Resource Based Build Direction in Additive Manufacturing Process

Three dimensional free-form geometric shapes can be built by putting layers upon layer in a predefined direction via Additive Manufacturing (AM) processes. The fabrication processes require computational as well as physical resources and can vary not only upon the product but its process plan. Overly simplified process plan may expedite the pre-fabrication techniques, but may create difficulty during fabrication of those slices. For an example, slices with concavity or discrete contour plurality may introduce deposition discontinuity, over deposition, and higher build time during the fabrication. These issues demand more resources there by affecting the part quality and fabrication cost. In this work, we focus upon the build direction of AM process plan to address the fabrication and resource utilization. First, a set of uniform build direction is identified and the object is discretized using a set of critical points considering the object concavity along the build direction. Cutting planes are generated and the object is discretized into strips and each strip is analyzed for contour plurality and the build directions are quantified through the allocation of importance factors. The optimal build direction thus found will result in lowest possible fabrication complexity. The proposed methodology is implemented and presented with a sample example in this paper.Mechanical Engineerin

Optimizing Material Deposition Direction for Functional Internal Architecture in Additive Manufacturing Processes

AbstractOne of the major constraints of additive manufacturing processes is its relatively slower speed. Higher throughput is achieved by using newer and better hardware which is limited under many circumstances. Better additive manufacturing process plan algorithm to expedite the process is quick and easy to implement. Current material deposition techniques are primarily differentiated into fine contours and course infill deposition for faster build time. However, the internal architecture of the part cannot be ignored in functional and free-form objects. A free-form object with internal feature can be decomposed into two dimensional layers with islands where each island represents an associated feature's properties. The material deposition path-plan in such multi-contour layers can be interrupted by frequent directional changes. Dealing with these interruptions during fabrication requires more resources and may affect the part's integrity, quality, and build time. This research aims to minimize such interruptions in the decomposed slices for layer based additive manufacturing by focusing on deposition direction. A computational algorithm is proposed for the free form object with or without internal hollow feature that quantifies the deposition direction considering the feature geometry and building time. Moreover, a new filling pattern is proposed following the optimal deposition direction. The proposed algorithm to optimize the deposition direction will improve the process plan for objects containing internal hollow features

3D Printability of Alginate-Carboxymethyl Cellulose Hydrogel

Three-dimensional (3D) bio-printing is a revolutionary technology to reproduce a 3D functional living tissue scaffold in-vitro through controlled layer-by-layer deposition of biomaterials along with high precision positioning of cells. Due to its bio-compatibility, natural hydrogels are commonly considered as the scaffold material. However, the mechanical integrity of a hydrogel material, especially in 3D scaffold architecture, is an issue. In this research, a novel hybrid hydrogel, that is, sodium alginate with carboxymethyl cellulose (CMC) is developed and systematic quantitative characterization tests are conducted to validate its printability, shape fidelity and cell viability. The outcome of the rheological and mechanical test, filament collapse and fusion test demonstrate the favorable shape fidelity. Three-dimensional scaffold structures are fabricated with the pancreatic cancer cell, BxPC3 and the 86% cell viability is recorded after 23 days. This hybrid hydrogel can be a potential biomaterial in 3D bioprinting process and the outlined characterization techniques open an avenue directing reproducible printability and shape fidelity

In-situ particle analysis with heterogeneous background: a machine learning approach

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

What Questions Are on the Minds of STEM Undergraduate Students and How Can They Be Addressed?

Addressing common student questions in introductory STEM courses early in the term is one way that instructors can ensure that their students have all been presented with information about how to succeed in their courses. However, categorizing student questions and identifying evidence-based resources to address student questions takes time, and instructors may not be able to easily collect and respond to student questions at the beginning of every course. To help faculty effectively anticipate and respond to student questions, we 1) administered surveys in multiple STEM courses to identify common student questions, 2) conducted a qualitative analysis to determine categories of student questions (e.g., what are best practices for studying, how can in- and out-of- course time be effectively used), and 3) collaboratively identified advice on how course instructors can answer these questions. Here, we share tips, evidence-based strategies, and resources from faculty that instructors can use to develop their own responses for students. We hope that educators can use these common student questions as a starting point to proactively address questions throughout the course and that the compiled resources will allow instructors to easily find materials that can be considered for their own courses