Correlation Between Process Parameters and Mechanical Properties in Parts Printed by the Fused Deposition Modeling Process

Attoye, Samuel; Malekipour, Ehsan; El-Mounayri, Hazim

doi:10.1007/978-3-319-95083-9_8

Cited by 26 publications

(18 citation statements)

References 6 publications

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“…The resulting quality of layer-to-layer adhesion and mechanical properties of printed tensile specimens proves the high effectiveness of the proposed temperature control systemin comparison to the other optimization methods/techniques or tools listed in [ 2 ]. Results, obtained using such methods/techniques, are shown in [ 53 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ] for specimens printed of ABS and [ 47 , 50 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 ] for the PLA ones.…”

Section: Resultsmentioning

confidence: 99%

Indirect Temperature Measurement in High Frequency Heating Systems

Oskolkov

Bezukladnikov

Трушников

2021

Sensors

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One of the biggest challenges of fused deposition modeling (FDM)/fused filament fabrication (FFF) 3D-printing is maintaining consistent quality of layer-to-layer adhesion, and on the larger scale, homogeneity of material inside the whole printed object. An approach for mitigating and/or resolving those problems, based on the rapid and reliable control of the extruded material temperature during the printing process, was proposed. High frequency induction heating of the nozzle with a minimum mass (<1 g) was used. To ensure the required dynamic characteristics of heating and cooling processes in a high power (peak power > 300 W) heating system, an indirect (eddy current) temperature measurement method was proposed. It is based on dynamic analysis over various temperature-dependent parameters directly in the process of heating. To ensure better temperature measurement accuracy, a series-parallel resonant circuit containing an induction heating coil, an approach of desired signal detection, algorithms for digital signal processing and a regression model that determines the dependence of the desired signal on temperature and magnetic field strength were proposed. The testbed system designed to confirm the results of the conducted research showed the effectiveness of the proposed indirect measurement method. With an accuracy of ±3 °C, the measurement time is 20 ms in the operating temperature range from 50 to 350 °C. The designed temperature control system based on an indirect measurement method will provide high mechanical properties and consistent quality of printed objects.

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Section: Resultsmentioning

confidence: 99%

Indirect Temperature Measurement in High Frequency Heating Systems

Oskolkov

Bezukladnikov

Трушников

2021

Sensors

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“…To fulfill such objective, real-time monitoring, and control of VED is the first step. This is because thermal specifications and the evolution of any inherently thermal AM process such as PBF have been found to be the predominant contributing factors affecting the microstructure and ultimate mechanical properties of manufactured parts [47,48].…”

Section: Discussionmentioning

confidence: 99%

A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process

Marrey¹,

Malekipour²,

El-Mounayri³

et al. 2021

Preprint

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Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

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“…A preliminary study on how the specific process' set-up affects the mechanical behaviour of the components is necessary for choosing the materials and building parameters. 15–18…”

Section: Materials and Fabrication Methodsmentioning

confidence: 99%

Development of a gripper for garment handling designed for additive manufacturing

Jilich

Frascio

Avalle

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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The paper presents how a robotic gripper specific for grasping and handling of textiles and soft flexible layers can be miniaturized and improved by polymeric additive manufacturing-oriented re-design. Advantages of polymeric additive manufacturing are to allow a re-design of components with integrated functions, to be cost-effective equipment for small batches production and the availability of suitable materials for many applications. The drawback is that for design validation extended testing is still necessary because of lacks in standardization and that the mechanical properties are building parameters dependent. The outcomes are a lower complexity of the design overall and lower number of components. These are pursued taking advantage of the anisotropy of the additive manufacturing processed polymer and assigning appropriate shapes and linkages in the mechanisms. Set of common materials (polylactide, polyethylene terephthalate, acrylonitrile butadiene styrene) and technical (acrylonitrile styrene acrylate, polycarbonate/polybutylene terephthalate blend) are tested to obtain data for the modelling.

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Correlation Between Process Parameters and Mechanical Properties in Parts Printed by the Fused Deposition Modeling Process

Cited by 26 publications

References 6 publications

Indirect Temperature Measurement in High Frequency Heating Systems

Indirect Temperature Measurement in High Frequency Heating Systems

A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process

Development of a gripper for garment handling designed for additive manufacturing

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