SONG Chun-yue
,
HU Kai-lin
,
LI Ping
钢铁研究学报(英文版)
A new scheduling model for the bulk ore blending process in iron-making industry is presented, by converting the process into an assembly flow shop scheduling problem with sequence-depended setup time and limited intermediate buffer, and it facilitates the scheduling optimization for this process. To find out the optimal solution of the scheduling problem, an improved genetic algorithm hybridized with problem knowledge-based heuristics is also proposed, which provides high-quality initial solutions and fast searching speed. The efficiency of the algorithm is verified by the computational experiments.
关键词:
bulk ore blending
,
assembly flow shop
,
sequence-depended setup time
,
limited intermediate buffer
,
genetic algorithm
LIU Gu
,
WANG Liu-ying
,
CHEN Gui-ming
,
HUA Shao-chun
钢铁研究学报(英文版)
Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel. Experiments were carried out to characterize the hardening qualities. A predicting and optimizing model using genetic algorithm-back propagation neural network (GA-BP) was developed based on the experimental results. The non-linear relationship between properties of hardening layers and process parameters was established. The results show that the GA-BP predicting model is reliable since prediction results are in rather good agreement with measured results. The optimal properties of the hardened layer were deduced from GA. And through multi optimizations, the optimum comprehensive performances of the hardened layer were as follows: plasma arc current is 90 A, hardening speed is 22 m/min, plasma gas flow rate is 60 L/min and hardening distance is 43 mm. It concludes that GA-BP mode developed in this study provides a promising method for plasma hardening parameters prediction and optimization.
关键词:
plasma transferred arc
,
surface hardening
,
optimization
,
neural network
,
genetic algorithm
