Numerical simulation of temperature field and thermal stress field in the new type of ladle with the nanometer adiabatic material

With the development of metallurgical industry and the improvement of continuous casting technology, the processing properties of casting technology equipment are being paid more attention. Ladle is one of the most representatives of the furnace equipment; higher requirements of ladle are put forward in response to the call for national energy-saving and emission reduction. According to the requirements of actual operator and working condition, a lining structure of a new type of ladle with nanometer adiabatic material is put forward. Based on heat transfer theory and finite element technology, the three-dimensional finite element model of a new type of ladle is established. Temperature field and stress field of the new type of ladle with the nanometer adiabatic material in lining structure after baking are analyzed. The results indicate that the distributions of temperature and thermal stress level of working layer, permanent layer, and nanometer heat insulating layer are similar, and they are in the permissible stress and temperature range of each material for the new type of ladle. Especially heat preservation effect of nanometer adiabatic material is excellent. Furthermore, the maximum temperature of shell for the new type of ladle drops to 114°C than the traditional ladle, and the maximum stress of shell for the new type of ladle is lower than the traditional ladle, that is, 114 MPa. It can provide reliable theory for energy-saving and emission reduction of metallurgy industry, which also points out the right direction for the future development of the iron and steel industry

Learning for a robot:deep reinforcement learning, imitation learning, transfer learning

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed