Decision Tree Methods for Predicting Surface Roughness in Fused Deposition Modeling Parts

Barrios, Juan M.; Romero, Pablo E.

doi:10.3390/ma12162574

Cited by 58 publications

(35 citation statements)

References 26 publications

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“…While print acceleration seems not to have an influence in any of the cases studied, the flow is revealed as an important parameter in the surface roughness obtained in XY orientation. This result is consistent with those obtained by the authors in similar tests performed on PETG [47].…”

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confidence: 94%

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Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing

et al. 2020

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In the present work, ten data mining algorithms have been used to generate models capable of predicting the surface roughness of parts printed on polylactic acid (PLA) by using fused deposition modeling (FDM). The models have been trained using experimental data measured on 27 horizontal (XY) and 27 vertical (XZ) specimens, printed using different values for the parameters studied (layer height, extrusion temperature, print speed, print acceleration and flow). The models generated by multilayer perceptron (MLP) and logistic model trees (LMT) have obtained the best results in a cross-validation. Although it does not obtain such optimal results, the J48 algorithm (C4.5) allows the generation of models in the form of a decision tree. These trees permit to determine which print parameters have an influence on the surface roughness. For XY specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XY ) depends on the flow, the print temperature and the layer height; in the direction perpendicular to the extrusion path, the surface roughness (Ra,90,XY) depends only on the flow. For XZ specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XZ) depends only on the print speed; in the direction perpendicular to the extrusion path (Ra,90,XZ), it depends on the layer height and the extrusion temperature. According to the study carried out, the most suitable set up provides values of Ra,0,XY, Ra,90,XY, Ra,0,XZ and Ra,90,XZ equal to 0.46, 1.18, 0.45 and 11.54, respectively. A practical application of this work is the manufacture of PLA frame glasses using FDM.

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confidence: 94%

“…• The decision tree models can be easily interpreted by any 3D printer operator (they are not black-box models) [47]. For this reason, they are used for modeling other manufacturing processes in literature [48].…”

Section: Discussionmentioning

confidence: 99%

Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing

et al. 2020

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“…The research plan to determine the impact of three independent factors, namely, feed (

), depth of cut (

(mm)), and cutting speed (

(m/min)), on the values of the selected factors

and

(i.e., 3D areal surface texture parameters) of the surface roughness was developed according to the Taguchi experiment design guidelines (DOE) [ 6 ]. The factors

(i.e., the arithmetical mean height of the surface) and

(i.e., the maximum height of the surface—sum of the maximum peak height value and the maximum pit height value within a definition area) were defined in accordance with the standard ISO25178 [ 62 ].…”

Section: General Assumptionsmentioning

confidence: 99%

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Kowalczyk

Tomczyk

2020

Materials

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The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

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“…PET-G can be employed for both indoor and outdoor applications, and it is particularly suitable for manufacturing mechanical parts. 6 PET-G has a very low thermal expansion, which makes it an ideal material for additive manufacturing of large models. 7 Despite its relatively low flammability (rated one according to the hazardous materials identification system 8 ), in case of fire, PET-G releases a mass of harmful toxic gases, soot, and smoke.…”

Section: Introductionmentioning

confidence: 99%

The effect of 3D structure design on fire behavior of polyethylene terephthalate glycol containing aluminum hypophosphite and melamine cyanurate

Zárybnická

Mácová

Machová

et al. 2020

J of Applied Polymer Sci

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Effect of the shape of 3D printed samples on fire behavior of polyethylene terephthalate glycol (PET‐G) and PET‐G additivated with a mix of aluminum hypophosphite (AHP) and melamine cyanurate as flame retardant, was investigated. The additives improved fire performance (e.g., maximum average rate of heat emission, total oxygen consumption, heat release rate indices) irrespective of structural complexity, favoring carbonaceous char formation. However, at increasing structural complexity, they promoted higher release of smoke, compared to neat PET‐G, because of a change in the prevalent retardation mechanism, which became dominated by the flame inhibition action of AHP. Consequently, the synergistic effect obtained combining the two additives, was hindered. Impact of product design on mechanisms of fire retardation helps in devising engineering solutions aimed at meeting required level of fire‐safety performance, which should be tailored to the specific product.

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Decision Tree Methods for Predicting Surface Roughness in Fused Deposition Modeling Parts

Cited by 58 publications

References 26 publications

Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing

Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

The effect of 3D structure design on fire behavior of polyethylene terephthalate glycol containing aluminum hypophosphite and melamine cyanurate

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