Development of Optimized ANN Model Through Genetic Algorithm to Predict the Microstructural Parameters of Aluminum Alloy Castings

Rao, D. Hanumantha; Tagore, G.R.N.; Janardhana, G. Ranga; Kun, Kim S.

doi:10.26634/jfet.3.2.678

Cited by 3 publications

(15 citation statements)

References 4 publications

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“…A strong correlation is always inferred for R 2 value ≥ 0.7 [30].Correlation coe cient of 0.92 and mean average percentage error (MAV.PE) of 0.36 were obtained (Low average percentage error (close to zero) is desired because it indicates insigni cant error between the experimental and predicted data). From the present comparison very strong correlation between the two data sets (output and target) can be inferred.…”

Section: Characteristics Of the Treated Water After Coagulationmentioning

confidence: 83%

“…The nal residual masses for GSF and GSC estimated based on weight loss with respect to temperature are 5.72218 mg and 1.974 mg, representing 89.2% and 74.9% of the original weights of GSF (6.415mg) and GSC (2.634mg) sample, respectively. The initial weight loss observed in Figure 8 (c and d) could be linked to internal moisture content and gaseous loss from the matrix molecules [30]. The second phase weight loss may be as a result of decomposition in the samples.…”

Section: Dsc /Tga Analysis For Gsf and Gscmentioning

confidence: 90%

“…The predicted responses were compared with the experimental result and optimized using genetic algorithm. [30]. Figure 3, shows the ANN neural architecture for the inputhidden-output interaction.…”

Section: 3mentioning

confidence: 99%

“…This metaheuristic AI approach provides an approximate optimal by evaluating sets of chromosomes created via GA operational environment. The target of the response optimization (set at 99.999%) is subject to the total weight such that the optimal response is as higher as possible and not exceeding the maximum weight [16,30] which is 100% for the present study. The weight of the data set was scaled into a continuous interval using fuzzy C-means.…”

Section: Characteristics Of the Treated Water After Coagulationmentioning

confidence: 99%

“…The potentials of conchiolin as coagulant have been accessed in treatment of paint wastewater. Also, due to the robustness of response surface design (RSD), and the proven optimization e ciency of arti cial intelligence (genetic algorithm) [30], hybrid method (the use of RSD and ANN-GA) was explored for experimental design and optimization of the process response for reduction of colloidal particles in paint wastewater.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Conditions of Paint Wastewater Coagulation With Gastropod Shell Conchiolin Using Response Surface Design and Artificial Neural Network-Genetic Algorithm

Ejimofor

Ezemagu

Menkiti

et al. 2022

Preprint

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The potential of gastropod shell conchiolin (GSC) (a waste product of the deprotenization stage of chitosan production) as one of the alternatives to chemical coagulants has been explored for treatment of paint industrial wastewater (PW). The accuracy of response surface design (RSD) and the precision of artificial intelligence (AI) in predicting and optimizing the process conditions were harnessed in raising experimental design matrix and response optimization, respectively for the bench scale jar test coagulation experiment. PW was characterized using American public health association (APHA) standard methods. Extraction of conchiolin was done via alkaline extraction method. PW contains 2098mg/l total suspended solid (TSS) above discharge limit (1905mg/l). Fourier transform infrared (FTIR) spectrum of GSC revealed a broad N–H wagging band at 750 – 650 cm−1 indicating the presence of secondary amine linked to the presence of protein. Turbidity removal from PW via one factor at a time (OFAT) was found to be a function of pH, GSC dosage, temperature and time. Artificial neural network (ANN) response prediction shows 92% correlation with the response surface design (RSD) experimental result. The optimal conditions obtained via genetic algorithm (GA) for the response optimization at the best pH of 4 indicate optimal turbidity removal of 98% at GSC dosage, time and temperature of 4 g, 20 min and 45oC, respectively.

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Section: Characteristics Of the Treated Water After Coagulationmentioning

confidence: 83%

Section: Dsc /Tga Analysis For Gsf and Gscmentioning

confidence: 90%

Section: 3mentioning

confidence: 99%

Section: Characteristics Of the Treated Water After Coagulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Conditions of Paint Wastewater Coagulation With Gastropod Shell Conchiolin Using Response Surface Design and Artificial Neural Network-Genetic Algorithm

Ejimofor

Ezemagu

Menkiti

et al. 2022

Preprint

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Optimal conditions of paint wastewater coagulation with gastropod shell conchiolin using response surface design and artificial neural network-genetic algorithm

et al. 2022

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An analysis to enhance the machining performance of micro-EDM for drilling of blind micro-hole using ANN

Pratap¹,

Upadhyay²,

Sharma³

et al. 2023

Preprint

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The high demand for compact and multitasking devices in the market has been a driving force behind the growing interest in microfabrication techniques. These techniques have wide-ranging applications in many industries, including aerospace, automobile, electronics, and defense. Micro electrical discharge machining (µEDM) techniques have the unique ability to produce highly precise and intricate features on small components, which has further fueled the demand for such products. However, with the increasing demand for micro-featured products, there is a pressing need to enhance the process capability of µEDM process. This work aims to address this need by focusing on enhancing the performance of µEDM by varied process parameters and materials such as copper, brass, and tungsten carbide for the drilling of blind micro holes. Surface roughness (SR) and material removal rate (MRR) are the main performance factors taken into account in this investigation. Notably, the minimum SR was achieved on tungsten carbide, while the maximum MRR was achieved using copper electrodes. For SR and MRR, artificial neural network (ANN) models have been constructed that predict with more than 90% accuracy. These findings have significant implications for the future of microfabrication using µEDM.

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Development of Optimized ANN Model Through Genetic Algorithm to Predict the Microstructural Parameters of Aluminum Alloy Castings

Cited by 3 publications

References 4 publications

Optimal Conditions of Paint Wastewater Coagulation With Gastropod Shell Conchiolin Using Response Surface Design and Artificial Neural Network-Genetic Algorithm

Optimal Conditions of Paint Wastewater Coagulation With Gastropod Shell Conchiolin Using Response Surface Design and Artificial Neural Network-Genetic Algorithm

Optimal conditions of paint wastewater coagulation with gastropod shell conchiolin using response surface design and artificial neural network-genetic algorithm

An analysis to enhance the machining performance of micro-EDM for drilling of blind micro-hole using ANN

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