21,872 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

    Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images

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    Currently, analysis of microscopic In Situ Hybridization images is done manually by experts. Precise evaluation and classification of such microscopic images can ease experts' work and reveal further insights about the data. In this work, we propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders. The model's performance is optimized by balancing the latent layers' length and complexity and fine-tuning hyperparameters. The results are validated by adapting the mean-squared error (MSE) metric, and comparison to expert's evaluation.Comment: 9 pages; 9 figure

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

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    Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

    Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning

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    Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.Comment: DDDAS 202

    Influencia del design thinking para el modelo de negocios de la Empresa Byte Soluciones S.A.C. - Arequipa, 2022

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    El presente trabajo de investigación posee como motivación principal de estudio: Determinar la influencia del Design Thinking en el modelo de negocios de la empresa Byte Soluciones S.A.C. - Arequipa, 2022, siendo definidos los objetivos específicos a partir de las fases del Design Thinking promulgadas por Tim Brown, creador de este método: determinar el nivel de influencia de la fase de empatizar para el modelo de negocios de la empresa Byte Soluciones S.A.C. - Arequipa, 2022, determinar el nivel de influencia de la fase de definir para el modelo de negocios de la empresa Byte Soluciones S.A.C. - Arequipa, 2022, determinar el nivel de influencia de la fase de idear para el modelo de negocios de la empresa Byte Soluciones S.A.C. - Arequipa, 2022, determinar el nivel de influencia de la fase de prototipar para el modelo de negocios de la empresa Byte Soluciones S.A.C. - Arequipa, 2022 y determinar el nivel de influencia de la fase de evaluar para el modelo de negocios de la empresa Byte Soluciones S.A.C. - Arequipa, 2022. La ejecución del presente trabajo de investigación se desarrolló utilizando el método científico a modo de base, siendo una investigación del tipo básica, teniendo un nivel de investigación correlacional, bajo el diseño de no experimental. La presente investigación se realizó a una población total de 24 colaboradores de la empresa Byte Soluciones S.A.C. de la ciudad de Arequipa, así como, a 65 empresas - clientes de Byte Soluciones S.A.C. de los rubros de venta al por menor/mayor, servicios, transporte, restaurantes, courier y sitios web/comercio electrónico quienes cumplen los criterios de inclusión como: servicios a tiempo determinado, servicios a tiempo indeterminado y alianzas estratégicas, a quienes se les aplicaron los instrumentos de investigación: entrevista para la investigación cualitativa y cuestionario para la investigación cuantitativa, tanto para conocer el estado actual de la empresa y la validación de la propuesta, el procesamiento de los datos ser realizó utilizando los programas estadísticos SPSS y Microsoft Excel obteniéndose los resultados entre las variables de estudio. xv A la conclusión del presente trabajo de investigación, se puede indicar que la influencia del Design Thinking en el modelo de negocios de la empresa Byte Soluciones S.A.C. es positiva, debido a que los encuestados se encontraron totalmente de acuerdo en 66,30%, de acuerdo en 25,80%, en desacuerdo en 5,60% y totalmente en desacuerdo en 2,20% respecto a que las necesidades de los clientes han sido empatizadas por la empresa Byte Soluciones S.A.C., además, se encontraron totalmente de acuerdo en 64,00%, de acuerdo en 24,7%, en desacuerdo en 5,60% y totalmente en desacuerdo en 5,60% respecto a que el catálogo de productos y servicios de la empresa Byte Soluciones S.A.C. brindan valor a los requerimientos de los clientes, también, se encontraron totalmente de acuerdo en 65,20%, de acuerdo en 23,60%, en desacuerdo en 9,00% y totalmente en desacuerdo en 2,20% respecto a que el catálogo de productos y servicios de la empresa Byte Soluciones S.A.C. brinda soluciones innovadoras, respecto a que el producto mínimo viable del catálogo de productos y servicios de la empresa Byte Soluciones S.A.C. es atractivo para los clientes, se encontraron totalmente de acuerdo en 68,50%, de acuerdo en 24,70%, en desacuerdo en 5,60% y totalmente en desacuerdo en 1,10% y respecto a que el catálogo de productos y servicios de la empresa Byte Soluciones S.A.C. genera satisfacción al cliente, se encontraron totalmente de acuerdo en 62,90%, de acuerdo en 29,20%, en desacuerdo en 6,70% y totalmente en desacuerdo en 1,10%. A partir del cálculo de la prueba de hipótesis realizado en el SPSS con un intervalo de confianza del 95% (0.95) el nivel de significancia es mayor en la comprobación con cada uno de los pares formados por las preguntas realizadas a la muestra, aceptándose que el Design Thinking desde las fases de empatizar, definir, idear, prototipar y evaluar aportan positivamente en el modelo de negocios de la empresa Byte Soluciones S.A.C

    ENABLING EFFICIENT FLEET COMPOSITION SELECTION THROUGH THE DEVELOPMENT OF A RANK HEURISTIC FOR A BRANCH AND BOUND METHOD

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    In the foreseeable future, autonomous mobile robots (AMRs) will become a key enabler for increasing productivity and flexibility in material handling in warehousing facilities, distribution centers and manufacturing systems. The objective of this research is to develop and validate parametric models of AMRs, develop ranking heuristic using a physics-based algorithm within the framework of the Branch and Bound method, integrate the ranking algorithm into a Fleet Composition Optimization (FCO) tool, and finally conduct simulations under various scenarios to verify the suitability and robustness of the developed tool in a factory equipped with AMRs. Kinematic-based equations are used for computing both energy and time consumption. Multivariate linear regression, a data-driven method, is used for designing the ranking heuristic. The results indicate that the unique physical structures and parameters of each robot are the main factors contributing to differences in energy and time consumption. improvement on reducing computation time was achieved by comparing heuristic-based search and non-heuristic-based search. This research is expected to significantly improve the current nested fleet composition optimization tool by reducing computation time without sacrificing optimality. From a practical perspective, greater efficiency in reducing energy and time costs can be achieved.Ford Motor CompanyNo embargoAcademic Major: Aerospace Engineerin

    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
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