1D and 2D error assessment and correction for extrusion-based bioprinting using process sensing and control strategies

Armstrong, Ashley A.; Alleyne, Andrew G.; Johnson, Amy J. Wagoner

doi:10.1088/1758-5090/aba8ee

Cited by 29 publications

(15 citation statements)

References 36 publications

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“…In another study, trajectory measurements and corrections were performed along with simultaneous adjustment and correction of the bone scaffold width via a method similar to that mentioned above. 49 The final bone scaffold trajectory error and bone scaffold width error were reduced to an acceptable range (except for the end part). Additionally, the bone scaffold width control problem affects the bioprinting resolution.…”

Section: Bone Printing Process Controlmentioning

confidence: 95%

Computer vision-aided bioprinting for bone research

et al. 2022

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Bioprinting is an emerging additive manufacturing technology that has enormous potential in bone implantation and repair. The insufficient accuracy of the shape of bioprinted parts is a primary clinical barrier that prevents widespread utilization of bioprinting, especially for bone design with high-resolution requirements. During the last five years, the use of computer vision for process control has been widely practiced in the manufacturing field. Computer vision can improve the performance of bioprinting for bone research with respect to various aspects, including accuracy, resolution, and cell survival rate. Hence, computer vision plays a substantial role in addressing the current defect problem in bioprinting for bone research. In this review, recent advances in the application of computer vision in bioprinting for bone research are summarized and categorized into three groups based on different defect types: bone scaffold process control, deep learning, and cell viability models. The collection of printing parameters, data processing, and feedback of bioprinting information, which ultimately improves printing capabilities, are further discussed. We envision that computer vision may offer opportunities to accelerate bioprinting development and provide a new perception for bone research.

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Section: Bone Printing Process Controlmentioning

confidence: 95%

Computer vision-aided bioprinting for bone research

et al. 2022

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“…The first approach is the in‐process monitoring of the 3D printing procedure based on optical methods. [ 9–11 ] Similar process optimization strategies include the preceding image‐analysis of larger datasets of printed objects to define the acceptable parameter range. Among the important parameters are thickness and uniformity, [ 12,13 ] the structure fidelity of extruded filaments while traversing lower orthogonal layers [ 14 ] and the connectivity respectively the separation of channels or pore systems.…”

Section: Introductionmentioning

confidence: 99%

“…The first approach is the in-process monitoring of the 3D printing procedure based on optical methods. [9][10][11] Similar process optimization strategies include the preceding image-analysis of larger datasets of printed objects to define the acceptable parameter range.…”

mentioning

confidence: 99%

Magnetic resonance imaging as a tool for quality control in extrusion‐based bioprinting

Schmieg

Gretzinger

Schuhmann

et al. 2022

Biotechnology Journal

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Bioprinting is gaining importance for the manufacturing of tailor‐made hydrogel scaffolds in tissue engineering, pharmaceutical research and cell therapy. However, structure fidelity and geometric deviations of printed objects heavily influence mass transport and process reproducibility. Fast, three‐dimensional and nondestructive quality control methods will be decisive for the approval in larger studies or industry. Magnetic resonance imaging (MRI) meets these requirements for characterizing heterogeneous soft materials with different properties. Complementary to the idea of decentralized 3D printing, magnetic resonance tomography is common in medicine, and image data processing tools can be transferred system‐independently. In this study, a MRI measurement and image analysis protocol was evaluated to jointly assess the reproducibility of three different hydrogels and a reference material. Critical parameters for object quality, namely porosity, hole areas and deviations along the height of the scaffolds are discussed. Geometric deviations could be correlated to specific process parameters, anomalies of the ink or changes of ambient conditions. This strategy allows the systematic investigation of complex 3D objects as well as an implementation as a process control tool. Combined with the monitoring of metadata this approach might pave the way for future industrial applications of 3D printing in the field of biopharmaceutics.

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“…Armstrong et al . presented an iteration-to-iteration process monitoring system that enabled direct process feedback in material deposition based on the laser displacement scanner integrated to the printing platform[ 6 , 18 ]. They modified the spatial material placement error and the material width error, and developed process control strategies based on the measured errors to adjust control inputs and ultimately eliminate material deposition errors.…”

Section: Introductionmentioning

confidence: 99%

In situ defect detection and feedback control with three-dimensional extrusion-based bioprinter-associated optical coherence tomography

Yang¹,

Chen²,

Xu³

2022

IJB

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Extrusion-based three-dimensional (3D) bioprinting is one of the most common methods used for tissue fabrication and is the most widely used additive manufacturing technique in all industries. In extrusion-based bioprinting, printing defects related to material deposition errors lead to a significant deviation from shape to function between the printed construct and design model. Using 3D extrusion-based bioprinter-associated optical coherence tomography (3D P-OCT), an in situ defect detection and feedback system was presented based on the accurate defect analysis and location, and a pre-built feedback mechanism. Using 3D P-OCT, multi-parameter quantification of the material deposition was carried out in real time, including the filament size, layer thickness, and layer fidelity. The material deposition errors under different paths were quantified and located specifically, including the start-stop points, straight-line path, and turnarounds. The pre-built feedback mechanism involving the control inputs, such as printing path, pressure, and velocity, provided the basis for in situ defect detection and real-time feedback control. In particular, the second printing repair can be performed after the broken filament defect is detected and located. After printing, fidelity can be quantitatively analyzed based on the point cloud registration between the 3D P-OCT result and the design model. In conclusion, 3D P-OCT enables in situ defect detection and feedback control, broken filament repair, and 3D fidelity analysis to achieve high-fidelity printing from shape to function.

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1D and 2D error assessment and correction for extrusion-based bioprinting using process sensing and control strategies

Cited by 29 publications

References 36 publications

Computer vision-aided bioprinting for bone research

Computer vision-aided bioprinting for bone research

Magnetic resonance imaging as a tool for quality control in extrusion‐based bioprinting

In situ defect detection and feedback control with three-dimensional extrusion-based bioprinter-associated optical coherence tomography

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