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Abstract Current research practice for optimizing bioink involves exhaustive experimentation with multi-material composition for determining the printability, shape fidelity and biocompatibility. Predicting bioink properties can be beneficial to the research community but is a challenging task due to the non-Newtonian behavior in complex composition. Existing models such as Cross model become inadequate for predicting the viscosity for heterogeneous composition of bioinks. In this paper, we utilize a machine learning framework to accurately predict the viscosity of heterogeneous bioink compositions, aiming to enhance extrusion-based bioprinting techniques. Utilizing Bayesian optimization (BO), our strategy leverages a limited dataset to inform our model. This is a technique especially useful of the typically sparse data in this domain. Moreover, we have also developed a mask technique that can handle complex constraints, informed by domain expertise, to define the feasible parameter space for the components of the bioink and their interactions. Our proposed method is focused on predicting the intrinsic factor (e.g. viscosity) of the bioink precursor which is tied to the extrinsic property (e.g. cell viability) through the mask function. Through the optimization of the hyperparameter, we strike a balance between exploration of new possibilities and exploitation of known data, a balance crucial for refining our acquisition function. This function then guides the selection of subsequent sampling points within the defined viable space and the process continues until convergence is achieved, indicating that the model has sufficiently explored the parameter space and identified the optimal or near-optimal solutions. Employing this AI-guided BO framework, we have developed, tested, and validated a surrogate model for determining the viscosity of heterogeneous bioink compositions. This data-driven approach significantly reduces the experimental workload required to identify bioink compositions conducive to functional tissue growth. It not only streamlines the process of finding the optimal bioink compositions from a vast array of heterogeneous options but also offers a promising avenue for accelerating advancements in tissue engineering by minimizing the need for extensive experimental trials. 

Abstract The performance of conventional image processing techniques is highly dependent on many parameters like image quality, light source, background surface texture, optimal threshold value and particle morphology. However, during intermediate stages of manufacturing processes (such as continuous deposition, coating, mixing, and transfer), complex backgrounds can arise from heterogeneous particle-substrate (HPS) systems. In such HPS environments, particles become integrated with substrates or suspended in liquid carriers or etching media, making them challenging to identify using traditional particle analysis tools and techniques. In response to this challenge, a deep learning object detection algorithm (YOLO) has been put into practical use. Initially, an HPS (heterogeneous particle-substrate) system was created using a wet-deposition particle transfer process that involved the immersion of poly-disperse particles on to a cylindrical substrate. By manipulating the capillary number in the wet-deposition process, four distinct HPS morphologies were captured, each characterized by variations in image heterogeneity. These morphologies were subsequently subjected to detailed analysis with neural network-based AI algorithm. The proposed artificial intelligence tool has demonstrated an impressive ability to identify and analyze poly-dispersed particles within HPS morphologies, achieving an accuracy rate of over 97%. We can evaluate the quality of sorting by calculating the particle size distribution using the proposed method and find the ideal process parameters for the particle transfer process. The results of this study, outlined in this paper, underscore the potential of deep learning as a particle analysis tool for in-situ applications, even in environments with heterogeneous backgrounds. This developed tool holds promise for various manufacturing processes, including semiconductor industries, high-density powder-based 3D printing, powder metallurgy, refractory coatings in harsh environments, and particle sorting, among others. 

Abstract The fisherman’s knot, renowned for its strength and reliability, finds applications in engineering and medicine. However, a comprehensive understanding of its mechanics remains limited in scientific literature. In this paper, we present a systematic study of the tightening behavior of the fisherman’s knot through a combined approach of tabletop experiments and discrete elastic rods simulations. Our experimental setup involves gradually applying tension to the two ends of the fisherman’s knot until it fractures. We observed a correlation between the knot’s material properties and its behavior during tightening, leading up to fracture. The tightening process of the fisherman’s knot exhibits distinct “sliding” or “stretching” motions, influenced by factors such as friction and elastic stiffness. Furthermore, the failure modes of the knot (material fracture and topological failure) are determined by an interplay between elastic stiffness, friction, and initial conditions. This study sheds light on the underlying mechanics of the fisherman’s knot and provides insight into its behavior during the tightening process, contributing to the broader understanding of the mechanics of knots in practical applications. 

Abstract We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing. 

Abstract Changing the surface properties (i.e., roughness or friction) can be instrumental for many applications but can be a complex and resource-intensive process. In this paper, we demonstrate a novel process of controlling the friction of a continuous rod by delivering inorganic microparticles. A standardized continuous particle transfer protocol has been developed in our laboratory for depositing particles from a liquid carrier system (LCS) to the cylindrical rod substrate. The particle transfer process can produce controllable and tunable surface properties. Polymeric binder is used to deliver the particles as asperities over the rod substrate and by controlling their size, shape, and distribution, the coefficient of friction of the rod is determined. Tabletop experiments are designed and performed to measure the friction coefficient following the Capstan equation. The entrained particles on the substrate will create size- and shape-based asperities, which will alter the surface morphology toward the desired direction. Both oblique and direct quantitative measurements are performed at different particles and binder concentrations. A systematic variation in the friction coefficient is observed and reported in the result section. It is observed from the capstan experiment that adding only 1% irregular shaped particles in the suspension changes the friction coefficient of the rods by almost 115%. The proposed friction control technique is a simple scale-up, low-cost, low-waste, and low-energy manufacturing method for controlling the surface morphology. 

Abstract In this work, the physical phenomenon of the polydisperse micro-particle entrainment process from density mismatch mixture is investigated with the variation of substrate withdrawal speed. A liquid carrier system (LCS) is prepared by a polymer-based binder and an evaporating solvent. Nickel-based inorganic and spherical particles with a. moderate vol%. of 35% are added to the LCS solution. The cylindrical AISI 1006 mild steel wire substrate is dipped at different withdrawal speed ranging from 0.01 mms-1 to 20 mms-1. The binder vol%. is varied between 6.5% and 10.5%. Once the cylindrical substrate is extracted from the mixture, the surface coverage and the particle size are measured following the image analysis technique. The average particle size, coating thickness and the surface packing coverage by the particles are increasing with the higher withdrawal speed of the substrate. We observed relatively low size of particles (< 10 micrometers) as well as low surface coverage (∼33%) when the withdrawal speed remains at 0.01 mm/s. However, with high withdrawal speed (20 mm/s), we found all sizes of particles present on the substrate with a surface coverage of over 90%. The finding of this research will help to understand the high-volume solid transfer technique and develop a novel manufacturing process. 

Abstract In manufacturing industries, spherical micro-particles are commonly used as (e.g., brazing powder, metal filler, and 3D printing powder) which are produced with droplet-based particle fabrication techniques. Such processes create spherical morphology but introduce polydispersity and follow a continuous exponential pattern commonly expressed with Rosin-Rammler expression. Sorting those micro-particles in a narrower size range is an important but difficult, costly, and challenging process. Here we demonstrate the successful separation of the particles from a poly-disperse mixture with a particle volume fraction of 10% by dipping process. Nickel-based micro-particles (avg. dia. 5.69 μm) are added in a binder-based liquid carrier system. To encounter the gravitational force, external kinetic energy in the form of agitation is applied to ensure the uniform dispersion of the particles. The cylindrical substrate is prepared and dipped in the ‘pseudo suspension’ to separate the particles by adhering to it. The substrate is dried, and images are taken to characterize the separated particles using image J software. A clear size distribution can be observed which is also plotted. Additionally, a relationship between the process parameter and sorted particles has been established. The proposed method is quick, controllable, and easy to implement, which can be a useful tool for sorting wide-range poly-disperse particles. 

Abstract Micro-scale inorganic particles (d > 1 µm) have reduced surface area and higher density, making them negatively buoyant in most dip-coating mixtures. Their controlled delivery in hard-to-reach places through entrainment is possible but challenging due to the density mismatch between them and the liquid matrix called liquid carrier system (LCS). In this work, the particle transfer mechanism from the complex density mismatching mixture was investigated. The LCS solution was prepared and optimized using a polymer binder and an evaporating solvent. The inorganic particles were dispersed in the LCS by stirring at the just suspending speed to maintain the pseudo suspension characteristics for the heterogeneous mixture. The effect of solid loading and the binder volume fraction on solid transfer has been reported at room temperature. Two coating regimes are observed (i) heterogeneous coating where particle clusters are formed at a low capillary number and (ii) effective viscous regime, where full coverage can be observed on the substrate. ‘Zero’ particle entrainment was not observed even at a low capillary number of the mixture, which can be attributed to the presence of the binder and hydrodynamic flow of the particles due to the stirring of the mixture. The critical film thickness for particle entrainment is$${h}^{*}=0.16a$$ $h^{\ast }=0.16a$for 6.5% binder and$${h}^{*}=0.26a$$ $h^{\ast }=0.26a$for 10.5% binder, which are smaller than previously reported in literature. Furthermore, the transferred particle matrices closely follow the analytical expression (modified LLD) of density matching suspension which demonstrate that the density mismatch effect can be neutralized with the stirring energy. The findings of this research will help to understand this high-volume solid transfer technique and develop novel manufacturing processes.