Process-Parameter Optimization for Fused Deposition Modeling Based on Taguchi Method

Zhang, Jin Wen; Peng, An Hua

doi:10.4028/www.scientific.net/amr.538-541.444

Cited by 65 publications

(27 citation statements)

References 5 publications

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“…Extrusion-based printing techniques where PLA is used as raw material are widely used in teaching aids [109], construction of laboratory equipment [110], agricultural instruments [111] and surgical equipment [112]. Due to the biocompatibility of PLA, its use in tissue engineering and "patient-specific" implants is also very popular [113,114]. However, it is necessary to evaluate the strength and fracture properties of the 3D printed PLA parts with the FDM technique.…”

Section: Pla In Extrusion-based Printing Technologiesmentioning

confidence: 99%

Surface Modification of 3D Printed PLA Objects by Fused Deposition Modeling: A Review

Baran

Erbil

2019

Colloids and Interfaces

148

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Polylactic acid (PLA) filaments are very popular as a thermoplastic source used in the 3D printing field by the “Fused Deposition Modeling” method in the last decade. The PLA market is expected to reach 5.2 billion US dollars in 2020 for all of its industrial uses. On the other hand, 3D printing is an expanding technology that has a large economic potential in many industries where PLA is one of the main choices as the source polymer due to its ease of printing, environmentally friendly nature, glossiness and multicolor appearance properties. In this review, we first reported the chemical structure, production methods, general properties, and present market of the PLA. Then, the chemical modification possibilities of PLA and its use in 3D printers, present drawbacks, and the surface modification methods of PLA polymers in many different fields were discussed. Specifically, the 3D printing method where the PLA filaments are used in the extrusion-based 3D printing technologies is reviewed in this article. Many methods have been proposed for the permanent surface modifications of the PLA where covalent attachments were formed such as alkaline surface hydrolysis, atom transfer polymerization, photografting by UV light, plasma treatment, and chemical reactions after plasma treatment. Some of these methods can be applied for surface modifications of PLA objects obtained by 3D printing for better performance in biomedical uses and other fields. Some recent publications reporting the surface modification of 3D printed PLA objects were also discussed.

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Section: Pla In Extrusion-based Printing Technologiesmentioning

confidence: 99%

Surface Modification of 3D Printed PLA Objects by Fused Deposition Modeling: A Review

Baran

Erbil

2019

Colloids and Interfaces

148

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“…Moreover, for the geometric deviation of the product, Sood et al employed the gray Taguchi method to study the influence of five setting variables (i.e., part orientation, deposition width, layer thickness, air gap, and deposition angle) on the product geometric deviation [ 30 ]. Similarly, Zhang and Peng applied the Taguchi method which is combined with a fuzzy comprehensive evaluation to established empirical relations between the setting variables and the geometric deviation of product [ 31 ]. However, the aforementioned works mainly concentrate on the run-to-run study to optimize the process recipe and identify the significant process setting variables in the AM process, instead of modeling the relationship between the product quality with the process variables from the sensing system which can reflect the real-time fabrication variation.…”

Section: Related Workmentioning

confidence: 99%

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

Wang

Jin

2021

Sensors

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Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.

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“…AM offers major advantages over traditional manufacturing, e.g., increased part complexity and new degrees of freedom, weight reduction and materials savings, reduced assembly and cycle times, and (mass) customization opportunities [ 3 ]. Recently, many 3D printers have begun to operate using non-proprietary feedstock materials, especially in material-extrusion-based AM (MEAM), also known as fused filament fabrication (FFF) or fused deposition modeling (FDM) [ 2 , 4 , 5 , 6 , 7 ]. Along with an opportunity to design by using a wider range of engineering-grade materials, this brings new requirements to quickly and systematically determine valid process parameters (PPs).…”

Section: Introductionmentioning

confidence: 99%

On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion

Bakradze

Arājs²,

Gaidukovs

et al. 2020

Polymers

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We present a heuristic procedure for determining key processing parameters (PPs) in materials-extrusion-based additive manufacturing processes. The concept relies on a design-of-experiment approach and consists of eleven “test objects” to determine the optimal combinations of key PPs values, starting with the PPs for printing the first layer and progressing to more complex geometric features, e.g., “bridges”. In each of the test objects, several combinations of the known PPs’ values are used, and only the values resulting in the best printed-part quality are selected for the following tests. The concept is intrinsically insensitive to different artefacts of the additive manufacturing machine (e.g., discrepancies between the nominal and actual nozzle diameters, and improper calibration of the feeding screws) and the optimal values of key PPs for manufacturing defect-free parts under the actual processing conditions can be determined. We validated the proposed procedure for two common commercial polymer feedstock materials, and we show that, by using the proposed procedure, it is possible to reduce the optimization time down to several hours, as well as to reduce the amount of consumed feedstock material. Tensile tests revealed a strong effect of amorphous and semi-crystalline nature of the polymer on the results of optimization. To the best of our knowledge, this is the first attempt to describe a systematic approach for optimizing PPs for materials extrusion-based additive manufacturing processes without relying on statistical data analysis or virtual simulations. The concept was implemented as a web-tool 3DOptimizer®.

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Process-Parameter Optimization for Fused Deposition Modeling Based on Taguchi Method

Cited by 65 publications

References 5 publications

Surface Modification of 3D Printed PLA Objects by Fused Deposition Modeling: A Review

Surface Modification of 3D Printed PLA Objects by Fused Deposition Modeling: A Review

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion

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