Hierarchical volumetric object representations for digital fabrication workflows

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 111-114).Modern systems for computer-aided design and manufacturing (CAD/CAM) have a history dating back to drafting boards, early computers, and machine shops with specialized technicians for each stage in a manufacturing workflow. In recent years, personal-scale digital fabrication has challenged many of these workflows' build-in assumptions. A single individual may control the entire workflow, from design to manufacture; they will be using computers that are exponentially more powerful than those in the 1970s; and they may be using a wide variety of tools, machines, and processes. The variety of tools and machines leads to a combinatorial explosion of possible workflows. In addition, tools are based on boundary representations, which are fragile and can easily describe nonsensical objects. This thesis addresses these issues with a set of tools for end-to-end digital fabrication based on volumetric solid models. Workflows are modular, making it easy to add new machines, and a shared core of path-planning operations reduces system complexity. Replacing boundary representations with volumetric representations guarantees that models represent reasonable real-world solids. Adaptively sampled distance fields are used as a generic interchange format. Functional representations are used as a design representation, and we examine scaling behavior and efficient rendering. We present interactive design tools that use these representations as their geometry engine. Data from CT scans is also used to populate these distance fields, showing significant benefits in file size and resolution compared to meshes. Finally, these representations are used as inputs to a modular multimachine CAM workflow. Toolpath generation is implemented, characterized, and tested on a complex solid model. We conclude with a summary of results and recommendations for future research directions.by Matthew Keeter.S.M

Flow-based fabrication: An integrated computational workflow for design and digital additive manufacturing of multifunctional heterogeneously structured objects

Structural hierarchy and material organization in design are traditionally achieved by combining discrete homogeneous parts into functional assemblies where the shape or surface is the determining factor in achieving function. In contrast, biological structures express higher levels of functionality on a finer scale through volumetric cellular constructs that are heterogeneous and complex. Despite recent advancements in additive manufacturing of functionally graded materials, the limitations associated with computational design and digital fabrication of heterogeneous materials and structures frame and limit further progress. Conventional computer-aided design tools typically contain geometric and topologic data of virtual constructs, but lack robust means to integrate material composition properties within virtual models. We present a seamless computational workflow for the design and direct digital fabrication of multi-material and multi-scale structured objects. The workflow encodes for and integrates domain-specific meta-data relating to local, regional and global feature resolution of heterogeneous material organizations. We focus on water-based materials and demonstrate our approach by additively manufacturing diverse constructs associating shape-informing variable flow rates and material properties to mesh-free geometric primitives. The proposed workflow enables virtual-to-physical control of constructs where structural, mechanical and optical gradients are achieved through a seamless design-to-fabrication tool with localized control. An enabling technology combining a robotic arm and a multi-syringe multi nozzle deposition system is presented. Proposed methodology is implemented and full-scale demonstrations are included

Foundry: Hierarchical Material Design for Multi-Material Fabrication

We demonstrate a new approach for designing functional material definitions for multi-material fabrication using our system called Foundry. Foundry provides an interactive and visual process for hierarchically designing spatially-varying material properties (e.g., appearance, mechanical, optical). The resulting meta-materials exhibit structure at the micro and macro level and can surpass the qualities of traditional composites. The material definitions are created by composing a set of operators into an operator graph. Each operator performs a volume decomposition operation, remaps space, or constructs and assigns a material composition. The operators are implemented using a domain-specific language for multi-material fabrication; users can easily extend the library by writing their own operators. Foundry can be used to build operator graphs that describe complex, parameterized, resolution-independent, and reusable material definitions. We also describe how to stage the evaluation of the final material definition which in conjunction with progressive refinement, allows for interactive material evaluation even for complex designs. We show sophisticated and functional parts designed with our system.National Science Foundation (U.S.) (1138967)National Science Foundation (U.S.) (1409310)National Science Foundation (U.S.) (1547088)National Science Foundation (U.S.). Graduate Research Fellowship ProgramMassachusetts Institute of Technology. Undergraduate Research Opportunities Progra

Machine learning-based automated segmentation with a feedback loop for 3D synchrotron micro-CT

Die Entwicklung von Synchrotronlichtquellen der dritten Generation hat die Grundlage für die Untersuchung der 3D-Struktur opaker Proben mit einer Auflösung im Mikrometerbereich und höher geschaffen. Dies führte zur Entwicklung der Röntgen-Synchrotron-Mikro-Computertomographie, welche die Schaffung von Bildgebungseinrichtungen zur Untersuchung von Proben verschiedenster Art förderte, z.B. von Modellorganismen, um die Physiologie komplexer lebender Systeme besser zu verstehen. Die Entwicklung moderner Steuerungssysteme und Robotik ermöglichte die vollständige Automatisierung der Röntgenbildgebungsexperimente und die Kalibrierung der Parameter des Versuchsaufbaus während des Betriebs. Die Weiterentwicklung der digitalen Detektorsysteme führte zu Verbesserungen der Auflösung, des Dynamikbereichs, der Empfindlichkeit und anderer wesentlicher Eigenschaften. Diese Verbesserungen führten zu einer beträchtlichen Steigerung des Durchsatzes des Bildgebungsprozesses, aber auf der anderen Seite begannen die Experimente eine wesentlich größere Datenmenge von bis zu Dutzenden von Terabyte zu generieren, welche anschließend manuell verarbeitet wurden. Somit ebneten diese technischen Fortschritte den Weg für die Durchführung effizienterer Hochdurchsatzexperimente zur Untersuchung einer großen Anzahl von Proben, welche Datensätze von besserer Qualität produzierten. In der wissenschaftlichen Gemeinschaft besteht daher ein hoher Bedarf an einem effizienten, automatisierten Workflow für die Röntgendatenanalyse, welcher eine solche Datenlast bewältigen und wertvolle Erkenntnisse für die Fachexperten liefern kann. Die bestehenden Lösungen für einen solchen Workflow sind nicht direkt auf Hochdurchsatzexperimente anwendbar, da sie für Ad-hoc-Szenarien im Bereich der medizinischen Bildgebung entwickelt wurden. Daher sind sie nicht für Hochdurchsatzdatenströme optimiert und auch nicht in der Lage, die hierarchische Beschaffenheit von Proben zu nutzen. Die wichtigsten Beiträge der vorliegenden Arbeit sind ein neuer automatisierter Analyse-Workflow, der für die effiziente Verarbeitung heterogener Röntgendatensätze hierarchischer Natur geeignet ist. Der entwickelte Workflow basiert auf verbesserten Methoden zur Datenvorverarbeitung, Registrierung, Lokalisierung und Segmentierung. Jede Phase eines Arbeitsablaufs, die eine Trainingsphase beinhaltet, kann automatisch feinabgestimmt werden, um die besten Hyperparameter für den spezifischen Datensatz zu finden. Für die Analyse von Faserstrukturen in Proben wurde eine neue, hochgradig parallelisierbare 3D-Orientierungsanalysemethode entwickelt, die auf einem neuartigen Konzept der emittierenden Strahlen basiert und eine präzisere morphologische Analyse ermöglicht. Alle entwickelten Methoden wurden gründlich an synthetischen Datensätzen validiert, um ihre Anwendbarkeit unter verschiedenen Abbildungsbedingungen quantitativ zu bewerten. Es wurde gezeigt, dass der Workflow in der Lage ist, eine Reihe von Datensätzen ähnlicher Art zu verarbeiten. Darüber hinaus werden die effizienten CPU/GPU-Implementierungen des entwickelten Workflows und der Methoden vorgestellt und der Gemeinschaft als Module für die Sprache Python zur Verfügung gestellt. Der entwickelte automatisierte Analyse-Workflow wurde erfolgreich für Mikro-CT-Datensätze angewandt, die in Hochdurchsatzröntgenexperimenten im Bereich der Entwicklungsbiologie und Materialwissenschaft gewonnen wurden. Insbesondere wurde dieser Arbeitsablauf für die Analyse der Medaka-Fisch-Datensätze angewandt, was eine automatisierte Segmentierung und anschließende morphologische Analyse von Gehirn, Leber, Kopfnephronen und Herz ermöglichte. Darüber hinaus wurde die entwickelte Methode der 3D-Orientierungsanalyse bei der morphologischen Analyse von Polymergerüst-Datensätzen eingesetzt, um einen Herstellungsprozess in Richtung wünschenswerter Eigenschaften zu lenken

Alternate Means of Digital Design Communication

This thesis reconceptualises communication in digital design as an integrated social and technical process. The friction in the communicative processes pertaining to digital design can be traced to the fact that current research and practice emphasise technical concerns at the expense of social aspects of design communication. With the advent of BIM (Building Information Modelling), a code model of communication (machine-to-machine) is inadequately applied to design communication. This imbalance is addressed in this thesis by using inferential models of communication to capture and frame the psychological and social aspects behind the communicative contracts between people. Three critical aspects of the communicative act have been analysed, namely (1) data representation, (2) data classification and (3) data transaction, with the help of a new digital design communication platform, Speckle, which was developed during this research project for this purpose. By virtue of an applied living laboratory context, Speckle facilitated both qualitative and quantitative comparisons against existing methodologies with data from real-world settings. Regarding data representation (1), this research finds that the communicative performance of a low-level composable object model is better than that of a complete and universal one as it enables a more dynamic process of ontological revision. This implies that current practice and research operates at an inappropriate level of abstraction. On data classification (2), this thesis shows that a curatorial object-based data sharing methodology, as opposed to the current file-based approaches, leads to increased relevancy and a reduction in noise (information without intent, or meaning). Finally, on data transaction (3), the analysis shows that an object-based data sharing methodology is technically better suited to enable communicative contracts between stakeholders. It allows for faster and more meaningful change-dependent transactions, as well as allow for the emergence of traceable communicative networks outside of the predefined exchanges of current practices