TY - JOUR
T1 - A two-stage reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient
AU - Du, Yanbin
AU - Wu, Guoao
AU - Tang, Ying
AU - Liu, Shihao
N1 - Funding Information:
We gratefully acknowledge the financial support of National Natural Science Foundation of China ( NSFC ) (Grant No. 51775071 ), the Key Projects of Strategic Scientific and Technological Innovation Cooperation of National Key R&D Program of China (No. 2020YFE0201000 ), the Innovative Research Group of Universities in Chongqing (No. CXQT21024 ) and the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJZD-K202000801 ).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/1
Y1 - 2022/1
N2 - Reliability allocation is an important task that needs to be done in the design phase of machine tool remanufacturing to ensure that remanufactured machine tools meet the reliability target. However, unlike new machine tool products, remanufactured machine tools have high uncertainty and small samples, and traditional reliability methods are not suitable for remanufactured machine tools. This paper aims to propose an improved reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient. With the fault tree analysis (FTA) model constructed, the fault of remanufactured machine tools can be divided into three levels: system-level, subsystem-level, and part-level. The three-layer feedforward artificial neural network is adopted to allocate system reliability to subsystem-level. When reliability is allocated from subsystem-level to part-level, the remanufacturing comprehensive evaluation system and a remanufacturing coefficient that takes into account the characteristics of remanufactured components are introduced. Finally, the proposed method is illustrated in a case of reliability allocation for remanufactured NC gear hobbing machines. Moreover, the results show that the reliability target can be achieved and the growth of reliability can be guaranteed through the proposed method.
AB - Reliability allocation is an important task that needs to be done in the design phase of machine tool remanufacturing to ensure that remanufactured machine tools meet the reliability target. However, unlike new machine tool products, remanufactured machine tools have high uncertainty and small samples, and traditional reliability methods are not suitable for remanufactured machine tools. This paper aims to propose an improved reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient. With the fault tree analysis (FTA) model constructed, the fault of remanufactured machine tools can be divided into three levels: system-level, subsystem-level, and part-level. The three-layer feedforward artificial neural network is adopted to allocate system reliability to subsystem-level. When reliability is allocated from subsystem-level to part-level, the remanufacturing comprehensive evaluation system and a remanufacturing coefficient that takes into account the characteristics of remanufactured components are introduced. Finally, the proposed method is illustrated in a case of reliability allocation for remanufactured NC gear hobbing machines. Moreover, the results show that the reliability target can be achieved and the growth of reliability can be guaranteed through the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85120340062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120340062&partnerID=8YFLogxK
U2 - 10.1016/j.cie.2021.107834
DO - 10.1016/j.cie.2021.107834
M3 - Article
AN - SCOPUS:85120340062
VL - 163
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
M1 - 107834
ER -