Natural Frequency prediction of FDM manufactured parts using ANN approach

Ali, Fahraz; Chowdary, Boppana V.

doi:10.1016/j.ifacol.2019.11.083

Cited by 16 publications

(14 citation statements)

References 14 publications

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“…PLA presents a relatively brittle behavior under tensile loading [ 13 ]. Although the dimensional accuracy and mechanical properties of PLA have been already studied, there is little literature on the printed PLA’s toughness with a honeycomb internal fill pattern [ 14 , 15 , 16 , 17 , 18 ]. Hence, the effects of extruder temperature (ET), infill percentage (IP), and layer thickness (LT) and their interactions on toughness, thickness, and production cost of the 3D printed specimens in PLA were investigated by response surface methodology (RSM).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

et al. 2021

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Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

show abstract

Section: Introductionmentioning

confidence: 99%

“…According to the literature, the ML techniques can be effective tools for modeling the FFF process [ 14 , 15 , 16 , 17 ]. In addition, hybrid ML techniques provided higher accuracy and performance compared with single ML techniques [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

et al. 2021

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“…The maximum tensile strength was estimated to be 0.0405 kN/mm2 for PETG in the presence of 0.1 mm layer height, material density 1.27 g/cm3 and extrusion temperature 225 °C. Ali and Chowdary [15] employed ANN for the prediction the mechanical characteristics of FFF printed parts in the presence of air gap, raster angle, number of contours and build orientation as input variables. ANN was trained using a Bayesian function.…”

Section: Introductionmentioning

confidence: 99%

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

Moradi¹,

Meiabadi²,

Moghadam³

et al. 2020

Preprint

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Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for toughness, part thickness, and production cost dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could could successfully improveenhances the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

show abstract

“…PLA presents a relatively brittle behavior under tensile loading [13]. Although the dimensional accuracy and mechanical properties of PLA have been already studied, there is little literature on the printed PLA's toughness with honeycomb internal fill pattern [14][15][16][17][18]. Hence, effects of extruder temperature (ET), infill percentage (IP), and layer thickness (LT) and their interactions on toughness, thickness, and production cost of the 3D printed specimens in PLA are investigated by Response Surface Methodology (RSM).…”

Section: Introductionmentioning

confidence: 99%

“…According to the literature, the ML techniques can be effective tools for modeling the FFF process [14][15][16][17]. In addition, hybrid ML techniques provided higher accuracy and performance compared with single ML techniques [15]. This made us to move toward comparing ANN as the frequently used and simple ML technique with ANN-GA as the hybrid ML technique.…”

Section: Introductionmentioning

confidence: 99%

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Deposition Modelling and Machine Learning

Moradi¹,

Meiabadi²,

Moghadam³

et al. 2020

Preprint

View full text Add to dashboard Cite

Polylactic acid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the producibility of fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDM-printed tensile test samples with the desired part thickness. To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering layer thickness, infill percentage, and extruder temperature as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The honeycomb internal fill pattern is applied for the production of light-weight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using ANN and ANN-GA techniques for developing an accurate estimation for toughness, part thickness, and production cost as dependant variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid ML technique could successfully improve the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. In the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which develops the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing fused deposition modeling setup without changing the functional hardware/software of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

show abstract

Natural Frequency prediction of FDM manufactured parts using ANN approach

Cited by 16 publications

References 14 publications

Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Deposition Modelling and Machine Learning

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