A two-stage reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient. (January 2022)
- Record Type:
- Journal Article
- Title:
- A two-stage reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient. (January 2022)
- Main Title:
- A two-stage reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient
- Authors:
- Du, Yanbin
Wu, Guoao
Tang, Ying
Liu, Shihao - Abstract:
- Highlights: The two-stage procedure of reliability allocation for remanufactured machine tools. The construction of fault tree model for remanufactured machine tools. Integrating neural networks and remanufacturing coefficient. Abstract: 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 reliabilityHighlights: The two-stage procedure of reliability allocation for remanufactured machine tools. The construction of fault tree model for remanufactured machine tools. Integrating neural networks and remanufacturing coefficient. Abstract: 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. … (more)
- Is Part Of:
- Computers & industrial engineering. Volume 163(2022)
- Journal:
- Computers & industrial engineering
- Issue:
- Volume 163(2022)
- Issue Display:
- Volume 163, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 163
- Issue:
- 2022
- Issue Sort Value:
- 2022-0163-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-01
- Subjects:
- Remanufacturing -- Reliability allocation -- Machine tool -- FTA model -- Feedforward neural networks
Engineering -- Data processing -- Periodicals
Industrial engineering -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03608352 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cie.2021.107834 ↗
- Languages:
- English
- ISSNs:
- 0360-8352
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.713000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20363.xml