A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

Cheng, Qiang; Sun, Bingwei; Zhao, Yongsheng; Gu, Peihua

doi:10.17531/ein.2016.4.10

Cited by 16 publications

(13 citation statements)

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“…The probabilistic technique using the Markov chain has the inherent capability of generating artificial synthetic data like the actual maximum and minimum stress history data (Cheng et al, 2016). The process of generating random artificial synthetic data is performed within the controlled boundary condition of the actual maximum and minimum loads obtained from the national automotive industry.…”

Section: Durability Schematic Frameworkmentioning

confidence: 99%

“…The stochastic process of using the Markov process has been employed to overcome the problems associated with missing data or large intervals by generating sequences of random data from the sampling data (Lu and Liu, 2011;Cheng et al, 2016). The results obtained from this probabilistic model have been used to model the scatters in loads that are related to the operational behaviour of the structure ( Jiang and Murthy, 2011;Jiang, 2014;Almalki and Nadarajah, 2014;Echard et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Durability analysis using Markov chain modeling under random loading for automobile crankshaft

Singh

Abdullah²

2019

IJSI

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Purpose The purpose of this paper is to present the durability analysis in predicting the reliability life cycle for an automobile crankshaft under random stress load using the stochastic process. Due to the limitations associated with the actual loading history obtained from the experimental analysis or due to the sensitivity of the strain gauge, the fatigue reliability life cycle assessment has lower accuracy and efficiency for fatigue life prediction. Design/methodology/approach The proposed Markov process embeds the actual maximum and minimum stresses by a continuous updating process for stress load history data. This is to reduce the large credible intervals and missing loading points used for fatigue life prediction. With the reduction and missing loading intervals, the accuracy of fatigue life prediction for the crankshaft was validated using the statistical correlation properties. Findings It was observed that fatigue reliability corresponded well by reporting the accuracy of 95–98 per cent with a mean squared error of 1.5–3 per cent for durability and mean cycle to failure. Hence, the proposed fatigue reliability assessment provides an accurate, efficient, fast and cost-effective durability analysis in contrast to costly and lengthy experimental techniques. Research limitations/implications An important implication of this study is durability-based life cycle assessment by developing the reliability and hazard rate index under random stress loading using the stochastic technique in modeling for improving the sensitivity of the strain gauge. Practical implications The durability analysis is one of the fundamental attributes for the safe operation of any component, especially in the automotive industry. Focusing on safety, structural health monitoring aims at the quantification of the probability of failure under mixed mode loading. In practice, diverse types of protective barriers are placed as safeguards from the hazard posed by the system operation. Social implications Durability analysis has the ability to deal with the longevity and dependability of parts, products and systems in any industry. More poignantly, it is about controlling risk whereby engineering incorporates a wide variety of analytical techniques designed to help engineers understand the failure modes and patterns of these parts, products and systems. This would enable the automotive industry to improve design and increase the life cycle with the durability assessment field focussing on product reliability and sustainability assurance. Originality/value The accuracy of the simulated fatigue life was statistically correlated with a 95 per cent boundary condition towards the actual fatigue through the validation process using finite element analysis. Furthermore, the embedded Markov process has high accuracy in generating synthetic load history for the fatigue life cycle assessment. More importantly, the fatigue reliability life cycle assessment can be performed with high accuracy and efficiency in assessing the integrity of the component regarding structural integrity.

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Section: Durability Schematic Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Durability analysis using Markov chain modeling under random loading for automobile crankshaft

Singh

Abdullah²

2019

IJSI

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“…It results from numerous research concerning i.e. the accuracy of the machined parts [5], or the stability of the high-speed machining process [30].…”

Section: Introductionmentioning

confidence: 99%

Assessment model of cutting tool condition for real-time supervision system

Kozłowski¹,

Mazurkiewicz²,

Żabiński³

et al. 2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Further development of manufacturing technology, in particular machining requires the search for new innovative technological solutions. This applies in particular to the advanced processing of measurement data from diagnostic and monitoring systems. The increasing amount of data collected by the embedded measurement systems requires development of effective analytical tools to efficiently transform the data into knowledge and implement autonomous machine tools of the future. This issue is of particular importance to assess the condition of the tool and predict its durability, which are crucial for reliability and quality of the manufacturing process. Therefore, a mathematical model was developed to enable effective, real-time classification of the cutting blade status. The model was verified based on real measurement data from an industrial machine tool.

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“…For example, Yang et al [26] provided a method of simulating the reliability of degradation using Monte Carlo principle and cloud theory. Cheng et al [6] proposed an approach to analyze the reliability evaluation based on fast Markov chain simulations. Leturiondo et al [12] presented a model to evaluate the system performance with localized damage.…”

Section: Introductionmentioning

confidence: 99%

Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

Dui¹,

Chen²,

Wu³

2017

EiN

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Generalized inteGrated importance measure for system performance evaluation: application to a propeller plane system uoGólniona miara zinteGrowanej ważności komponentów jako narzędzie oceny wydajności systemu: zastosowanie w odniesieniu do układu śmiGłowca : state vector of the components at time t ( ( )) X t Φ system structure function at time t and range {0,1, , [7,8] studied the IIM in system lifetime and semi-Markov process to evaluate the change of the system performance, respectively.The IIM evaluates the rate of system performance change due to a component changing from one state to another. The IIM simply considers the scenarios when the transition rate of a component from one state to another is constant. This may contradict the assumption of the degradation, based on which system performance is degrading and therefore the transition rate may be increasing over time.On the other hand, the Weibull distribution is one of the most commonly used lifetime distributions in reliability modeling and lifetime testing [25]. It has been used in many different engineering applications to model complex data sets, such as life tests [15], fault diagnosis [4,5], oral irrigators [16], et al. The Weibull distribution and its variants can accommodate increasing, constant or decreasing failure rates [10]. Thus, one may extend the IIM to a new importance measure that considers the scenarios where the transition rate of a component changing from one state to another as a time-dependent function. Typically, one may consider the conditional probability distribution of a component sojourning in a state is the Weibull distribution, given the next state that the component will jump to. On the basis of such consideration, this paper proposes a new importance measure. The research on the new importance measure can identify the most important component during three different time periods of the system lifetime, which is corresponding to the characteristics of Weibull distributions. The paper then derives some probabilistic properties. It also analyzes the properties of the proposed importance measure of the parallel-series systems and the series-parallel systems, respectively. A real-world example is borrowed to illustrate the proposed importance measure.The rest of the paper is as follows. Section 2 extends the IIM. The corresponding properties of IIM in the Weibull distribution are analyzed for typical parallel-series system and series-parallel system structures in Section 3. An application is presented to illustrate the proposed method in Section 4. Section 5 gives the conclusion of this paper. The extended IIM for system performance under Weibull distributionsIn this paper, the state space of component i is {0,1,…, M i } and the state space of the system is{0,1,…, M}. State 0 represents the complete failure state and state M (M i ) is the perfect functioning state. The states are ordered from the complete failure state to the perfect functioning state.We assume that the levels of maintenance cost and the system states ( 0 1) a...

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A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

Abstract: Cheng Q, Sun B, ZhAo Y, gu P. A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation. eksploatacja i niezawodnosc -Maintenance and Reliability 2016; 18 (4): 552-564, http://dx

Cited by 16 publications

References 32 publications

Durability analysis using Markov chain modeling under random loading for automobile crankshaft

Durability analysis using Markov chain modeling under random loading for automobile crankshaft

Assessment model of cutting tool condition for real-time supervision system

Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

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