Wear assessment of 3–D printed parts of PLA (polylactic acid) using Taguchi design and Artificial Neural Network (ANN) technique

Pant, Meena; Singari, Ranganath M.; Arora, Pawan Kumar; Moona, Girija; Kumar, Harish

doi:10.1088/2053-1591/abc8bd

Cited by 36 publications

(14 citation statements)

References 29 publications

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“…With respect to the friction and wear performance of PLA samples printed by FDM, the authors of previous research pointed out the fact that both parameters, the color of the PLA filament [ 35 ] and the extrusion temperature [ 36 , 37 ], influence these characteristics. In relation to the friction performance, the surface finishing of the parts has to be considered and therefore the influence of the filament color [ 29 , 34 ] and the extrusion temperature [ 22 , 34 ] on the sample’s roughness.…”

Section: Introductionmentioning

confidence: 99%

The Influence of the Printing Temperature and the Filament Color on the Dimensional Accuracy, Tensile Strength, and Friction Performance of FFF-Printed PLA Specimens

et al. 2022

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The printing variable least addressed in previous research aiming to reveal the effect of the FFF process parameters on the printed PLA part’s quality and properties is the filament color. Moreover, the color of the PLA, as well as its manufacturer, are rarely mentioned when the experimental conditions for the printing of the samples are described, although current existing data reveal that their influence on the final characteristics of the print should not be neglected. In order to point out the importance of this influential parameter, a natural and a black-colored PLA filament, produced by the same manufacturer, were selected. The dimensional accuracy, tensile strength, and friction properties of the samples were analyzed and compared for printing temperatures ranging from 200 °C up to 240 °C. The experimental results clearly showed different characteristics depending on the polymer color of samples printed under the same conditions. Therefore, the optimization of the FFF process parameters for the 3D-printing of PLA should always start with the proper selection of the type of the PLA material, regarding both its color and the fabricant.

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Section: Introductionmentioning

confidence: 99%

The Influence of the Printing Temperature and the Filament Color on the Dimensional Accuracy, Tensile Strength, and Friction Performance of FFF-Printed PLA Specimens

et al. 2022

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“…Currently, most researchers are comparatively showing their interest in the development of the artificial intelligence-based prediction and optimization models as an alternate to the traditional/conventional approaches for checking the influence of the FDM printing parameters on the performance and quality of parts. [24,[45][46][47][48][49][50][51][52][53] In a study, [29] the influence of process parameters, that is, cutting speed, feed and depth of cut was investigated on tool wear and supervised feed-forward ANN prediction model was developed to estimate the tool wear in turning process. [54] Gupta et al [55] developed the deep learning neural network model to study the wire electric discharge machining (WEDM) process parameters.…”

Section: Literature Summarymentioning

confidence: 99%

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

et al. 2022

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Fused deposition modeling (FDM) is one of the most economical and popular technology amongst numerous additive manufacturing techniques. The quality of FDM fabricated parts is highly sensitive to the production parameters. Thus, in the present work, an investigation on the FDM printed polylactic acid parts has been performed considering six printing process parameters, that is, nozzle diameter, build orientation, raster pattern, layer height and print speed to develop the feedforward backpropagation (FFBP) artificial neural network prediction model for the prediction of responses, namely, tensile strength, material consumption, build time and surface quality. Tensile specimens as per L 27 orthogonal array are printed considering the various combination of parameters. The printed samples have been subjected to tensile strength testing, surface roughness measurement, build time recording, and material consumption evaluation. The highest tensile strength of 57.633 MPa, lowest surface roughness of 1.71 μm, lowest build time of 0.35 h and lowest material consumption of 7.8 g are observed. The experimental results have been used to develop the artificial intelligence-based prediction model through FFBP algorithm and sigmoid transfer function to predict the responses. The best performance of the developed neural network with R 2 for testing (0.99343), training (0.99366), and validation (0.99372) of data is recorded for prediction of responses with minimum percentage error. The study concluded that developed model is capable of predicting the responses of FDM process according to the input process parameters.

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“…Pant et al [ 120 ] optimized the process parameters of fused deposition modeling (FDM) by exploring the wear performance of PLA. A variation of process parameters, such as layer thickness, orientation, and extruder temperature, was investigated.…”

Section: Applications Of Ann In 3d Printingmentioning

confidence: 99%

Artificial Neural Network Algorithms for 3D Printing

et al. 2020

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Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

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Wear assessment of 3–D printed parts of PLA (polylactic acid) using Taguchi design and Artificial Neural Network (ANN) technique

Cited by 36 publications

References 29 publications

The Influence of the Printing Temperature and the Filament Color on the Dimensional Accuracy, Tensile Strength, and Friction Performance of FFF-Printed PLA Specimens

The Influence of the Printing Temperature and the Filament Color on the Dimensional Accuracy, Tensile Strength, and Friction Performance of FFF-Printed PLA Specimens

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

Artificial Neural Network Algorithms for 3D Printing

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