1,720,981 research outputs found
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Partitioning and placement for cooperative 3D printing
No full textThis thesis proposes novel approaches to fill in several gaps in the emerging Additive Manufacturing (AM) process of Cooperative 3D Printing (C3DP). C3DP is a rudimentary form of Swarm Manufacturing (SM), where mobile 3D printing robots can move around the factory floor and collaborate in the manufacturing environment. Several C3DP sub-processes, such as geometric partitioning for division of labor between the different robots, scheduling of tasks, and path planning of mobile robots, have been partially or fully explored in previous work; however, gaps remain in the process. These gaps include the inability to print tall objects with the current geometric partitioning method, a lack of automation for placing jobs on the factory floor, and a compromised part strength due to geometric partitioning. These must be addressed before C3DP is a viable manufacturing process for a wide variety of projects. To address these gaps, this thesis asks three fundamental research questions: 1) How can the geometric partitioning process be expanded to enable the printing of tall objects? 2) What is the optimal placement of jobs on the factory floor in a multi-print-job context? 3) How can part strength be maintained despite partitioning of the part while increasing the printing speed? To answer the first question, we propose a new Z-Chunking strategy to divide tall projects into multiple, printable jobs that can be further partitioned with the existing methodology for printing. Additionally, we automatically generate Assembly Geometry to facilitate the reconnection of the printed jobs. To show the viability of this approach, we conduct a study in which we utilize the new strategy to autonomously chunk several tall parts and perform physical printing to ensure the feasibility of reassembly. To address the second question, we develop a job placement optimization algorithm that takes into account partitioning, job structure, and the number of robots to minimize makespan. We conduct several simulations in a study to show the efficacy of the algorithm and test the impact of factors such as the number of jobs and robots, among others. For the third question, we develop a novel approach to the generation of space-filling internal cells that validates bonding strength and enables collision-free cooperation of multiple toolheads in the same workspace. By answering these questions, the gaps in the C3DP process are successfully addressed. This thesis improves the state of the art in C3DP and enables it to be successfully used as a manufacturing technique for a wide range of projects. This thesis work also expands the field of SM and brings even more flexibility to AM as a whole.Mechanical Engineerin
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Optical motion tracking integration in mobile robot-enabled manufacturing
Get PDFThis report presents the integration of an OptiTrack motion tracking system into a mobile robot-enabled manufacturing environment. The mobile robots consist of ClearPath Robotics' Jackal UGV mobile base, with Universal Robotics' UR3e 7-DOF robot arm mounted on top. The goal of this project overall is to facilitate real-time implementation of motion tracking into the robot-enabled manufacturing environment, with an eventual goal of collision detection and avoidance procedures for mobile robot-enabled manufacturing. The primary contribution of this study is a robust method of streaming positional data in real-time from the OptiTrack System, through the OptiTrack Software on a Windows system, into a Linux-based system used to control robotic behavior. This integration is expected to increase the efficiency and autonomy of robots in a manufacturing environment, with collision avoidance capabilities implemented place to avoid any failures. This report covers the preparatory knowledge and system setup completed in order to prepare the manufacturing environment for testing. It details the setup methodology and calibration of all three systems, integration challenges and solutions, and presents a series of calibration/integration confirmation case studies performed along the way that demonstrate the system's effectiveness. The studies performed evaluated the OptiTrack system’s tracking capabilities, demonstrating high accuracy within the central zone of the testing environment and a significant decrease of approximately 60% in accuracy as the mobile robot approached the testing environment’s boundaries. Additionally, this study showed that with an average software latency of about 1ms, the system is suitable for real-time applications. Through the meticulous documentation of the setup, calibration, and integration process alongside a series of case studies, this report aims to highlight the potential of optical motion tracking in transforming manufacturing environments into highly efficient, autonomous systems. The research done encapsulates the complexity of synchronizing physical movements with digital precision. Recognizing the system’s limited tracking accuracy near boundaries and its challenges with complex rigid body geometries, future work will focus on environmental adaptations to mitigate potential interferences, the inclusion of 3D models for established rigid bodies, and an increase in stability of the mobile robot configuraton. These steps aim to address the system’s current limitations, improving its performance for the subsequent phases of this project.Mechanical Engineerin
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Physical validation and monitoring of cooperative 3D printing
Get PDFSwarm Manufacturing (SM) is a novel manufacturing paradigm that utilizes a swarm of mobile robots to complete manufacturing tasks in a factory setting. Cooperative 3D Printing (C3DP) is a rudimentary form of SM that utilizes mobile 3D printing robots working in tandem to print large objects. Several parts of the C3DP pipeline have been explored in previous work, such as geometric partitioning, scheduling, path planning, and placement. While this pipeline has been thoroughly developed, gaps still exist regarding the validation and monitoring of various steps along the process in order to ensure the computational pipeline can be operated safely in the real world. This thesis proposes a physical validation study and process monitoring integration for the emerging additive manufacturing (AM) technology of cooperative 3D printing (C3DP). To address these research gaps, we pose a few research questions that are explored over the course of this thesis: 1) Can the existing placement and scheduling algorithms be physically validated? If so, to what extent can they be validated and what changes should be made to these algorithms to improve their functionality when being physically implemented? 2) How can the C3DP process be monitored to further improve its implementation and account for uncertainties and errors in manufacturing? To answer the first question, this thesis presents a physical validation study of the previously developed placement optimization algorithm. This is done using two test cases which are analyzed to determine what changes must be made to the algorithm to make it better represent the physical C3DP platform. The second question will be addressed with a thorough review of various process monitoring techniques for both object tracking and 3D printing error detection. The latter will be expanded further, with both 2D and 3D vision techniques being tested and integrated to detect errors in parts manufactured using C3DP. Between these two studies, numerous gaps in the C3DP process will be filled in, specifically relating to the autonomy of the physical system. In doing so, this thesis expands on the concept of SM, and brings more flexibility to the manufacturing field as a whole.Mechanical Engineerin
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Socio-technical systems engineering and design : a meso-level network-based approach
Get PDFDifferent from traditional engineering systems design, which is keen on the design and optimization of technical artifacts, the design of socio-technical systems (STS) is guided by fundamentally understanding the complex interactions between social and technical aspects. This has posed significant challenges when applying existing systems engineering (SE) and design approaches to STS. For example, classical top-down design methodologies, such as the Waterfall model and the SE Vee model, are not appropriate for the engineering and design of large-scale STS with spontaneous interactions among individual entities or components. Although existing bottom-up design approaches are adaptable to the system scale, they primarily focus on understanding the behaviors and interactions between individual entities at the micro-level and their impact on system performance at the macro-level. In my dissertation research, the central hypothesis is that subsystems at the meso level (e.g., small clusters of individual entities) serve as critical links in system structures and could influence both macro-level performance and micro-level interactions, and thus deserve scientific investigation in STS engineering and design. However, there is a knowledge gap in understanding what meaningful subsystem information at the meso-level is and how it can be extracted and used to guide the design of an STS system to achieve the desired system performance. To fill this gap, my research objective is to develop a novel meso-level network-based framework for STS engineering and design. This dissertation is driven by answering three research questions: 1) RQ1 : How can significant meso-level system structures be identified? 2) RQ2 : What are the influences of the significant meso-level subsystems on the system performance at the macro level and the interaction mechanism at the micro level? 3) RQ3 : How can meso-level structural information be used to design an STS to achieve desired macro-level performance and micro-level functionality? The methodologies proposed to address these questions are validated through two case studies: shared mobility systems and customer-product market systems. For shared mobility systems, a network motif-based robust design framework is proposed to improve the robustness and resilience of socio-technical systems against seasonal effects. Within this framework, trip motif mining addresses RQ1, while trip motif-based system robustness metrics tackle RQ2. Formulating and solving optimization problems serves to address RQ3. Additionally, a graph neural network-based (GNN-based) link prediction (LP) model is introduced to support STS design decision-making and validation. The GNN-based model leverages local network information to enhance prediction accuracy, addressing RQ2, while implementing the LP model for design strategy validation contributes to addressing RQ3. In the context of customer-product market systems, a socio-technical system data collection framework integrating information retrieval and survey design methods is proposed to tackle the data scarcity issue in STSs. Furthermore, a novel micro-level entity design framework of STS, considering meso-level dependencies, is proposed, marking the first attempt to solve the inverse problem. This framework contributes to addressing RQ1, RQ2, and RQ3 by incorporating network motif mining, quantification of subsystem-based individual entity functionality, and entity optimization design within a unified framework. Lastly, a preliminary exploration of meso-level temporal network motifs in STS is conducted, encompassing solutions to the dynamic data scarcity issue, dynamic network modeling, and significant temporal subnetwork mining and empirical interpretation. This exploration contributes to answering RQ1 and RQ2 when considering the time dimension. Regarding the key findings and conclusions of this dissertation, we first show the effectiveness of combining information retrieval and survey design to tackle the data accessibility challenge in STS network data. Additionally, our survey study, for the first time, gathered customers' social network data alongside their purchase decision-making data, aiding in the examination of social factors influencing customers' decision-making. Moreover, while survey studies are time-consuming, leveraging named entity recognition (NER) models for mining online text data offers a viable alternative for supporting entity relationship data collection. Then, when working on the shared mobility system case study, we find that: 1) An STS's seasonal sensitivity is closely tied to imbalanced capacity planning within its subsystems. Therefore, balancing the capacity of meso-level service systems is beneficial to enhancing STS robustness against seasonal demand fluctuations; 2) The outperformance of the GNN-based predictive model, which incorporates local network information, compared to a simple neural network model lacking such consideration, demonstrates the importance of local network information in demand prediction between stations in shared mobility networks. Moreover, this outperformance persists even when network structures and density change significantly. Next, in the study of design for customer-product systems, the inter-brand triadic competition closure competition, where three products from different brands form a closed triangle competition, emerges as a significant pattern in the vacuum cleaner market system. Identifying these meso-level patterns offers a means to quantify product competitiveness. Integrating this information with network predictive models and metaheuristic approaches, like the genetic algorithm, facilitates the inclusion of local competition data in the product design process. In the study of STS dynamic analysis, we demonstrate that increasing undersampling ratios improves predictive performance, particularly in moderately imbalanced systems, enhancing the GNN-based LP model. However, in extremely imbalanced systems, a tuning process is necessary to balance computational efficiency and model performance, with the threshold-based postprocessing method consistently outperforming the rank-based method. Additionally, six temporal competition motifs are interpreted, aiding in tracking market system dynamics. In summary, my dissertation contributes to the systems science literature by introducing a novel meso-level network-based framework for STS engineering and design, thus addressing the knowledge gap pertaining to the identification and interpretation of statistically significant subsystem structures (i.e., meso-level structures formed within a complex system) and the use of such structures for STS engineering and design. The findings presented herein shed light on the importance of treating significant subsystems as crucial functional units and building blocks of STSs and underscore the need to consider them in both macro-level system design and micro-level individual entity design for optimizing system performance and entity functionality. Beyond enriching systems science from the meso-level subsystem perspective, this dissertation is expected to generate broader impacts in: 1) Addressing imbalanced source allocations in societal infrastructure systems, such as uneven distribution of public resources in urban areas. By treating local communities as meso-level subsystems and utilizing their information, this research offers policymakers actionable insights for more efficient resource distribution; 2) showing the potential to inform robust design strategies for large networked physical systems like power grids and transportation networks, the meso-level subsystem-based approach facilitates the identification of critical functional units within these systems. Subsequently, system optimization design can be guided by preserving the functionality of these identified subsystems. 3) enhancing interdisciplinary collaboration between engineering and social sciences. The frameworks proposed in this dissertation are extensible to incorporate societal analytical models. For example, in the case study of customer-product market systems, a more advanced network model that integrates customer social networks into the proposed product competition network can be easily generated to support a more in-depth analysis. By bridging the gap between technical systems engineering and social aspects, it fosters a holistic approach to addressing complex societal challenges.Mechanical Engineerin
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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A closed-loop in-situ monitoring framework for cooperative 3D printing using edge detection and image augmentation
No full textCooperative 3D Printing (C3DP) is an emerging area within Swarm Manufacturing (SM) that enables large-format parts to be printed in parallel by multiple mobile robotic arms. This improves efficiency without compromising print quality. However, as C3DP scales, print failures become more likely, and there is a growing need for reliable in-situ monitoring and closed-loop control. In our previous work, we developed a monitoring framework that used image-matching and machine learning to detect stringing and other surface-level defects in fused deposition modeling (FDM). While promising, the system lacked the precision and consistency needed for closed loop integration. This thesis presents an improved in-situ monitoring system that addresses those gaps. First, we retrain the stringing detection model using YOLOv8, an image detection training framework, and a dataset expanded through image augmentation, improving performance and robustness across different camera angles and lighting conditions. Second, we integrate Canny edge detection to improve image matching and enable the detection of geometric defects like warping, layer splitting, and interstitial gaps. We evaluate four edge-based comparison methods and find that corner point analysis provides the most consistent results for detecting and quantifying warping severity and between-layer splitting. Using this data, we implement an in-situ closed-loop feedback system that can adjust bed temperature or halt the print in response to real-time errors. Together, these enhancements create a vision-based monitoring framework that improves the accuracy and reliability of defect detection in C3DP. The results show that with the right combination of image augmentation and edge detection, C3DP systems can begin to operate autonomously with real-time correction and minimal human oversight. The contributions of this thesis are threefold: (1) a retrained YOLOv8-based stringing detection model with improved generalization through dataset augmentation, (2) the development and evaluation of four edge-based comparison methods for detecting warping, layer splitting, and interstitial gaps, and (3) the implementation of a real-time closed-loop feedback system that actively responds to detected defects by adjusting print parameters. These contributions move C3DP toward autonomous, scalable manufacturing with built-in defect correction.Mechanical Engineerin
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Towards human-centered generative design : cross-modal synthesis for three-dimensional design concept generation
No full textThere has been a fast-growing interest in generative design (GD) — an artificial intelligence (AI) — based approach for early-stage design that automates the creation and optimization of various design concepts. GD can quickly generate numerous design variants, allowing for efficient exploration of design space, and thus can shorten the product design cycle and reduce development costs. Despite advances in GD technologies, current GD approaches place AI at the center of the design process and lack direct involvement of human preference and judgment in the concept generation process. This raises significant concerns, as current GD methods lack mechanisms that incorporate human factors, such as aesthetic preferences, and the safety and comfort of designs. This omission could lead to the creation of design concepts that fail to meet human needs effectively. In contrast, human designers bring their expertise and knowledge to generate human-centered designs, playing an irreplaceable role in the design process. This is especially true in the early stages, where human domain knowledge and preferences are crucial in determining the potential to produce user-centered and user-friendly products. These elements critically influence the success of product design and development. To fill this research gap, the overarching goal is to realize human-centered generational design. Towards that goal, the research objective of my dissertation is to develop a human-supervised data-driven generative design framework that can actively keep humans in the loop, as well as in charge of the generation and evaluation of AI-assisted design concepts in early-stage design. In particular, my research is motivated to answer the following central research question: In what ways and to what extent can human designers’ intent and preferences be incorporated as input to actively interact and guide the GD process to improve the quality and relevance of the design outcomes? The central research hypothesis is that human designers' intent and preferences can be incorporated as input to guide the data-driven design generation using cross-modal synthesis. Cross-modal synthesis is a machine learning technique that can generate data in one modality (such as images) based on another input modality. Our rationale is that human designers can use different design modalities (such as sketches of car models) or a combination of them to represent their intent and preferences (e.g., certain curvatures in a car's exterior design), and cross-modal synthesis methods can automatically transform them to desired design modalities, such as 3D CAD models. However, the development of cross-modal synthesis methods for engineering design needs to address several fundamental challenges. These challenges include the scarcity of design data, complexities in 3D design representations, large semantic gaps between different modalities (challenging to maintain design integrity and intent embedded in the input design modality), and vectorized design representations for AI training. To test the central hypothesis, we aim to answer three Research Questions (RQs). (1) How feasible and to what extent can cross-modal synthesis methods with unimodal input incorporate human designers' intent and preferences as input to guide the data-driven design generation? (2) How feasible and to what extent can cross-modal synthesis methods with multimodal inputs incorporate human designers' intent and preferences as input to guide the data-driven design generation? (3) What are the effects of different representations of the generated designs on the data-driven design evaluation? To address these RQs, we explore various methodologies. For RQ 1, we conduct a systematic review of deep learning methods for cross-modal tasks, from which we identify technology on how to develop cross-modal synthesis methods for engineering design. We then develop a novel neural network architecture, target embedding variational autoencoders, based on which we create two cross-modal synthesis methods with unimodal input for the task of (1) silhouette contour sketch to 3D mesh and (2) image to CAD sequence. In response to RQ 2, we propose a multimodal CAD dataset to enable and evaluate large language models' ability to generate CAD models from multimodal inputs (i.e., textual descriptions, sketches, and images). Lastly, RQ 3 is answered by developing a data-driven structure-aware generative design and evaluation approach and examining vectorized design representations to improve the assessment of generated designs. From the results, we conclude that: (1) Cross-modal synthesis methods, capable of processing either unimodal or multimodal inputs, exhibit significant potential in capturing and integrating human designers' intent and preferences to guide the generation of early-stage design concepts in 3D representations. Although textual descriptions, sketches, and images may not fully encapsulate the designers' envisioned ideas—a challenge also prevalent in traditional design practices—our cross-modal synthesis approaches can still discern and interpret the underlying design preferences from these varied input modalities. Consequently, these methods can generate 3D designs that are closely aligned with the specified design requirements embedded in the input design modalities, thus bridging the gap between conceptual intent and tangible design artifacts. (2) The choice of mathematical design representations (e.g., vectors of design features) significantly influences the evaluation of generated designs in design performance prediction. Such influences are particularly significant when product geometries become more intricate and when dealing with systems design generation where complex interdependent relations between components exist. Although latent spaces are commonly utilized for these vectorized representations to accelerate AI-assisted design evaluation and optimization, such latent vectors may prove unsuitable if any information irrelevant to the engineering performance of interest is encapsulated during the formulation of design representations, consciously or unconsciously. This observation underscores the imperative for designers to consider the suitability of vectorized design representations for evaluation purposes from the beginning of developing DGD methodologies. In summary, this dissertation represents a critical step forward in human-centered generative design. It advances the design field by addressing a crucial gap in existing GD methodologies, facilitating a human-centered GD approach. Specifically, we introduce a novel Human-Supervised Data-Driven Generative Design Framework with cross-modal synthesis methods for design generation and AI-assisted design evaluation methods, which enhance human control and interaction within the GD process. Notably, this work develops an innovative neural network architecture tailored for cross-modal synthesis in engineering design. This architecture is particularly adept at incorporating human intent and preferences into the GD of 3D design concepts. The proposed methodologies can significantly accelerate design ideation, enhance the exploration of design spaces, and incorporate downstream considerations into early-stage decision-making. The methodologies are domain-independent and can be employed across different products in industries to expedite the product development cycle and decrease associated costs. Furthermore, the methods have the potential to be translated into pedagogical tools in design education, preparing next-generation engineers for future careers in an evolving landscape that increasingly values human-AI collaboration in engineering design.Mechanical Engineerin
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