摘要
把产品质量特性分为产品策划级(VOC级)QCs(Quality Characteristics)、概念设计级(产品级)QCs、详细设计级(零部件级)QCs、产品制造级QCs4个阶段,以用户需求(VOC)技术指标为输入,构成四阶段逐级映射关系,进而利用人工神经网络(ANNs)技术求出VOC技术指标相对重要度和各级映射权重,然后提出基于ANNs技术的复杂机电产品关键质量特性提取模型。避免产品生产各个阶段不该重点控制的质量特性(QCs)以关键控制现象出现,应该重点控制的QCs以非关键控制现象出现。最后,应用实例验证了所提理论与方法的正确性和有效性。
Quality Characteristics （QC） are divided into product planning QCs , conceptual design QCs, detailed design QCs and manufacturing QCs. A kind of progressive reflecting-relationship of four stages QCs has been established by using Voice of the Customer（VOC） specifications as the input indicators. Through the statistics methods of Artificial Neural Networks （ANNs）, the relative importance of technical specifications of VOC and the reflecting weights of each level are obtained. An extraction model of the key quality characteristics of complicated mechanical and electrical products based on ANNs technology is formulated. The model can avoid putting non-key controlling QCs as key controlling ones in the process of manufacturing and vice versa. The correctness and effectiveness of the model are verified by application examples.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第2期8-14,共7页
Journal of Chongqing University
基金
国家863计划资助项目(2009AA04Z119)
国家自然科学基金资助项目(50835008)
数字制造装备与技术国家重点实验室(华中科技大学)开放基金资助项目
国家'高档数控机床与基础制造装备'科技重大专项资助项目(2009ZX04014-01)
关键词
关键质量特性
提取
四阶段逐级映射及映射权重
人工神经网络
key quality characteristics
extraction
progressive reflecting-relationship of four stages and reflecting weight
artificial neural networks
