16,216 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

    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

    Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

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    This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making

    HR Analytics: Concept, Application, and Impact on Talent Management, Branding, and Challenges

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    Purpose: Making wiser decisions about employees to improve performance at the individual and/or organizational levels is the process of HR analytics. HR analytics is a method for determining the correlation between HR practices and organizational performance outcomes such as sales volume or customer satisfaction. Human Resource Analytics was established in 1978 by Jac Fitz-Enz, the pioneer of human capital strategic analysis and performance benchmarking. In this paper, the researcher wants to discuss the concept of HR analytics, its application, impact on talent management, branding, and challenges in its application.Design/methodology/approach: The researcher examines secondary data and conducts a thorough literature review to understand the concept and its application across industries and nations, as well as to identify any challenges encountered during deployment and any benefits perceived by various industry professionals. Findings: The study's findings indicate that using HR analytics can help businesses build their brand and gain a competitive edge in today's fiercely competitive business environment while also enhancing workforce and employee productivity.Originality/value: This study has significant implications for both literature and HR analytics. Researchers will know more about the factors that contribute to and the mechanisms by which HR analytics improve organisational performance. The author's second claim is that having access to HR technology both facilitates and precedes HR analytics. Finally, concrete data from the literature demonstrates its influence on branding and organisational success. Keywords: Human resource (HR) analytics, People analytics, Branding, Talent Management, Organizational performance. Paper type: Research paper JEL Code: M12, M15 & M51 DOI: 10.7176/EJBM/15-8-06 Publication date: April 30th 202

    The impact of innovative technologies in construction activities on concrete debris recycling in China : a system dynamics-based analysis

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    As construction activities become more intensive in developing countries, increasing improperly managed construction and demolition waste (CDW) brings serious environmental impacts. Recycling is a beneficial way to dispose of CDW that reduces environmental impact and brings economic benefits, especially for concrete. China is the country that generates the most CDW in the world, but its domestic recycling rate is much lower than that of developed countries. While the efficient technologies in developed regions have helped them to achieve a well-established recycling industry, whether these innovative technologies can be used to improve the concrete debris recycling targets in developing regions is unclear. This study examines whether innovations currently widely used in construction activities and materials can have a positive effect on the recycling of End-of-Life concrete materials in China. Results from modeling system dynamics imply that the introduction of innovative technologies in the recycling system of concrete debris can probably contribute to CO2 reduction (3.6% reduction) and economic benefits (2.6 times increase, but mainly from landfill charges and fines) from 2022 to 2030. Prefabrication and 3D printing significantly impact recycled concrete production and CDW recycling, and they are recommended as a priority for promotion. In contrast, carbonation is not suggested for application due to its minor role. Nevertheless, since the market share of innovative technologies and the basic CDW recycling rates are currently low in China, fluctuations in their usage are hardly to have a substantial positive impact. We suggest that financial support from the government is needed for upcycling by recyclers and technology providers to improve the base recycling rate in order for innovative technologies to make an effective contribution to the sustainable construction industry, creating a win–win situation for both the economy and the environment of the recycling system

    "Just old age" - a qualitative investigation of owner and veterinary professional experiences of and attitudes to ageing in dogs in the UK.

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    ObjectivesMany UK dogs live into old age, but owners may not recognise or report age-associated signs of disease which lead to negative welfare. This study investigated dog owner and veterinary professional experiences and attitudes towards ageing in dogs, how health care is offered, barriers to its delivery, and some best-practice solutions.Materials and methodsIn-depth semi-structured interviews were conducted with 15 owners of 21 dogs (aged 8 to 17 years mean: 13) and 11 veterinary professional (eight veterinary surgeons, two nurses and one physiotherapist). Open-text responses from 61 dog owner were collected using an online survey. Transcripts and survey responses were inductively coded into themes.ResultsFour themes were constructed: "just old age", barriers to care, trust in veterinary surgeons, and tools to improve health care. Age-related changes were mostly perceived as "just old age" by dog owner. Many dogs were no longer vaccinated and did not attend check-ups unless owners identified a problem. The greatest barriers to health care were finances (dog owner), owner awareness, willingness to act and consultation time (veterinary professional). Trust in veterinary professional was more likely when dog owner experienced continuity, prioritisation of care, clear communication and an accessible, knowledgeable and empathic veterinary professional. Participants suggested that senior health care and communication between dog owner and veterinary professional could be improved through questionnaires, and evidence-based online information.Clinical significanceOpportunities to educate owners on which clinical signs represent healthy or pathological ageing are being missed. Resources should be developed to guide on best-practice discussions in consultations, encourage more owners to recognise clinical signs and to seek and trust veterinary advice

    Perceptions of surveillance: exploring feelings held by Black community leaders in Boston toward camera enforcement of roadway infractions

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    Roadway camera enforcement programs have been found to effectively reduce vehicle travel speeds, as well as decrease the number and severity of collisions. Despite a wealth of evaluative research confirming this enforcement approach's aptitude at promoting safer roadway behavior, fewer than 50 % of US states currently host camera-based programs. Public opposition is frequently cited as the cause for the slow proliferation of this enforcement strategy. However, with public demand for police reform having an increasing presence on the national political stage, how might feelings toward camera technology currently stand among groups most marginalized by existing enforcement systems, and how might those feelings vary by type of enforcement application? Through a series of focus groups, this work centers Black voices on matters of surveillance and roadway enforcement by discussing sentiment toward camera programs with Black community leaders. This discussion is contextually situated in Boston, Massachusetts, where legislation that would allow for camera enforcement of roadway infractions is actively being deliberated in the State Senate. Findings culminate in a list of right-sizing and procedural recommendations for policy makers hoping to gain support for camera enforcement, improve roadway safety, and advance racial equity in our systems of policing and governance

    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

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries
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