PREDICTION OF MECHANICAL PROPERTIES OF 25CrMo48V SEAMLESS TUBE USING NEURAL NETWORK MODEL

摘要：

In this investigation, a neural network model was established to predict mechanical properties of 25CrMo48V seamless tubes. The sensitivity analysis was also performed to estimate the relative significance of each chemical composition in mechanical behavior of steel tubes. The results of this investigation show that there is a good agreement between experimental and predicted values indicating desirable validity of the model. Among those alloying elements, the elements of carbon, silicon and chromium tended to play a more important role in controlling both the yielding strength and the Charpy-V-Notch transverse impact toughness. In comparison, the impurities such as O, N, S and P have a relatively weak impact. More detailed dependences of mechanical properties on each chemical composition in isolation can be revealed using the established model. The well-trained neural network has a great potential in designing tough and ultrahigh-strength seamless tubes and modeling the on-line production parameters.

关键词： Artificial neural network seamless tubing and casing mechanical properties sensitivity

作者: LAIBO SUN CHUANYOU ZHANG QINGFENG WANG MINGZHI WANG ZESHENG YAN

作者单位: State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao Technical Center, Tianjin Pipe (Group) Corporation limited, Tianjin300301, P.R.China

会议类型: 国际会议

会议名称: 第五届先进材料与加工国际会议(Fifth International Conference on Advanced Materials and Processing ICAMP-5)

会议地点: 哈尔滨

会议语种:英文

页码: 1074-1079

在线出版日期: 2008-09-03（万方平台首次上网日期，不代表论文的发表时间）