Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement

Deshwal, Sandeep; Kumar, Ashwani; Chhabra, Deepak

doi:10.1016/j.cirpj.2020.05.009

Cited by 117 publications

(39 citation statements)

References 32 publications

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“…Some other studies, such as that of Sai et al [ 26 ], employed a central composite design and an ANFIS to analyze the influence of process parameters in FDM of PLA implants. Moreover, hybrid optimization techniques based on genetic algorithm-adaptive neuro fuzzy interface system (GA-ANFIS) have also been used in order to optimize the FDM process parameters, as can be observed in Deshwal et al [ 27 ]. Another study developed by Rajpurohit et al [ 28 ] employed an ANFIS for prediction of tensile strength in FDM parts.…”

Section: Introductionmentioning

confidence: 99%

Analysis of AM Parameters on Surface Roughness Obtained in PLA Parts Printed with FFF Technology

2021

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Fused filament fabrication (FFF) 3D printing technology allows very complex parts to be obtained at a relatively low cost and in reduced manufacturing times. In the present work, the effect of main 3D printing parameters on roughness obtained in curved surfaces is addressed. Polylactic acid (PLA) hemispherical cups were printed with a shape similar to that of the acetabular part of the hip prostheses. Different experiments were performed according to a factorial design of experiments, with nozzle diameter, temperature, layer height, print speed and extrusion multiplier as variables. Different roughness parameters were measured—Ra, Rz, Rku, Rsk—both on the outer surface and on the inner surface of the parts. Arithmetical mean roughness value Ra and greatest height of the roughness profile Rz are usually employed to compare the surface finish among different manufacturing processes. However, they do not provide information about the shape of the roughness profile. For this purpose, in the present work kurtosis Rku and skewness Rsk were used. If the height distribution in a roughness profile follows a normal law, the Rku parameter will take a value of 3. If the profile distribution is symmetrical, the Rsk parameter will take a value of 0. Adaptive neural fuzzy inference system (ANFIS) models were obtained for each response. Such models are often employed to model different manufacturing processes, but their use has not yet been extended to 3D printing processes. All roughness parameters studied depended mainly on layer height, followed by nozzle diameter. In the present work, as a general trend, Rsk was close to but lower than 0, while Rku was slightly lower than 3. This corresponds to slightly higher valleys than peaks, with a rounded height distribution to some degree.

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Section: Introductionmentioning

confidence: 99%

Analysis of AM Parameters on Surface Roughness Obtained in PLA Parts Printed with FFF Technology

2021

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“…The developed ANFIS model, described in the previous section, is hybridized with the GA optimizer in an attempt improve the fuzzy inference system coefficients of the ANFIS. Hybridized ANFIS-GA models have been applied with some success in various fields (Kumar et al, 2019;Deshwal et al, 2020;Kumar and Hynes, 2020). Linear and non-linear coefficients of the FIS are key model control parameters responsive to optimization.…”

Section: Anfis-ga Modelmentioning

confidence: 99%

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

Yavari

Khosravanian

Wood

et al. 2021

Adv. Geo-Energy Res.

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“…The GA technique considers a sample of the population with individuals defined by their genes and consequently their chromosomes. Chromosomes of these individuals through crossover can mutate until they reach the non-evaluative level with a given generation; this evaluation is based on the fitness, which is considered as the reverse of the cost function or the loss function ( Deshwal et al., 2020 ; Bardeji et al., 2020 ). The performances of ANN trained with gradient descendant and PSO-ANN and GA-ANN will be evaluated on datasets 1 and 2, and a comparative analysis will be focused on these datasets.…”

Section: Ann Pso-ann and Ga-ann: Major Analysis Tools For Complex Systemsmentioning

confidence: 99%

Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning

Mbakop

Biyeme

Voufo

et al. 2021

Heliyon

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Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement

Cited by 117 publications

References 32 publications

Analysis of AM Parameters on Surface Roughness Obtained in PLA Parts Printed with FFF Technology

Analysis of AM Parameters on Surface Roughness Obtained in PLA Parts Printed with FFF Technology

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning

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