scholarly journals Prediction and Experimental Validation of Part Thermal History in the Fused Filament Fabrication Additive Manufacturing Process

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Mriganka Roy ◽  
Reza Yavari ◽  
Chi Zhou ◽  
Olga Wodo ◽  
Prahalada Rao
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Thermal History ◽  
Experimental Validation ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Temperature Profiles ◽  
Shape Distortion ◽  
Fused Filament Fabrication ◽  
Thermal Profile

Abstract Part design and process parameters directly influence the instantaneous spatiotemporal distribution of temperature in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as the thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step toward this goal, the objectives of this work are two-fold. First, to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts. Second, validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different material deposition speeds. In the specific context of FFF, this foray is the critical first-step toward understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high degree of correlation therein, with a mean absolute percentage error less than 6% (root mean squared error <6 °C). This work thus presents one of the first efforts in validating thermal profiles in FFF via direct in situ measurement of the temperature. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

Modeling and Experimental Validation of Part-Level Thermal Profile in Fused Filament Fabrication

2019 ◽  
Author(s):  
Mriganka Roy ◽  
Reza Yavari ◽  
Chi Zhou ◽  
Olga Wodo ◽  
Prahalad Rao
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Feed Rate ◽  
Experimental Validation ◽  
Acrylonitrile Butadiene Styrene ◽  
Temperature Profiles ◽  
Maximum Absolute Error ◽  
Shape Distortion ◽  
Fused Filament Fabrication ◽  
Thermal Profile

Abstract Part design and process parameters directly influence the spatiotemporal distribution of temperature and associated heat transfer in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step towards this goal, the objectives of this work are two-fold: (1) to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts, and (2) validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different feed rate settings (viz., the translation velocity, also called scan speed or deposition speed, of the extruder on the FFF machine). In the specific context of FFF, this foray is the critical first-step towards understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high-degree of correlation therein, with maximum absolute error less than 10%. This work thus presents one of the first efforts in validation of thermal profiles in FFF via in-process sensing. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

Impact of quasi-isotropic raster layup on the mechanical behaviour of fused filament fabrication parts

2021 ◽  
pp. 095400832110419
Author(s):  
Lovin K John ◽  
Ramu Murugan ◽  
Sarat Singamneni
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Mechanical Strength ◽  
Process Parameters ◽  
Mechanical Behaviour ◽  
Fused Filament Fabrication ◽  
Anisotropic Behaviour ◽  
Oriented Samples ◽  
Weak Interlayer ◽  
Special Concern

The development of fused filament fabrication has extended the range of application of additive manufacturing in various areas of research. However, the mechanical strength of the fused filament fabrication–printed parts were considerably lower than that of parts fabricated by other conventional methods, owing to the observed anisotropic behaviour and formation of voids by weak interlayer diffusion. Intense studies on the effect of design and process parameters of the printed parts on the mechanical properties have been done, whereas studies on the effect of build orientations and raster patterns needs special concern. The main aim of this work is to fabricate parts printed using quasi-isotropic laminate arrangement of rasters, achieved by a raster layup of [45/0/−45/90]s, and to compare their mechanical properties with those of the commonly used 0°/90° (cross) and 45°/−45° (crisscross) raster oriented parts. The quasi-isotropic–oriented samples were observed with improved mechanical behaviour in tensile, compressive, flexural and impact tests compared to the commonly employed raster orientations.

A Sensitivity Analysis Study on the Material Properties and Process Parameters for Selective Laser Melting of Inconel 625

2015 ◽  
Author(s):  
Luis E. Criales ◽  
Yiğit M. Arısoy ◽  
Tuğrul Özel
Keyword(s):  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Process Parameters ◽  
Scanning Speed ◽  
Inconel 625 ◽  
Pool Data ◽  
Temperature Profiles ◽  
Laser Melting ◽  
Melt Pool ◽  
Laser Heat Source

A prediction of the 2-D temperature profile and melt pool geometry for Selective Laser Melting (SLM) of Inconel 625 metal powder with a numerically-based approach for solving the heat conduction-diffusion equation was established in this paper. A finite element method solution of the governing equation was developed. A review of the current efforts in numerical modeling for laser-based additive manufacturing is presented. Initially, two-dimensional (2-D) temperature profiles along the scanning (x-direction) and hatch direction (y-direction) are calculated for a moving laser heat source to understand the temperature rise due to heating during SLM. The effects of varying laser power, scanning speed and the powder material’s density are analyzed. Based on the predicted temperature distributions, melt pool geometry, i.e. the locations at which melting of the powder material occurs, is determined. The results are chiefly compared against the published literature on melt pool data. The main goal of this research is to develop a computational tool with which investigation of the importance of various laser, material, and process parameters on the built dimensional quality in laser-based additive manufacturing becomes not only possible but also practical and reproducible.

Numerical modeling and experimental validation of thermal history and microstructure for additive manufacturing of an Inconel 718 product

2018 ◽  
Vol 3 (1-2) ◽  
pp. 15-32 ◽  
Author(s):  
Patcharapit Promoppatum ◽  
Shi-Chune Yao ◽  
P. Chris Pistorius ◽  
Anthony D. Rollett ◽  
Peter J. Coutts ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Additive Manufacturing ◽  
Thermal History ◽  
Inconel 718 ◽  
Experimental Validation

scholarly journals Influence of FFF process parameters and macrostructure homogeneity on PLA impact strength

Polimery ◽  
2021 ◽  
Vol 66 (9) ◽  
Author(s):  
Michał Bączkowski ◽  
Dawid Marciniak ◽  
Marek Bieliński
Keyword(s):  
Image Analysis ◽  
Additive Manufacturing ◽  
Impact Strength ◽  
Cross Section ◽  
Process Parameters ◽  
Optical Microscope ◽  
Process Variables ◽  
Fused Filament Fabrication ◽  
The Impact ◽  
Printing Parameters

The article presents studies of the additive manufacturing printing parameters influence onthe impact strength of PLA samples obtained by the fused filament fabrication (FFF) method. Two processvariables were taken into account in the research program: the height of the printed layer andthe printing temperature. An optical microscope was used to analyze the cross-section image (breakthrough)of the samples. The impact strength was determined at −40°C and 23°C. Selected geometricfeatures of the macrostructure (uniformity and thickness of individual layers, voids) determined on thebasis of the sample cross-section image analysis, enhanced the possibility of assessing the PLA impactstrength, depending on the adopted process variables and the temperature at which the experiment wascarried out.

scholarly journals Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Amir M. Aboutaleb ◽  
Mark A. Tschopp ◽  
Prahalad K. Rao ◽  
Linkan Bian
Keyword(s):  
Experimental Data ◽  
Additive Manufacturing ◽  
Process Optimization ◽  
Process Parameters ◽  
Process Conditions ◽  
Optimization Approach ◽  
Geometric Accuracy ◽  
Multi Objective Optimization ◽  
Fused Filament Fabrication ◽  
Multi Objective

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

In-Situ Temperature Monitoring of ABS During Fused Filament Fabrication (FFF) Process With Varying Process Parameters

2021 ◽  
Author(s):  
Youmna Mahmoud ◽  
Souran Manoochehri
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Temperature Measurement ◽  
Process Parameters ◽  
Transfer Model ◽  
Temperature Profiles ◽  
Fused Filament Fabrication ◽  
Bed Temperature

Abstract Fused Filament Fabrication (FFF) is presently one of the most commonly used Additive Manufacturing (AM) technology for various engineering applications. However, accuracy and stability remain a major challenge during AM processes. FFF is inherently a thermal process. So, it is important to analyze and monitor the temperature evolution of each deposited filament during and after printing. This work presents an in-situ temperature measurement setup with an infrared camera, used in collecting temperature profiles of printed layers. These temperature profiles were compared to a theoretical 1D heat transfer model, demonstrating good agreement between the two sets of data. The temperature measurement experiment has been repeated for different printing process parameters, namely print speed, flowrate, and bed temperature. The effect of fan cooling is also studied. These data play a significant role in determining the optimal settings needed to achieve the desired bonding between adjacent filaments. This can be concluded by studying the effect of changing the parameters on the cooling of each deposited filament concerning the material’s glass transition temperature. The average temperature of any two adjacent layers in a part has been evaluated and compared to the material’s glass transition temperature to provide a better insight on the quality of adhesion taking place. A visual inspection of the part has also been proven to be useful in evaluating the effect on the final quality.

scholarly journals Design of Shape Memory Thermoplastic Material Systems for FDM-Type Additive Manufacturing

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4254
Author(s):  
Paulina A. Quiñonez ◽  
Leticia Ugarte-Sanchez ◽  
Diego Bermudez ◽  
Paulina Chinolla ◽  
Rhyan Dueck ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Thermomechanical Processing ◽  
Material Selection ◽  
Shape Memory Polymer ◽  
Fused Filament Fabrication ◽  
Materials Development ◽  
Thermoplastic Material ◽  
Polymer Systems ◽  
Injection Molded

The work presented here describes a paradigm for the design of materials for additive manufacturing platforms based on taking advantage of unique physical properties imparted upon the material by the fabrication process. We sought to further investigate past work with binary shape memory polymer blends, which indicated that phase texturization caused by the fused filament fabrication (FFF) process enhanced shape memory properties. In this work, two multi-constituent shape memory polymer systems were developed where the miscibility parameter was the guide in material selection. A comparison with injection molded specimens was also carried out to further investigate the ability of the FFF process to enable enhanced shape memory characteristics as compared to other manufacturing methods. It was found that blend combinations with more closely matching miscibility parameters were more apt at yielding reliable shape memory polymer systems. However, when miscibility parameters differed, a pathway towards the creation of shape memory polymer systems capable of maintaining more than one temporary shape at a time was potentially realized. Additional aspects related to impact modifying of rigid thermoplastics as well as thermomechanical processing on induced crystallinity are also explored. Overall, this work serves as another example in the advancement of additive manufacturing via materials development.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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