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1. Systemic Control of 3D Bioprinting Process Parameters to Achieve Defined Scaffold Porosity

   https://doi.org/10.1115/msec2023-104235  

   Quigley, Connor; Tuladhar, Slesha; Adhikari, Samrat; Habib, MD Ahasan (June 2023, Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference (MSEC2023))

   Abstract Due to its inbuilt ability to release biocompatible materials encapsulating living cells in a predefined location, 3D bioprinting is a promising technique for regenerating patient-specific tissues and organs. Among various 3D bioprinting techniques, extrusion-based 3D bio-printing ensures a higher percentage of cell release, ensuring suitable external and internal scaffold architectures. Scaffold architecture is mainly defined by filament geometry and width. A systematic selection of a set of process parameters, such as nozzle diameter, print speed, print distance, extrusion pressure, and material viscosity, can control the filament geometry and width, eventually confirming the user-defined scaffold porosity. For example, carefully selecting two sets of process parameters can result in a similar filament width. However, the lack of availability of sufficient analytical relations between printing process parameters and filament width creates a barrier to achieving defined scaffold architectures with available resources. In this paper, filament width was determined using an image processing technique and an analytical relationship was developed, including various process parameters to maintain defined filament width variation for different hydrogels within an acceptable range to confirm the overall geometric fidelity of the scaffold. Proposed analytical relations can help achieve defined scaffold architectures with available resources. 

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2. Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures

   AA Armstrong, A Pfeil (April 2021, Bioprinting)

   null (Ed.)

   Extrusion-based bioprinting is the most common printing technology used in regenerative medicine. Despite recent technological advances, a pressing challenge for extrusion printing is low spatial resolution, which limits the functionality of printed constructs. One of the reasons for the low spatial resolution is a lack of process monitoring and control strategies to monitor fabrication and correct for print errors. Few research efforts implement process control and investigate the relationship between extrusion process parameters and printing fidelity. The lack of understanding between process parameters and print results ultimately limits the complexity of the possible structures. For example, fabrication of structures whose topologies vary spatially within the part is not possible without advanced process control. Here we enable fabrication of advanced spatially graded structures by implementing process monitoring and control strategies. We develop material models to better understand the relationship between process parameters and printing outcomes. We also present an experimental procedure to generate a process map that provides insight into the regions of the processing space that produce the desired extrusion features (e.g., width of the filament). After generation of a process map and material models, we implement a process monitoring and control strategy that measures the feature error and intelligently updates the process control inputs to reduce defects and improve spatial fidelity, which will lead to better functionality of the final construct. 

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3. Computational Fluid Dynamics Modeling of Material Deposition in Pneumatic Micro-Extrusion Additive Manufacturing

   O’Malley, Ethan; Salary, Roozbeh “Ross” (July 2025, American Society of Mechanical Engineers (ASME) - International Mechanical Engineering Congress and Exposition (IMECE 2025))

   Pneumatic micro-extrusion (PME) presents inherent challenges in achieving precise control over material deposition due to the small scale and high viscosity of the material flow. Traditional experimental methods often fall short in capturing the complex mechanisms, interactions, and dynamics governing material transport and deposition in PME, highlighting the necessity for advanced computational approaches to unveil these intricate physical phenomena. The primary objective of this study is to develop a robust computational framework for simulating material transport and deposition in PME, offering detailed insights into the fluid dynamics, flow complexities, and deposition characteristics intrinsic to the PME process. This involves systematically investigating the influence of key process parameters, such as material properties, print speed, and flow pressure, on deposition dynamics. Three-dimensional (3D) computational fluid dynamics (CFD) modeling was employed using ANSYS Fluent, with boundary conditions defined to replicate pneumatic extrusion on a moving substrate. The CFD simulations captured three distinct deposition regimes: (i) under-extrusion, (ii) normal extrusion, and (iii) over-extrusion. Results show that decreasing print speed at constant inlet velocity produced over-extrusion, with increased bead height and width due to material overflow. At normal extrusion, bead geometry remained stable, while under-extrusion reduced bead width and ultimately led to discontinuities when flow stresses exceeded cohesive strength. Notably, bead width decreased significantly between 10 mm/s and 15 mm/s, but showed little difference between 15 mm/s and 20 mm/s. Instead, the higher speed produced discontinuities consistent with experimental observations. Centerline velocity profiles revealed that the flow inside the cartridge was very slow at approximately 0.058 mm/s, whereas the velocity increased sharply within the nozzle throat, reaching nearly 12.88 mm/s. These predictions were further supported by validation experiments, where the measured nozzle velocity of 12.95 mm/s closely matched the CFD-simulated value of 12.88 mm/s, demonstrating strong agreement between simulation and experiment. Additionally, pressure decreased slightly with increasing print speed due to reduced backflow, while nozzle velocity and wall shear stress remained unchanged under fixed inlet velocity conditions. Overall, the outcomes of this study are expected to inform parameter optimization strategies and enhance PME process efficiency, providing critical insights into how variations in PME process dynamics influence print morphology and quality, advancing the path toward optimized fabrication of scaffolds for tissue engineering applications. 

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4. Compressibility in Fused Deposition Modeling

   David O. Kazmer; Amy M. Peterson (May 2022, Society of Plastics Engineering Annual Technical Conference, Additive Manufacturing Division)

   Fused deposition modeling (FDMTM), also referred to as fused filament fabrication (FFF) is an additive manufacturing technique in which extruded material is deposited into roads and layers to form complex products. This paper provides a physics-based model for predicting and controlling the effect of compressibility in material extrusion including elasticity in the driven filament and compression of the melt in the hot end. The model is validated with a test part embodying a full factorial design of experiments with three print speeds. The model is used for control and shows elimination of 50% of the associated road width variance due to compressibility, thereby enabling higher quality levels even at higher print speeds. 

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5. Monitoring and control of biological additive manufacturing using machine learning

   https://doi.org/10.1007/s10845-023-02092-6  

   Gerdes, Samuel; Gaikwad, Aniruddha; Ramesh, Srikanthan; Rivero, Iris V.; Tamayol, Ali; Rao, Prahalada (March 2023, Journal of Intelligent Manufacturing)

   The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue constructs are twofold: (1) detect flaw formation using data from in-situ infrared thermocouple sensors; and (2) prevent flaw formation through preemptive process control. In realizing the first objective, data signatures acquired from in-situ sensors were analyzed using several machine learning approaches to ascertain critical quality metrics, such as print regime, strand width, strand height, and strand fusion severity. These quality metrics are intended to capture the process state at the basic 1D strand-level to the 2D layer-level. For this purpose, machine learning models were trained to classify and predict flaw formation. These models predicted print quality features with accuracy nearing 90%. In connection with the second objective, the previously trained machine learning models were used to preempt flaw formation by changing the process parameters (print velocity) during deposition—a form of feedforward control. With the feedforward process control, strand width heterogeneity was statistically significantly reduced, reducing the strand width difference between strand halves to less than 50 µm. Using this integrated process monitoring, detection, and control approach, we demonstrate consistent, repeatable production of Bio-AM constructs. 

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