Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process

Yaghoobi, Amirreza; Bakhshi-Jooybari, Mohammad; Gorji, Hamid; Baseri, Hamid

doi:10.1007/s00170-016-8349-2

Cited by 44 publications

(21 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANFIS combines the dual benefits of fuzzy logic and the neural network in a single framework, which enables it to solve complex problems in a way that optimal parameters of membership function are obtained from input to output mapping [15,31]. It is a variant of the Takagi-Sugeno fuzzy inference system [32,33].…”

Section: Theory Of Anfismentioning

confidence: 99%

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Olatunji

Akinlabi

Madushele

et al. 2020

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making. Keywords GA-ANFIS • Biomass feedstock • Efficient utilization • Elemental composition 1 Introduction As of 2017 the United Nation, UN projected population growth from 7.6 billion to 9.8 billion by 2050 [1]. The clean and sustainable energy is the viable solution

show abstract

Section: Theory Of Anfismentioning

confidence: 99%

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Olatunji

Akinlabi

Madushele

et al. 2020

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…The optimal estimation of the membership function parameters is achieved by dual advantages of fuzzy logic and artificial neural network. This enable the ANFIS to be able to solve complex and nonlinear problems [34,36]. The fuzzy rules are based on first order Takagi-Sugeno fuzzy inference as follows:…”

Section: Fundamental Principles Of Anfismentioning

confidence: 99%

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Olatunji¹,

Akinlabi²,

Madushele³

et al. 2019

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socioeconomic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H 2 O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).

show abstract

“…Body-in-white passenger compartment frame, cross and side beams, front-end structure, roof structure, panels 23~28 Data classification and clustering, pattern recognition, and machine learning are important problems in a variety of engineering and scientific disciplines, such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing [10][11][12][13][14]. Numerous artificial intelligence techniques have been used to optimize of various problems in the fields of mechanical and automobile engineering including sensing and control of unmanned vehicles [15,16], fault diagnosis of machineries [17,18], and manufacturing applications [19,20].…”

Section: System Major Components In System Mass Breakdown (%)mentioning

confidence: 99%

Roof-Crush Protection Design of Automotive Bodies Using Clustering and Pattern Recognition

et al. 2019

View full text Add to dashboard Cite

Computer-aided engineering (CAE) tools play an indispensable role in the vehicle development process. However, it is difficult to accurately predict the relationships and behavior of automotive bodies in vehicle crashes owing to high-order nonlinearity and numerous design variables of the automotive body structure. In this study, clustering and pattern recognition techniques were used to develop a novel optimization design of an automotive body considering roof crushing by vehicle rollover. The large-scale data were clustered to find the strong and weak clusters, and new response surface models were acquired by clustering analysis to achieve better performance than the response surface model of traditional optimization. For an efficient robust design, clusters with weak performance were excluded from the optimum solution. Finally, it was confirmed that the solutions by the proposed optimization technique were better than those obtained by the traditional optimum method based on a comparative analysis by various cluster combinations.

show abstract

Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process

Cited by 44 publications

References 24 publications

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Roof-Crush Protection Design of Automotive Bodies Using Clustering and Pattern Recognition

Contact Info

Product

Resources

About