when compared to their conventional counterparts. Due to such unpredictability, various alloys with industrial interest are currently overlooked in most prior AM research.
Guilherme Maziero Volpato +6 more
wiley +1 more source
Production scheduling with multi-robot task allocation in a real industry 4.0 setting. [PDF]
Shakeri Z +4 more
europepmc +1 more source
Additive manufacturing (AM) transforms space hardware by enabling lightweight, high‐performance, and on‐demand production. This review outlines AM processes—powder bed fusion (PBF), directed energy deposition (DED), binder jetting (BJ), sheet lamination (SL), and material extrusion (ME)—applied to propulsion, satellite structures, and thermal devices ...
Stelios K. Georgantzinos +8 more
wiley +1 more source
Centralized scheduling, decentralized scheduling or demand scheduling? How to more effectively allocate and recycle shared takeout lunch boxes. [PDF]
Bai Y, Liu D, Ma J.
europepmc +1 more source
Magnetoactive Metamaterials: A State‐of‐the‐Art Review
Magnetoactive metamaterials combine magnetoactive composites with architected metastructures to enable contactless, tunable control of mechanical, acoustic, and elastic properties. This review highlights recent advances in their design, fabrication, and applications in soft robotics, biomedical devices, and adaptive structures and outlines future ...
Seyyedmohammad Aghamiri, Ramin Sedaghati
wiley +1 more source
An improved intelligent optimization algorithm for small-batch order production scheduling. [PDF]
Zhang X, Wang Z, Zhang D, Xu T, Jiang H.
europepmc +1 more source
Real‐time imaging and energy‐dispersive diffraction during solidification of Sn‐Bi alloy interconnect for electronic packaging applications are studied. Sn‐Bi solder alloys have generated significant interest in recent times due to their potential use in electronic packaging.
Amey Luktuke +3 more
wiley +1 more source
Dynamic job shop scheduling under multiple order disturbances using deep reinforcement learning. [PDF]
Sun Z, Han W, Gao L, Zhu C, Lyu Q.
europepmc +1 more source
Automatic algorithm configuration for flow shop scheduling problems
Artur Brum
openalex +1 more source

