Effects of Nozzle Details on Print Quality and Hardened Properties of Underwater 3D Printed Concrete.

This study developed a 3D concrete printing (3DCP) system that can print not only in air but also underwater. This underwater 3DCP system is equipped with many distinct technologies, such as a technology to supply the printing material to the nozzle tip at a constant rate by detecting its amount in the printer hopper. Using the developed 3DCP system, the effect of nozzle details on underwater print quality and hardened properties was investigated.

Scalable Scheduling of Semiconductor Packaging Facilities Using Deep Reinforcement Learning.

Reinforcement learning (RL) has emerged as a promising approach for scheduling semiconductor operations. Yet, it is still challenging to solve large-scale scheduling problems based on an RL method since learning complexity grows fast as the size of shop floor increases. This challenge becomes more apparent when solving the scheduling problems with a diverse number of job types, which leads to the difficulties in exploration and function approximation in RL.