Functional cause analysis of complex manufacturing systems using structure

Loganathan, M. K.; Gandhi, Minu Shikha; Gandhi, O.P.

doi:10.1177/0954405414528310

Cited by 19 publications

(7 citation statements)

References 19 publications

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“…28 When using FMEA tool, one identifies all the potential failures in the production cycle and the effects of these failures. 29,30 The tool also aids in the identification of the criticality of the failures. This makes the tool effective in the reliability prediction and planning of the production cycle.…”

Section: Reliability and Costmentioning

confidence: 99%

Failure mode–driven quality analysis of operational risks for composite fiber-reinforced plastic assembly

Suparayan

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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Composite fiber-reinforced plastic products have many failure modes in the manufacturing stage. The cost of rework is high. To ensure a reliable manufacturing process and outcome, this article presents a new method to prioritize assembly process errors according to a risk-based cost of poor quality. The method combines a range of reliability techniques including cause–effect diagram, fault tree, and analytic hierarchy process to achieve multiple failure mode assessment. The outcome of this research provides direct links to develop risk mitigation plans to treat the prioritized risks of production errors in the composite fiber-reinforced plastic assembly process.

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Section: Reliability and Costmentioning

confidence: 99%

Failure mode–driven quality analysis of operational risks for composite fiber-reinforced plastic assembly

Suparayan

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…When using FMEA tool, one identifies all the potential critical failures in the operating cycle and the effects of these failures [23]. Li & Zeng [24] proposed a supplier selection method that applied FMEA to assess the risks in the decision process.…”

Section: Failure Modes Identificationmentioning

confidence: 99%

Reliability Based Maintenance Planning of Wind Turbine Using Bond Graph

Mo¹,

Chan²

2017

ujme

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Wind power generation is an effective form of clean, renewable energy which operates both on land and offshore. The primary means of converting wind to power is by wind turbines. The issue with wind turbines is high uncertainty of operating environment resulting in low life cycle reliability. Frequent breakdown failures resulting in reactive maintenance is costly which results in downtime and loss of production. FMEA has been used in some cases to develop maintenance schedule but the effect is minimal. This paper investigates a new method of determining faults from adverse operating conditions by bond graph model and combines with the concept of failure mode and effects analysis to simulate the effects of maintenance strategies on the mean time to failure of wind turbine components. The mean time to failure data can then be used to refine the maintenance task with additional inspections and replacements that can prevent breakdown failures and maximize the utilization rate of expensive components.

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“…Zhou and Jiang 23 proposed a systematic variation source identification method for deep-hole boring process based on multi-source information fusion using Dempster-Shafer (D-S) evidence theory. Loganathan et al 24 presented a function event digraph model for functional cause analysis of complex manufacturing system by considering its input and output functions and their interrelations. However, it still seems to be a significant challenge to realize the process fluctuation analysis and the identification of fluctuation sources in small-batch production.…”

Section: Literature Reviewmentioning

confidence: 99%

Fluctuation evaluation and identification model for small-batch multistage machining processes of complex aircraft parts

Wang

Jiang

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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The key to improve the machining quality of workpiece is to decrease the process fluctuation, which requires identifying the fluctuation sources first. For small-batch multistage machining processes of complex aircraft parts, how to identify the fluctuation sources efficiently has become a difficult issue due to the limited shop-floor data and the complicated interactive effects among different stages. Aiming at this issue, a fluctuation evaluation and identification model for smallbatch multistage machining process is proposed based on the sensitivity analysis theory. In order to improve the data utilization, an analytical structure of the fluctuation evaluation and identification model for small-batch multistage machining process is presented, which comprises four levels, namely, part level, multistage level, single-stage level and quality feature level. Corresponding to the four levels in the analytical structure, four fluctuation analysis indices are proposed to quantitatively evaluate the fluctuation level of different parts and identify the weak stages and elements that result in the abnormal fluctuation in the process flow. A five-stage deep-hole machining process of aircraft landing gear is used as a case to verify the proposed model.

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Functional cause analysis of complex manufacturing systems using structure

Cited by 19 publications

References 19 publications

Failure mode–driven quality analysis of operational risks for composite fiber-reinforced plastic assembly

Failure mode–driven quality analysis of operational risks for composite fiber-reinforced plastic assembly

Reliability Based Maintenance Planning of Wind Turbine Using Bond Graph

Fluctuation evaluation and identification model for small-batch multistage machining processes of complex aircraft parts

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