Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm

Ülkir, Osman; Akgün, Gazi

doi:10.1080/13621718.2023.2200572

Cited by 13 publications

(11 citation statements)

References 29 publications

(36 reference statements)

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“…The special issue contains contributions on the major variants of AM including laser DED [6,9,18,24], gas metal arc DED [7,[10][11][12][13][14]16,19,[21][22][23], laser PBF [8], cold spray [17], laser-arc hybrid manufacturing [20], and fused deposition modelling [15]. Chaurasia et al [22] presented a novel approach to real-time monitoring of melt pool cross-sectional asymmetry during laser PBF using a two-color thermal camera.…”

Section: Processmentioning

confidence: 99%

“…Machine learning models provided 99% accuracy in the prediction of transient temperature distribution. Ulkir et al [15] used neural network-based machine learning to determine the optimal combination of process parameters for reducing the surface roughness of parts made by fused deposition modelling. The accuracy in predicting surface roughness using machine learning was more accurate compared to the random experimental trials.…”

Section: Articles In This Special Issuementioning

confidence: 99%

“…This special issue includes all aforementioned trends of progress through 19 original research articles [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The editors of STWJ invited active researchers from the AM and welding communities to contribute to this special issue.…”

Section: Introductionmentioning

confidence: 99%

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Recent progress in process, structure, properties, and performance in additive manufacturing

Mukherjee

2023

Science and Technology of Welding and Joining

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The similarities between additive manufacturing (AM) and fusion welding have made Science and Technology of Welding and Joining an appropriate venue to discuss the recent progress made in AM. This special issue highlights significant recent progress in AM towards inventing new processes, developing novel materials, improving microstructure, achieving unique mechanical properties, and reducing defects. Furthermore, it identifies the modern tools of the digital age such as mechanistic modelling, machine learning, and digital twin as powerful drivers of this progress. The articles in this special issue are also believed to identify research needs and opportunities for the AM community.

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Section: Processmentioning

confidence: 99%

Section: Articles In This Special Issuementioning

confidence: 99%

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Recent progress in process, structure, properties, and performance in additive manufacturing

Mukherjee

2023

Science and Technology of Welding and Joining

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“…This feature may be due to factors, such as surface roughness, dimensional accuracy, energy consumption, and tensile strength. [27][28][29][30] In this study, four printing parameters (infill pattern, wall thickness, infill density, and layer thickness) that have a high impact on dimensional accuracy were selected because of the studies examined. These parameters have not been examined in any study in terms of the dimensional accuracy of additively manufactured parts.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network to evaluation of dimensional accuracy of 3D‐printed polylactic acid parts

Gunes,

Ulkir,

Kuncan

2024

Journal of Polymer Science

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Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 μm. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R‐square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method.

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“…Then, the relationship between input parameters and surface roughness was established using the regression model. Ulkir and Gazi aimed to determine the optimal combination of input parameters to predict and minimize the surface roughness of samples fabricated by FDM on a 3D printer using a cascaded forward neural network and a genetic algorithm (Ulkir and Akgun, 2023). The Box-Behnken design with four independent printing parameters at three levels was used, and 25 parts were fabricated with a 3D printer.…”

Section: Introductionmentioning

confidence: 99%

Optimizing surface roughness in soft pneumatic gripper fabricated via FDM: experimental investigation using Taguchi method

Uludag,

Ulkir

2024

MMMS

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Purpose In this study, experimental studies were carried out using different process parameters of the soft pneumatic gripper (SPG) fabricated by the fused deposition modeling method. In the experimental studies, the surface quality of the gripper was examined by determining four different levels and factors. The experiment was designed to estimate the surface roughness of the SPG.Design/methodology/approach The methodology consists of an experimental phase in which the SPG is fabricated and the surface roughness is measured. Thermoplastic polyurethane (TPU) flex filament material was used in the fabrication of SPG. The control factors used in the Taguchi L16 vertical array experimental design and their level values were determined. Analysis of variance (ANOVA) was performed to observe the effect of printing parameters on the surface quality. Finally, regression analysis was applied to mathematically model the surface roughness values obtained from the experimental measurements.Findings Based on the Taguchi signal-to-noise ratio and ANOVA, layer height is the most influential parameter for surface roughness. The best surface quality value was obtained with a surface roughness value of 18.752 µm using the combination of 100 µm layer height, 2 mm wall thickness, 200 °C nozzle temperature and 120 mm/s printing speed. The developed model predicted the surface roughness of SPG with 95% confidence intervals.Originality/value It is essential to examine the surface quality of parts fabricated in additive manufacturing using different variables. In the literature, surface roughness has been examined using different factors and levels. However, the surface roughness of a soft gripper fabricated with TPU material has not been examined previously. The surface quality of parts fabricated using flexible materials is very important.

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Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm

Cited by 13 publications

References 29 publications

Recent progress in process, structure, properties, and performance in additive manufacturing

Recent progress in process, structure, properties, and performance in additive manufacturing

Application of artificial neural network to evaluation of dimensional accuracy of 3D‐printed polylactic acid parts

Optimizing surface roughness in soft pneumatic gripper fabricated via FDM: experimental investigation using Taguchi method

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