Ensuring supply chain integrity for material extrusion 3D printed polymer parts

Kipling, Isabelle; Gibbons, Gregory John

doi:10.1016/j.addma.2023.103403

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…New additive processes in polymer printing, such as fused deposition modelling, eliminate the requirement for additional tooling stages because either only one material is added layer by layer or composite materials are developed in different ways, such as by adding reinforcements to the matrix. To improve the mechanical behavior of 3D-printed components, printing parameters, such as the velocity, bed temperature, extrusion temperature, and raster direction, have been examined because they can generate internal defects that can result in premature failure under quasistatic or dynamic loads [1][2][3]. Travieso-Rodriguez et al [4] revealed the relationship between printing parameters and quasistatic response in terms of the stiffness and bending strength, but they did not define the parameters necessary to result in an improvement in fatigue strength.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Fatigue Life of Polylactic Acid Components through Post-Printing Heat Treatment

Jimenez-Martinez,

Varela-Soriano,

Carrera-Espinoza

et al. 2024

Designs

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To reduce the carbon footprint of manufacturing processes, it is necessary to reduce the number of stages in the development process. To this end, integrating additive manufacturing processes with three-dimensional (3D) printing makes it possible to eliminate the need to use tooling for component manufacturing. Furthermore, using 3D printing allows the generation of complex models to optimize different components, reducing the development time and realizing lightweight structures that can be applied in different industries, such as the mobility industry. Printing process parameters have been studied to improve the mechanical properties of printed items. In this regard, although the failure of most structural components occurs under dynamic load, the majority of the evaluations are quasistatic. This work highlights an improvement in fatigue strength under dynamic loads in 3D-printed components through heat treatment. The fatigue resistance was improved regarding the number of cycles and the dispersion of results. This allows 3D-printed polylactic acid components to be structurally used, and increasing their reliability allows their evolution from a prototype to a functional component.

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Section: Introductionmentioning

confidence: 99%

Enhancement of Fatigue Life of Polylactic Acid Components through Post-Printing Heat Treatment

Jimenez-Martinez,

Varela-Soriano,

Carrera-Espinoza

et al. 2024

Designs

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“…Additive manufacturing (AM), also known as rapid prototyping, 3D printing, and freeform fabrication, is an advanced manufacturing method founded upon 3D model data. This method constructs intricate structures or parts by depositing materials layer-bylayer [1,2]. In contrast to traditional manufacturing technologies, AM exhibits significant advantages, including its capacity to handle intricate structures, low waste generation, and heightened production efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning

Shi,

Sun,

Tian

et al. 2023

Micromachines

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The dimensions of material extrusion 3D printing filaments play a pivotal role in determining processing resolution and efficiency and are influenced by processing parameters. This study focuses on four key process parameters, namely, nozzle diameter, nondimensional nozzle height, extrusion pressure, and printing speed. The design of experiment was carried out to determine the impact of various factors and interaction effects on filament width and height through variance analysis. Five machine learning models (support vector regression, backpropagation neural network, decision tree, random forest, and K-nearest neighbor) were built to predict the geometric dimension of filaments. The models exhibited good predictive performance. The coefficients of determination of the backpropagation neural network model for predicting line width and line height were 0.9025 and 0.9604, respectively. The effect of various process parameters on the geometric morphology based on the established prediction model was also studied. The order of influence on line width and height, ranked from highest to lowest, was as follows: nozzle diameter, printing speed, extrusion pressure, and nondimensional nozzle height. Different nondimensional nozzle height settings may cause the extruded material to be stretched or squeezed. The material being in a stretched state leads to a thin filament, and the regularity of processing parameters on the geometric size is not strong. Meanwhile, the nozzle diameter exhibits a significant impact on dimensions when the material is in a squeezing state. Thus, this study can be used to predict the size of printing filament structures, guide the selection of printing parameters, and determine the size of 3D printing layers.

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Ensuring supply chain integrity for material extrusion 3D printed polymer parts

Cited by 2 publications

References 23 publications

Enhancement of Fatigue Life of Polylactic Acid Components through Post-Printing Heat Treatment

Enhancement of Fatigue Life of Polylactic Acid Components through Post-Printing Heat Treatment

Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning

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