Large quantities of data are accumulated in process planning for body in white （BIW）. To acquire thepotential and valuable process knowledge from these data, the rough set theory and association rule technique arein...Large quantities of data are accumulated in process planning for body in white （BIW）. To acquire thepotential and valuable process knowledge from these data, the rough set theory and association rule technique areintegrated to discover the useful correlations between the welding type and process requirements. The correlationscan guide us to select the welding type according to the given process requirements. During data mining, everyprocess requirement is regarded as an attribute. First, the decision table for the welding type is constructed. Sec-ond, rough set theory is employed to remove redundant attributes. A simplified decision table is constructed.Third, association rule is used to extract the useful rules. Finally, an illustrative example indicates this methodol-ogy can extract useful rules for the selection of welding type.展开更多
Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is pre...Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.展开更多
基金support by Xinjiang special major project of science and technology [201130110]Xinjiang University doctor initial foundation [BS130119]
文摘Large quantities of data are accumulated in process planning for body in white （BIW）. To acquire thepotential and valuable process knowledge from these data, the rough set theory and association rule technique areintegrated to discover the useful correlations between the welding type and process requirements. The correlationscan guide us to select the welding type according to the given process requirements. During data mining, everyprocess requirement is regarded as an attribute. First, the decision table for the welding type is constructed. Sec-ond, rough set theory is employed to remove redundant attributes. A simplified decision table is constructed.Third, association rule is used to extract the useful rules. Finally, an illustrative example indicates this methodol-ogy can extract useful rules for the selection of welding type.
基金supported by 2013 Comprehensive Reform Pilot of Marine Engineering Specialty(No.ZG0434)
文摘Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.
文摘以激光峰值功率、焊接速度、脉冲宽度、离焦量为优化工艺参数,以焊接接头的抗拉强度、断后伸长率、焊缝熔深、焊缝宽度为综合优化工艺目标,运用正交试验与集对分析相结合的方法对6061铝合金脉冲激光焊接工艺进行了多目标优化.通过正交试验获得数据样本,利用集对分析法对数据分析以实现工艺参数的优化.首先确定单工艺目标与理想解的同一度、对立度、贴近度,然后以单工艺目标贴近度的权重和表示综合工艺目标的贴近度.最后根据不同工艺参数、不同工艺水平的综合工艺目标的平均贴近度确定最佳工艺.优化结果为:激光功率3.5 k W、焊接速度2.4 m/min、脉冲宽度4.0 ms、离焦量-1 mm.
