摘要
针对废旧零部件质量状况差异性导致的再制造工艺路线和工艺时间不确定性问题,建立基于模糊Petri网的废旧零部件再制造工艺过程模型,建立一个废旧零部件不确定性再制造工艺时间的模糊学习系统,基于废旧零部件的质量状况信息对其再制造工艺时间开展模糊学习。同时引入一种自适应学习机制对再制造工艺时间开展参数统计和更新,不断提高预测结果对实际再制造系统动态变化的适应性。将所建立的模糊学习系统应用到某废旧机床主轴的再制造实践中,运用Matlab软件开展仿真应用。
In view of the uncertainties inherent in remanufacturing process, a fuzzy petri net based remanufacturing process model is proposed to explicitly represent the variations in remanufacturing process routings and process time with respect to product conditions. A fuzzy learning system with an adaptive learning mechanism is designed to estimate the remanufacturing process time of used component under specific conditions, and to dynamically reshape the distributions of actual process time to improve future predictions. The proposed method is illustrated through the remanufacturing of a batch of used lathe spindles with a Matlab-based simulation system.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2013年第15期137-146,共10页
Journal of Mechanical Engineering
基金
国家自然科学基金(51105395)
国家科技支撑计划(2012BAF02B03)
中央高校基本科研业务费专项资金(CDJZR12110076)资助项目
关键词
再制造
不确定性
工艺时间
模糊学习
废旧零部件
Remanufacturing Uncertainties Process time Fuzzy learning Used component
