A review on quality control in additive manufacturing

Kim, Hoejin; Lin, Yirong; Tseng, Tzu-Liang

doi:10.1108/rpj-03-2017-0048

Cited by 229 publications

(90 citation statements)

References 117 publications

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“…

FFF is known to have issues with part quality and consistency, which has limited its use to prototyping and noncritical applications where functional reliability does not affect safety. [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer.

…”

mentioning

confidence: 99%

“…[2] The occurrence of issues such as poor surface finish, layer delamination, and poor dimensional stability depends on a number of parameter settings, including nozzle temperature, print speed, environmental conditions, geometry, and location (Figure 1a,b). [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. It can be difficult for even an expert to select optimum parameter settings (Figure 1c,d).…”

mentioning

confidence: 99%

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Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Gardner

Hunt

Ebel

et al. 2019

Adv Materials Technologies

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FFF is known to have issues with part quality and consistency, which has limited its use to prototyping and noncritical applications where functional reliability does not affect safety. [2] The occurrence of issues such as poor surface finish, layer delamination, and poor dimensional stability depends on a number of parameter settings, including nozzle temperature, print speed, environmental conditions, geometry, and location (Figure 1a,b). [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. It can be difficult for even an expert to select optimum parameter settings (Figure 1c,d). [4] An operator can specify settings for over 100 different printing options using a slicing software such as Cura. Attempting all combinations is not practical, so an operator must rely on their knowledge of 3D printing to adjust parameter settings. This leads to operator-dependent part-to-part variations and uncertainty in the performance of the final part. Additionally, an operator will typically select a set of global parameter settings that are used throughout the part, or at most make some layer-to-layer adjustments. A more objective solution for selecting parameter settings is needed to ensure optimal use of the printer/material performance space and consistency in 3D-printed parts, regardless of part geometry, material, system, or operator.Machine learning offers a potential approach to addressing this problem. Machine learning models are trained on thousands to millions of data points to recognize patterns that are too difficult to identify using deterministic algorithms. [5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [5][6][7] Potential uses for machine learning in 3D printing have also been studied in a limited capacity. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [28,29] An Quality control and repeatability of 3D printing must be enhanced to fully unlock its utility beyond prototyping and noncritical applications. Machine learning is a potential solution to improving 3D printing performance and is explored for areas including flaw identification and property prediction. However, critical problems must be resolved before machine learning can truly enable 3D printing to reach its potential, including the very large data sets required for training and the inherently local nature of 3D print...

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“…

FFF is known to have issues with part quality and consistency, which has limited its use to prototyping and noncritical applications where functional reliability does not affect safety. [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer.

…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Gardner

Hunt

Ebel

et al. 2019

Adv Materials Technologies

View full text Add to dashboard Cite

FFF is known to have issues with part quality and consistency, which has limited its use to prototyping and noncritical applications where functional reliability does not affect safety. [2] The occurrence of issues such as poor surface finish, layer delamination, and poor dimensional stability depends on a number of parameter settings, including nozzle temperature, print speed, environmental conditions, geometry, and location (Figure 1a,b). [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. It can be difficult for even an expert to select optimum parameter settings (Figure 1c,d). [4] An operator can specify settings for over 100 different printing options using a slicing software such as Cura. Attempting all combinations is not practical, so an operator must rely on their knowledge of 3D printing to adjust parameter settings. This leads to operator-dependent part-to-part variations and uncertainty in the performance of the final part. Additionally, an operator will typically select a set of global parameter settings that are used throughout the part, or at most make some layer-to-layer adjustments. A more objective solution for selecting parameter settings is needed to ensure optimal use of the printer/material performance space and consistency in 3D-printed parts, regardless of part geometry, material, system, or operator.Machine learning offers a potential approach to addressing this problem. Machine learning models are trained on thousands to millions of data points to recognize patterns that are too difficult to identify using deterministic algorithms. [5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [5][6][7] Potential uses for machine learning in 3D printing have also been studied in a limited capacity. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [28,29] An Quality control and repeatability of 3D printing must be enhanced to fully unlock its utility beyond prototyping and noncritical applications. Machine learning is a potential solution to improving 3D printing performance and is explored for areas including flaw identification and property prediction. However, critical problems must be resolved before machine learning can truly enable 3D printing to reach its potential, including the very large data sets required for training and the inherently local nature of 3D print...

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“…It has excellent capabilities for highly geometric complex shapes and features which create unlimited possibilities in a production system. [10][11][12][13][14] By properly implementation of this technology in medical, it reduces the number of production steps of manufacturing of medical parts, material waste during production, inventory, eliminates the amount of tooling required and manufacture the complete part in less time and cost. [14][15][16] This paper studied about additive manufacturing and different processes of 3D printing technologies in detail.…”

Section: Introductionmentioning

confidence: 99%

3D printed medical parts with different materials using additive manufacturing

Haleem

Javaid

2020

Clinical Epidemiology and Global Health

108

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Additive manufacturing has the potential to improve the design and development of medical parts and products. This technology is applied for the manufacturing of customised and sophisticated products with different materials, thereby performing it with lesser wastage of time and material. There is a scope to achieve various flexibilities by using different software and technological platforms. AM helps improve the accuracy and reliability of the design, as well as ease of design. This technology is now available for improving the performance of medical part by providing unique and innovative design through the use of different materials with improved quality of the product. This emerging technology provides an extensive capability for future development.

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“…It requires no heating/cooling or cross‐linking since the liquid to solid transition occurs by solvent evaporation . This technique provides moldless fabrication by depositing a concentrated colloidal suspension in the continuous flow of layer‐by‐layer build sequence under computer‐controlled paths . The BT colloidal suspensions (ie, ceramic paste) are consisted of ceramic powder and various chemicals such as binder, plasticizer, and dispersant to help colloidal suspensions extrude smoothly out of small syringe tip and printed layers retain the shape .…”

Section: Introductionmentioning

confidence: 99%

Fabrication of bulk piezoelectric and dielectric BaTiO₃ ceramics using paste extrusion 3D printing technique

et al. 2018

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A simple and facile method was developed to fabricate functional bulk barium titanate (BaTiO3, BT) ceramics using the paste extrusion 3D printing technique. The BT ceramic is a lead‐free ferroelectric material widely used for various applications in sensors, energy storage, and harvesting. There are several traditional methods (eg, tape casting) to process bulk BT ceramics but they have disadvantages such as difficult handing without shape deformation, demolding, complex geometric shapes, expansive molds, etc. In this research, we utilized the paste extrusion 3D printing technique to overcome the traditional issues and developed printable ceramic suspensions containing BT ceramic powder, polyvinylidene fluoride (PVDF), N,N‐dimethylformamide (DMF) through simple mixing method and chemical formulation. This PVDF solution erformed multiple roles of binder, plasticizer, and dispersant for excellent manufacturability while providing high volume percent and density of the final bulk ceramic. Based on empirical data, it was found that the maximum binder ratio with good viscosity and retention for desired geometry is 1:8.8, while the maximum BT content is 35.45 vol% (77.01 wt%) in order to achieve maximum density of 3.93 g/cm3 (65.3%) for 3D printed BT ceramic. Among different sintering temperatures, it was observed that the sintered BT ceramic at 1400°C had highest grain growth and tetragonality which affected high performing piezoelectric and dielectric properties, 200 pC/N and 4730 at 103 Hz respectively. This paste extrusion 3D printing technique and simple synthesis method for ceramic suspensions are expected to enable rapid massive production, customization, design flexibility of the bulk piezoelectric and dielectric devices for next generation technology.

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A review on quality control in additive manufacturing

Cited by 229 publications

References 117 publications

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

3D printed medical parts with different materials using additive manufacturing

Fabrication of bulk piezoelectric and dielectric BaTiO₃ ceramics using paste extrusion 3D printing technique

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A review on quality control in additive manufacturing

Cited by 229 publications

References 117 publications

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

3D printed medical parts with different materials using additive manufacturing

Fabrication of bulk piezoelectric and dielectric BaTiO3 ceramics using paste extrusion 3D printing technique

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Product

Resources

About

Fabrication of bulk piezoelectric and dielectric BaTiO₃ ceramics using paste extrusion 3D printing technique