Intelligent Analysis Method of Mechanical Structure Performance

Qian, Wenxue; Yin, Xiaowei; Xie, Liyang; Huang, Dayan

doi:10.1109/ifita.2009.536

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Partheepan et al [ 17 ] conducted a study using a tiny specimen test and a neural network to assess fracture toughness. Qian et al [ 18 ] also described an ANN‐based method for analyzing the performance of complicated mechanical structures with many random load applications, which demonstrated that such a method overcomes the drawback of FEA computation time and simplifies performance analysis. To forecast the compressive strength of concrete containing flying ash, Topçu and Sardemir [ 19 ] used ANNs and fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Singh

Majumder

Bera

2022

Polymer Engineering & Sci

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The quality of polypropylene is one of the major components in the plastic industry. The quality of the polypropylene depends upon the melt flow index and the xylene solubility under the given condition such as hydrogen flow, donor flow, pressure, temperature, etc. This study investigates the use of artificial neural network (ANN) modeling for the prediction of the quality of polypropylene for petrochemical plants. This study proposes an ANN model to predict the “melt flow index (MFI) and xylene solubility” as the quality of polypropylene depends upon these two factors. Hydrogen (H2) flow, pressure, temperature, and donor flow are the controlling parameters for the MFI and xylene solubility. The study proposes a new approach for the selection of the best topology using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) multicriterion decision‐making (MCDM) process for the ANN technique. Experimental data are trained with three training algorithms each with four combinations of training functions and each with three combinations of numbers of neurons. In this study, the best model for ANN is found by the Levenberg–Marquardt backpropagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer functions. The best topology for the ANN model for prediction is selected by using the TOPSIS method. Some sensitivity analyses are provided graphically to show the physical nature of the problem.

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Section: Introductionmentioning

confidence: 99%

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Singh

Majumder

Bera

2022

Polymer Engineering & Sci

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“…Development of the finite element method began in earnest in the middle to late 1950s for airframe and structural analysis and gathered momentum at the University of Stuttgart through the work of John Argyris and at Berkeley through the work of Ray W. Clough in the 1960s for use in civil engineering by late 1950s, the key concepts of stiffness matrix and element assembly existed essentially in the form used today and NASA issued request for proposals for the development of the finite element software NASTRAN in 1965. The method was provided with a rigorous mathematical foundation in 1973 with the publication of Strang and Fix's An Analysis of The Finite Element Method, and has since been generalized into a branch of applied mathematics for numerical modeling of physical systems in a wide variety of engineering disciplines, such as thermodynamics and fluid dynamics [13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis of Slide Vane Compressor Stator Based on Finite Element Method

Qian

Yin

Xie

2014

AMM

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Mode analysis is important for slide vane compressor stator. Because of period force may set up resonance in the stator. In this paper, the stress distributions and vibration modes of stator of sliding vane compressor were studied through finite element method (FEM). The first-order vibration mode to five-order vibration mode of stator were computed. The results showed that the resonance frequencies are much larger than the working frequency. It is significant for sliding vane compressor design, fatigue life prediction and reliability assessment.

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“…Usually a lot of hypothesis should be done, but it often causes many mistakes. Industry can progress effectively with the knowledge and implementation of reliability engineering [1][2][3][4][5]. Gears are wildly used in engineering, and it is a common work for an engineer to build a 3-D model of a gear with modeling software.…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of a Gear Based on Moment Method

Yin

Qian

Xie

2011

AMM

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Gears are widely used in many engineering fields such as mining machinery, aerospace, transportation and petrochemical and are common and important parts for engineering, but for some gears it is still tedious to build the 3D models and assess their reliability. It is important for a gear to do reliability assessment, especially for some gear that under complex load conditions. In order to improve the reliability and stabilization, a gear assessment method is introduced and an example is given, and the example shows that do reliability assessment is useful and effective.

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Intelligent Analysis Method of Mechanical Structure Performance

Cited by 7 publications

References 7 publications

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Dynamic Analysis of Slide Vane Compressor Stator Based on Finite Element Method

Reliability Assessment of a Gear Based on Moment Method

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