Development of FEA-ANN hybrid model for Equivalent Stress prediction of automobile structural member

Patel, Tushar M.; Bhatt, Nilesh M.

doi:10.3103/s0146411616050084

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The generation of the ANN output involved the formation of a neural network matrix with assigned weights and biases. This procedure includes applying the weight and bias matrix in tandem with the transfer function, as elaborated on by Patel and Bhatt (2016).…”

Section: Mathematical Model For Output Predictionmentioning

confidence: 99%

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Modi,

Patel

2024

Eng. Res. Express

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The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a Neural Network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network coupled with traditional backpropagation facilitates the nonlinear mapping of inputs to outcomes.In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, RMSE of 0.0011, and MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC.Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing the predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.

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Section: Mathematical Model For Output Predictionmentioning

confidence: 99%

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Modi,

Patel

2024

Eng. Res. Express

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“…Artficial Neural Network (ANN) and RSM have been used to predict important attributes of laser transmission welds such as weld width and weld strength. Some researchers have managed to compare the accuracy of these predictions against each other and some have used them to complement each other in developing hybrid models which will be more efficient as well as more accurate [8]. [9] proved that the combined effect of clamp pressure and laser power increases the lap shear strength while stand-off distance and weld speed have a decreasing effect on the weld strength of LTW of acrylic (polymathy methacrylate) using ANN.…”

Section: Introductionmentioning

confidence: 99%

Prediction of weld strength of LTW of Natural plant-based fiber reinforced Polypropylene using RSM, ANN, and ANFIS - a comparative study

Kapuyanyika,

Ude,

Chinnasamy

2024

Preprint

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During Laser Transmission welding (LTW) of thermoplastics reinforced with natural fibers, the constituents of the composite materials affect the properties of the material which relates to the appropriate design of operating parameters for attaining good-quality welds. Material composition affects the amount of laser energy absorbed, thermal properties, and optical properties. All these attributes greatly influence the properties and quality of weld joints formed from the LTW process. Given the wide range of LTW operating parameters that are feasible for joining thermoplastics, it is important to understand the effect of each parameter on important weld attributes such as weld strength and weld width track in a specific parameter design space for a given composite material and be able to predict vital weld properties such as weld strength. In this research paper, the comparison of the predicted weld strength of the LTW of Pure Polypropylene as the transparent part joined to Polypropylene doped with 0.2wt% of carbon black and reinforced with 15wt% oak wood short fibers using an empirical model based on Response Surface Method (RSM) central composite design, Artificial Neural Network (ANN) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) model was done. The coefficient of determination (R2) was (0.90), (0.93), and (0.99) for the RSM model, ANN model, and the ANFIS model respectively. All the prediction models exhibited acceptable mean absolute error percentage and root-mean-square error. The results suggested a good performance in predicting weld strength for the specified materials in the specified parameter design space in this study. ANFIS model was found to give the best performance followed by the ANN model and lastly the RSM model.

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“…NAIK et al (2013) was investigated on optimization of effective parameter of karanja biodiesel using the Taguchi method.in this study four parameter are selected molar ratio, catalyst type, catalyst concentration, reaction temperature.it is concluded that the percentage of yield of karanja biodiesel was improved using Taguchi Method [7]. PATEL et al (2013) used Taguchi method to reduce the weight of the chassis frame of Eicher 11.10 chassis frame and FEA was performed to obtain the best solution for chassis frame [8]. PATEL et al (2013) take up Plastic Pyrolysis Oil (PPO) blend for parametric improvement of CI engine using Taguchi method for the optimization of the Factors and experimental outcome clearly exhibit that 220 injection timing, injection pressure 200 bar, compression ratio 16 and engine load 20kg are optimum parameter setting for lowest break specific fuel consumption [8].…”

Section: IImentioning

confidence: 99%

Parametric Optimization of Variable Compression Ratio Diesel Engine for Karanja biodiesel and its Blend Using the Taguchi Method

Prajapati¹,

Patel²,

Patel³

2017

IOSR JMCE

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Development of FEA-ANN hybrid model for Equivalent Stress prediction of automobile structural member

Cited by 6 publications

References 5 publications

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Prediction of weld strength of LTW of Natural plant-based fiber reinforced Polypropylene using RSM, ANN, and ANFIS - a comparative study

Parametric Optimization of Variable Compression Ratio Diesel Engine for Karanja biodiesel and its Blend Using the Taguchi Method

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