Research on meta‐action decomposition and meta‐action unit modeling technology for electromechanical product

Self Cite

As a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

Section: Discussionmentioning

confidence: 99%

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Self Cite

“…The FMA decomposition method, one of the motion-oriented decomposition techniques, was developed to research the relationships among structures and among quality characteristics of complex mechatronic products. 55 The FMA decomposition method employs a top-down architecture decomposition technique to obtain meta-action units that are the minimal motion unit. 56 A meta-action unit is not capable of being further divided from the motion point of view and only embraces two simplest types, that is, translation and rotation meta-action units.…”

Section: Function-motion-action Decomposition Methodsmentioning

confidence: 99%

“…The FMA decomposition method, one of the motion‐oriented decomposition techniques, was developed to research the relationships among structures and among quality characteristics of complex mechatronic products 55 . The FMA decomposition method employs a top‐down architecture decomposition technique to obtain meta‐action units that are the minimal motion unit 56 .…”

Section: Preliminariesmentioning

confidence: 99%

Failure mode and effects analysis using function–motion–action decomposition method and integrated risk priority number for mechatronic products

Wang

Ran

et al. 2021

Self Cite

Failure mode and effects analysis is a widely applied risk assessment method in various engineering and management domains. However, the identification of failure modes is difficult and uncountable. Therefore, a function-motion-action (FMA) decomposition method is developed to identify failure modes from the perspective of motion and extraordinarily suitable for mechatronic products. In the typical risk assessment, the ranking orders of failure modes are determined by risk priority number (RPN), which has been criticized for several drawbacks and improved by some alternative RPNs, but some drawbacks still exist, such as duplicate values, narrow admissible value range, and missing failure modes' and risk factors' weights. This study formulates several alternative weighted RPNs to overcome the above drawbacks, and the final ranking orders of failure modes are garnered through the integrated RPN (IRPN). First, failure modes are identified via the proposed FMA decomposition method and evaluated with crisp values, whose weights are aggregated from the basic failure modes' weights. Second, the weights of the basic failure modes, risk factors and different RPN methods are derived from analytic hierarchy process. Third, the conditional weights of risk factors are determined by incorporating risk factors' weights and failure modes' conditional weights deduced from Shannon entropy. Next, several alternative weighted RPNs and IRPN are formulated to rank failure modes' risk levels. Finally, an illustrative example about computer numerical control machine center is presented to demonstrate the application and effectiveness of the proposed method.

“…When the quality characteristic values are larger than the target values, the growth rate of quality loss decreases gradually and tends to be flat until the quality characteristic values exceed the specification limits, then products should be repaired. Based on the above analysis, combining with Wu et al.’s ideas on model building, the quadratic exponential QLF is constructed as follows 30–34 :

L false(x false) = \{\begin{matrix} A_{1}, \\ k_{1} {false(x - T false)}^{2}, \\ k_{2} ((), 1 - e^{- x + T}), \\ A_{2}, \end{matrix} \begin{matrix} x < T_{l} \\ T_{l} \leq x < T \\ T \leq x \leq T_{u} \\ x > T_{u} \end{matrix},

where x represents the quality characteristic value, T refers to the target value and T l means lower functional limit, T u is the upper functional limit. A 1 is the loss of the characteristic below the lower functional limit, A 2 refers the loss of the characteristic above the upper functional limit, k 1 means quality loss coefficient in the left of target value and k 2 means quality loss coefficient in the right of target value.…”

Section: Reliability Analysis Methods and Improvement Of Qlfmentioning

confidence: 99%

Reliability analysis for mechanical parts considering hidden cost via the modified quality loss model

Mao

Liu

et al. 2020

The improvement of mechanical parts inherent reliability has an impact on the reputation and performance of the company. To estimate the inherent reliability of products more conveniently and economically, a hidden quality cost‐production cost (HQC‐PC) reliability prediction model is put forward. To estimate the hidden quality cost (HQC) of products more accurately, a quadratic exponential quality loss function model is established, which is different from Taguchi's quadratic quality loss function (QLF) and the modified QLFs. In the new quality loss model, the growth rate of quality loss on both sides of the target value is considered. Under the condition that the quality characteristic value obeys normal distribution, the general estimation formulas of HQC in the tolerance range is obtained considering sampling error and the numerical model of inherent reliability is established. The effect of different parameters on the inherent reliability of products is discussed with practical case, such as design and production parameters. Then, the appropriate process capability index (PCI) is selected according to different production processes. The relationship between the HQC‐PC reliability prediction model and PCI is derived by a numerical model of inherent reliability. A new analysis method of inherent reliability is proposed.