12,531 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Sensitivity analysis for ReaxFF reparameterization using the Hilbert-Schmidt independence criterion

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    We apply a global sensitivity method, the Hilbert-Schmidt independence criterion (HSIC), to the reparameterization of a Zn/S/H ReaxFF force field to identify the most appropriate parameters for reparameterization. Parameter selection remains a challenge in this context as high dimensional optimizations are prone to overfitting and take a long time, but selecting too few parameters leads to poor quality force fields. We show that the HSIC correctly and quickly identifies the most sensitive parameters, and that optimizations done using a small number of sensitive parameters outperform those done using a higher dimensional reasonable-user parameter selection. Optimizations using only sensitive parameters: 1) converge faster, 2) have loss values comparable to those found with the naive selection, 3) have similar accuracy in validation tests, and 4) do not suffer from problems of overfitting. We demonstrate that an HSIC global sensitivity is a cheap optimization pre-processing step that has both qualitative and quantitative benefits which can substantially simplify and speedup ReaxFF reparameterizations.Comment: author accepted manuscrip

    Technical Dimensions of Programming Systems

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    Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art. In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too. We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them. We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions. Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems. Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants

    Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio

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    This paper reviews the most common situations where one or more regularity conditions which underlie classical likelihood-based parametric inference fail. We identify three main classes of problems: boundary problems, indeterminate parameter problems -- which include non-identifiable parameters and singular information matrices -- and change-point problems. The review focuses on the large-sample properties of the likelihood ratio statistic. We emphasize analytical solutions and acknowledge software implementations where available. We furthermore give summary insight about the possible tools to derivate the key results. Other approaches to hypothesis testing and connections to estimation are listed in the annotated bibliography of the Supplementary Material

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

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    In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods, whom performances are maximised using hyperparameter optimization techniques. The paper proposes a semi-supervised method with a data-driven approach to detect structural anomalies. The methodology consists of: (i) a Variational Autoencoder (VAE) to approximate undamaged data distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage sensitive features extracted from VAE's signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage's scenarios by IASC-ASCE Structural Health Monitoring Task Group

    neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time

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    IntroductionResearch in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even for larger networks with natural density, biologically plausible multi-compartment models and the modeling of long-term and structural plasticity.MethodsStressing the need for agility to adapt to new concepts or findings in the domain of neuroscience, we have developed the neuroAIx-Framework consisting of an empirical modeling tool, a virtual prototype, and a cluster of FPGA boards. This framework is designed to support and accelerate the continuous development of such platforms driven by new insights in neuroscience.ResultsBased on design space explorations using this framework, we devised and realized an FPGA cluster consisting of 35 NetFPGA SUME boards.DiscussionThis system functions as an evaluation platform for our framework. At the same time, it resulted in a fully deterministic neuroscience simulation system surpassing the state of the art in both performance and energy efficiency. It is capable of simulating the microcircuit with 20× acceleration compared to biological real-time and achieves an energy efficiency of 48nJ per synaptic event

    OLIG2 neural progenitor cell development and fate in Down syndrome

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    Down syndrome (DS) is caused by triplication of human chromosome 21 (HSA21) and is the most common genetic form of intellectual disability. It is unknown precisely how triplication of HSA21 results in the intellectual disability, but it is thought that the global transcriptional dysregulation caused by trisomy 21 perturbs multiple aspects of neurodevelopment that cumulatively contribute to its etiology. While the characteristics associated with DS can arise from any of the genes triplicated on HSA21, in this work we focus on oligodendrocyte transcription factor 2 (OLIG2). The progeny of neural progenitor cells (NPCs) expressing OLIG2 are likely to be involved in many of the cellular changes underlying the intellectual disability in DS. To explore the fate of OLIG2+ neural progenitors, we took advantage of two distinct models of DS, the Ts65Dn mouse model and induced pluripotent stem cells (iPSCs) derived from individuals with DS. Our results from these two systems identified multiple perturbations in development in the cellular progeny of OLIG2+ NPCs. In Ts65Dn, we identified alterations in neurons and glia derived from the OLIG2 expressing progenitor domain in the ventral spinal cord. There were significant differences in the number of motor neurons and interneurons present in the trisomic lumbar spinal cord depending on age of the animal pointing both to a neurodevelopment and a neurodegeneration phenotype in the Ts65Dn mice. Of particular note, we identified changes in oligodendrocyte (OL) maturation in the trisomic mice that are dependent on spatial location and developmental origin. In the dorsal corticospinal tract, there were significantly fewer mature OLs in the trisomic mice, and in the lateral funiculus we observed the opposite phenotype with more mature OLs being present in the trisomic animals. We then transitioned our studies into iPSCs where we were able to pattern OLIG2+ NPCs to either a spinal cord-like or a brain-like identity and study the OL lineage that differentiated from each progenitor pool. Similar to the region-specific dysregulation found in the Ts65Dn spinal cord, we identified perturbations in trisomic OLs that were dependent on whether the NPCs had been patterned to a brain-like or spinal cord-like fate. In the spinal cord-like NPCs, there was no difference in the proportion of cells expressing either OLIG2 or NKX2.2, the two transcription factors whose co-expression is essential for OL differentiation. Conversely, in the brain-like NPCs, there was a significant increase in OLIG2+ cells in the trisomic culture and a decrease in NKX2.2 mRNA expression. We identified a sonic hedgehog (SHH) signaling based mechanism underlying these changes in OLIG2 and NKX2.2 expression in the brain-like NPCs and normalized the proportion of trisomic cells expressing the transcription factors to euploid levels by modulating the activity of the SHH pathway. Finally, we continued the differentiation of the brain-like and spinal cord-like NPCs to committed OL precursor cells (OPCs) and allowed them to mature. We identified an increase in OPC production in the spinal cord-like trisomic culture which was not present in the brain-like OPCs. Conversely, we identified a maturation deficit in the brain-like trisomic OLs that was not present in the spinal cord-like OPCs. These results underscore the importance of regional patterning in characterizing changes in cell differentiation and fate in DS. Together, the findings presented in this work contribute to the understanding of the cellular and molecular etiology of the intellectual disability in DS and in particular the contribution of cells differentiated from OLIG2+ progenitors
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