Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm

Nwobi-Okoye, Chidozie Chukwuemeka; Okonji, Patrick C.; Okiy, Stanley

doi:10.1080/23311916.2019.1681055

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…ANNs also benefit from being able to process numerous data formats. It is flexible enough to take in a wide variety of numerical, category, and ordinal features [22]. This adaptability is particularly useful when working with heterogeneous datasets that include multiple forms of data.…”

Section: Why Use Annmentioning

confidence: 99%

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Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

et al. 2023

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Context: Network-on-Chips (NoCs) serve as essential interconnection infrastructures in Multi-processor System-on-Chip (MPSoC) designs, emphasizing flexibility, extensibility, and low power consumption. The effectiveness of communication within NoCs relies heavily on the routing algorithm employed. However, the routing process faces significant challenges, such as deadlock, livelock, congestion, and faults, which impact the Design Space Exploration (DSE) process. Objective: In this research, we propose a prediction model-based ANN with a Metaheuristic Optimization approach for predicting the utilized routing algorithm by the NoC-based MPSoC platform during the DSE in order to reduce the time required to specify the NoC-based MPSoC platform configurations. Method: The main idea of the proposed method is to develop a prediction model, specifically an Artificial Neural Network (ANN) optimized using the Guaranteed Convergence Arithmetic Optimization Algorithm (GCAOA-ANN), for predicting the utilized routing algorithm in NoC-based MPSoC platform during the DSE process. The methodology consists of two phases. Firstly, an automated strategy is proposed to execute and control the NOXIM simulator, enabling the simulation and collection of NoC-based MPSoC scenarios under different constraints, rules of NoC design (including routing protocols), and performance metrics. Consequently, NoC traffic data is gathered. Secondly, building upon the work of previous researchers, who have suggested the development of hybrid models to improve accuracy, this research introduces a novel methodology incorporating an ANN optimized using the GCAOA-ANN to predict the routing algorithm employed by the NoC-based MPSoC. Results:The hybrid GCAOA-ANN model demonstrated superior performance compared to other models, including PSOGW-ANN, SMA-ANN, AOA-ANN, Random Forest, and ANN. Where the learning rate and the number of nodes in the first and second hidden layers were: (0.0023, 1, 20) for the GCAOA-ANN model. In addition, the evaluation metrics of the GCAOA-ANN model showed excellent results, with an R2 value of (99.80%), an RMSE of (0.0000135 m3/s), an MSE of (0.0000118 m3/s), an SI of (0.000077), an MBE of (0.0000028), and a MAR of (0.000187616 m3/s). Conclusion:The outcomes of this research are anticipated to offer a guide for individuals interested in conducting research on On-Chip communication routing protocols and DSE in NoC-based MPSoCs in the context of IoT using ML approaches, while the GCAOA-ANN has the potential to generalize to new problems.

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Section: Why Use Annmentioning

confidence: 99%

“…The model structure of ANNs is inspired by the neural network architecture of the human mind [20], [21]. ANNs are an advanced computing tool for modeling nonlinear systems [22]. Mahto et al [35], Phat…”

Section: Ann Studiesmentioning

confidence: 99%

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

et al. 2023

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“…From the numerical optimization report, one solution that best meet the target criteria is chosen as the optimal solution. This is most times the solution that have the highest level of desirability, indicating there is an island of acceptable performances [31,32]. The optimal solution could be presented graphically using contour and overlay plots.…”

Section: Mixture Design Numerical Optimizationmentioning

confidence: 99%

Mechanical Characterization and Numerical Optimization of Aluminum Matrix Hybrid Composite

Adediran

Edoziuno

Adesina

et al. 2022

MSF

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Hybridization of aluminium matrix composite is with a view to offset the properties deficient in one composite reinforcement. The present investigation involves a comparative study of AA6063 matrix composites with single reinforcement of Al2O3, SiC, graphene respectively and various hybridized proportions of the same reinforcements. Physical (density and %porosity) and mechanical (tensile strength, fracture toughness, %elongation, elastic modulus, etc.) properties of composites developed via solidification processing technique were evaluated. The porosity of all the composites falls below the maximum acceptable limit for cast metal matrix composite. Maximum values for UTS, %elongation and absorbed energy at maximum stress was obtained by hybrid composite with 4wt% Al2O3, SiC and 2wt% graphene, while the composite with the highest single reinforcement of graphene have the highest value for elastic modulus and fracture toughness. Numerical optimization results show that a matrix and hybrid reinforcements contents of AA6063 (91.413wt.%), SiC (3.679wt.%), Al2O3 (0.277wt.%), and graphene (4.632wt.%) respectively, will result in optimal values for the evaluated properties.

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“…Hence, extensive researches are currently geared towards this direction with AI leading the charge [4,5,6,7]. There are several AI tools used in science and engineering which include but not limited to Artificial Neural Networks (ANN), Adpative Neuro-Fuzzy Inference System (ANFIS), Fuzzy Logic (FL), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc [8,9,10,11,12,13]. In this present application, Fuzzy Logic, Non Dominated Sorting Genetic Algorithm (NSGA II) and Particle Swarm Optimization were used for prediction and multi objective optimization of properties of a newly developed hybrid composite for golf club application.…”

Section: Introductionmentioning

confidence: 99%

“…Their findings showed that ANFIS was less effective than ANN as a fitness function for the NSGA II optimization algorithm. Nwobi-Okoye et al [9] used NSGA II algorithm combined with ANN as its fitness function to carry out multi objective optimization of dry compressive strength of groundnut shell ash particles and ant hill bonded foundry sand. The optimization objectives were maximization of compressive strength and minimization of cost of mould production.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Properties of Sponge Gourd-Bagasse Hybrid Reinforced Polymer Composite Using Fuzzy Logic, PSO, Modified Desirability Function and NSGA II

Chibueze

Obele

Nwobi-Okoye

et al. 2021

Preprint

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Development of mathematical models for prediction of properties of materials is often complex and cumbersome. This led to the advent of simpler, and often more accurate, computational models based on artificial intelligence for predicting materials properties. The aim of this study is to predict the mechanical properties of a newly developed hybrid composite material made with sponge gourd, baggase and epoxy resin for golf club application using fuzzy logic (FL) and carry out a multi-objective optimization of the properties with modified desirability function (DF) and NSGA II algorithm. The inputs were %Wt of baggase, %Wt of Sponge gourd and Fiber size (µm) while the response variables were tensile strength, hardness, flexural strength, modulus, elongation and impact strength. The FL model was separately coupled, as fitness function, with the modified DF algorithm and the NSGA II algorithm respectively. The DF was optimized with particle swarm optimization (PSO) algorithm. The results showed that the FL model predicted the mechanical properties accurately and the minimum correlation coefficient (R) between the experimental responses and FL predictions was 0.9529. The modified algorithms took care of certain peculiarities in the desirability properties such as elongation whose desirability is constant over a range. The optimized properties were found to be worse if the optimization algorithms were not modified.

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Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm

Cited by 8 publications

References 15 publications

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

Mechanical Characterization and Numerical Optimization of Aluminum Matrix Hybrid Composite

Multi-Objective Optimization of Properties of Sponge Gourd-Bagasse Hybrid Reinforced Polymer Composite Using Fuzzy Logic, PSO, Modified Desirability Function and NSGA II

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