Improving 3D Printing Geometric Accuracy Using Design of Experiments on Process Parameters in Fused Filament Fabrication (FFF)

2021 ◽  
Author(s):  
Jorg Schneidler ◽  
Cody Berry ◽  
Ahmad Barari
Keyword(s):  
3D Printing ◽  
Design Of Experiments ◽  
Process Parameters ◽  
Geometric Accuracy ◽  
Fused Filament Fabrication

scholarly journals The Impact of 3D Printing Process Parameters on the Dielectric Properties of High Permittivity Composites

Designs ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 50 ◽  
Author(s):  
Athanasios Goulas ◽  
Shiyu Zhang ◽  
Darren A. Cadman ◽  
Jan Järveläinen ◽  
Ville Mylläri ◽  
...  
Keyword(s):  
3D Printing ◽  
Process Parameters ◽  
Relative Permittivity ◽  
Main Process ◽  
Fused Filament Fabrication ◽  
Layer Height ◽  
Additive Manufacturing Technology ◽  
The Impact ◽  
Printing Speed ◽  
Hatch Spacing

Fused filament fabrication (FFF) is a well-known and greatly accessible additive manufacturing technology, that has found great use in the prototyping and manufacture of radiofrequency componentry, by using a range of composite thermoplastic materials that possess superior properties, when compared to standard materials for 3D printing. However, due to their nature and synthesis, they are often a great challenge to print successfully which in turn affects their microwave properties. Hence, determining the optimum printing strategy and settings is important to advance this area. The manufacturing study presented in this paper shows the impact of the main process parameters: printing speed, hatch spacing, layer height and material infill, during 3D printing on the relative permittivity (εr), and loss tangent (tanδ) of the resultant additively manufactured test samples. A combination of process parameters arising from this study, allowed successful 3D printing of test samples, that marked a relative permittivity of 9.06 ± 0.09 and dielectric loss of 0.032 ± 0.003.

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.

scholarly journals The Direct 3D Printing of Functional PEEK/Hydroxyapatite Composites via a Fused Filament Fabrication Approach

Polymers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 545
Author(s):  
Krzysztof Rodzeń ◽  
Preetam K. Sharma ◽  
Alistair McIlhagger ◽  
Mozaffar Mokhtari ◽  
Foram Dave ◽  
...  
Keyword(s):  
Tensile Strength ◽  
3D Printing ◽  
Selective Laser Sintering ◽  
Fused Filament Fabrication ◽  
Bone Apposition ◽  
Innovative Solutions ◽  
3D Printed ◽  
Work Samples ◽  
Viable Approach ◽  
Crystalline Domains

The manufacture of polyetheretherketone/hydroxyapatite (PEEK/HA) composites is seen as a viable approach to help enhance direct bone apposition in orthopaedic implants. A range of methods have been used to produce composites, including Selective Laser Sintering and injection moulding. Such techniques have drawbacks and lack flexibility to manufacture complex, custom-designed implants. 3D printing gets around many of the restraints and provides new opportunities for innovative solutions that are structurally suited to meet the needs of the patient. This work reports the direct 3D printing of extruded PEEK/HA composite filaments via a Fused Filament Fabrication (FFF) approach. In this work samples are 3D printed by a custom modified commercial printer Ultimaker 2+ (UM2+). SEM-EDX and µCT analyses show that HA particles are evenly distributed throughout the bulk and across the surface of the native 3D printed samples, with XRD highlighting up to 50% crystallinity and crystalline domains clearly observed in SEM and HR-TEM analyses. This highlights the favourable temperature conditions during 3D printing. The yield stress and ultimate tensile strength obtained for all the samples are comparable to human femoral cortical bone. The results show how FFF 3D printing of PEEK/HA composites up to 30 wt% HA can be achieved.

Fused Filament Fabrication 3D Printed Polylactic Acid Electroosmotic Pumps

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Liang Wu ◽  
Stephen Beirne ◽  
Joan-Marc Cabot Canyelles ◽  
Brett Paull ◽  
Gordon G. Wallace ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Polylactic Acid ◽  
Fused Filament Fabrication ◽  
Flexible Approach ◽  
Electroosmotic Pump ◽  
3D Printed ◽  
Electroosmotic Pumps

Additive manufacturing (3D printing) offers a flexible approach for the production of bespoke microfluidic structures such as the electroosmotic pump. Here a readily accessible fused filament fabrication (FFF) 3D printing...

scholarly journals Gaining a better understanding of the extrusion process in fused filament fabrication 3D printing: a review

2021 ◽  
Author(s):  
Bahaa Shaqour ◽  
Mohammad Abuabiah ◽  
Salameh Abdel-Fattah ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
...  
Keyword(s):  
3D Printing ◽  
Numerical Models ◽  
Extrusion Process ◽  
Analytical Models ◽  
Limiting Factor ◽  
Fused Filament Fabrication ◽  
Promising Tool ◽  
Relevant Work ◽  
3D Printing Technique ◽  
Required Quality

AbstractAdditive manufacturing is a promising tool that has proved its value in various applications. Among its technologies, the fused filament fabrication 3D printing technique stands out with its potential to serve a wide variety of applications, ranging from simple educational purposes to industrial and medical applications. However, as many materials and composites can be utilized for this technique, the processability of these materials can be a limiting factor for producing products with the required quality and properties. Over the past few years, many researchers have attempted to better understand the melt extrusion process during 3D printing. Moreover, other research groups have focused on optimizing the process by adjusting the process parameters. These attempts were conducted using different methods, including proposing analytical models, establishing numerical models, or experimental techniques. This review highlights the most relevant work from recent years on fused filament fabrication 3D printing and discusses the future perspectives of this 3D printing technology.

scholarly journals Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

2021 ◽  
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Single Point ◽  
Tool Material ◽  
Deformation Mode ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Tool Shape ◽  
Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

scholarly journals Effects of 3D Printing-Line Directions for Stretchable Sensor Performances

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1791
Author(s):  
Chi Cuong Vu ◽  
Thanh Tai Nguyen ◽  
Sangun Kim ◽  
Jooyong Kim
Keyword(s):  
3D Printing ◽  
Thermoplastic Polyurethane ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Human Motion ◽  
Repeated Measurements ◽  
Fused Filament Fabrication ◽  
And Performance ◽  
The Times ◽  
Stretchable Sensors

Health monitoring sensors that are attached to clothing are a new trend of the times, especially stretchable sensors for human motion measurements or biological markers. However, price, durability, and performance always are major problems to be addressed and three-dimensional (3D) printing combined with conductive flexible materials (thermoplastic polyurethane) can be an optimal solution. Herein, we evaluate the effects of 3D printing-line directions (45°, 90°, 180°) on the sensor performances. Using fused filament fabrication (FDM) technology, the sensors are created with different print styles for specific purposes. We also discuss some main issues of the stretch sensors from Carbon Nanotube/Thermoplastic Polyurethane (CNT/TPU) and FDM. Our sensor achieves outstanding stability (10,000 cycles) and reliability, which are verified through repeated measurements. Its capability is demonstrated in a real application when detecting finger motion by a sensor-integrated into gloves. This paper is expected to bring contribution to the development of flexible conductive materials—based on 3D printing.

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