Feature-based Product Modelling in a Collaborative Environment

Ph.DDOCTOR OF PHILOSOPH

Proto-fuse project

This chapter, via two experiments, focuses on proving the hypothesis with empirical evidences. Two separate experiments were conducted under the title: The Proto-fuse project. In each of these experiments the following two concepts and their correlation with creativity have been addressed: 1- Conceptual blending 2- Tolerance of ambiguity The experiments firstly aim to identify the relationship between conceptual blending and navigating UVEs and secondly aim to identify the importance of tolerances of ambiguity in the discipline of architecture and engineering. The empirical evidences are published in the fourth journal article: “The Proto-Fuse project: methods to boost creativity for architects”, International Journal of Design Creativity and Innovation, Taylor & Francis publisher, pp. 1-16

Collision Detection and Merging of Deformable B-Spline Surfaces in Virtual Reality Environment

This thesis presents a computational framework for representing, manipulating and merging rigid and deformable freeform objects in virtual reality (VR) environment. The core algorithms for collision detection, merging, and physics-based modeling used within this framework assume that all 3D deformable objects are B-spline surfaces. The interactive design tool can be represented as a B-spline surface, an implicit surface or a point, to allow the user a variety of rigid or deformable tools. The collision detection system utilizes the fact that the blending matrices used to discretize the B-spline surface are independent of the position of the control points and, therefore, can be pre-calculated. Complex B-spline surfaces can be generated by merging various B-spline surface patches using the B-spline surface patches merging algorithm presented in this thesis. Finally, the physics-based modeling system uses the mass-spring representation to determine the deformation and the reaction force values provided to the user. This helps to simulate realistic material behaviour of the model and assist the user in validating the design before performing extensive product detailing or finite element analysis using commercially available CAD software. The novelty of the proposed method stems from the pre-calculated blending matrices used to generate the points for graphical rendering, collision detection, merging of B-spline patches, and nodes for the mass spring system. This approach reduces computational time by avoiding the need to solve complex equations for blending functions of B-splines and perform the inversion of large matrices. This alternative approach to the mechanical concept design will also help to do away with the need to build prototypes for conceptualization and preliminary validation of the idea thereby reducing the time and cost of concept design phase and the wastage of resources

Applied Machine Learning in Extrusion-Based Bioprinting

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping other experimental parameters constant. Also, models trained across data from general literature were compared to models trained across data from one literature source that utilized alginate and gelatin bioinks and experimental conditions closely replicatable with available laboratory resources. The results indicate that models trained on large amounts of generalized data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predicted cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of around 70%, the cell viability predictions remained constant despite altering input parameter combinations. Trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design

Integration of e-business strategy for multi-lifecycle production systems

Internet use has grown exponentially on the last few years becoming a global communication and business resource. Internet-based business, or e-Business will truly affect every sector of the economy in ways that today we can only imagine. The manufacturing sector will be at the forefront of this change. This doctoral dissertation provides a scientific framework and a set of novel decision support tools for evaluating, modeling, and optimizing the overall performance of e-Business integrated multi-lifecycle production systems. The characteristics of this framework include environmental lifecycle study, environmental performance metrics, hyper-network model of integrated e-supply chain networks, fuzzy multi-objective optimization method, discrete-event simulation approach, and scalable enterprise environmental management system design. The dissertation research reveals that integration of e-Business strategy into production systems can alter current industry practices along a pathway towards sustainability, enhancing resource productivity, improving cost efficiencies and reducing lifecycle environmental impacts. The following research challenges and scholarly accomplishments have been addressed in this dissertation: Identification and analysis of environmental impacts of e-Business. A pioneering environmental lifecycle study on the impact of e-Business is conducted, and fuzzy decision theory is further applied to evaluate e-Business scenarios in order to overcome data uncertainty and information gaps; Understanding, evaluation, and development of environmental performance metrics. Major environmental performance metrics are compared and evaluated. A universal target-based performance metric, developed jointly with a team of industry and university researchers, is evaluated, implemented, and utilized in the methodology framework; Generic framework of integrated e-supply chain network. The framework is based on the most recent research on large complex supply chain network model, but extended to integrate demanufacturers, recyclers, and resellers as supply chain partners. Moreover, The e-Business information network is modeled as a overlaid hypernetwork layer for the supply chain; Fuzzy multi-objective optimization theory and discrete-event simulation methods. The solution methods deal with overall system parameter trade-offs, partner selections, and sustainable decision-making; Architecture design for scalable enterprise environmental management system. This novel system is designed and deployed using knowledge-based ontology theory, and XML techniques within an agent-based structure. The implementation model and system prototype are also provided. The new methodology and framework have the potential of being widely used in system analysis, design and implementation of e-Business enabled engineering systems

Unexpected learning: Art, play, and social space

This study is about play. It is about some of the forms of play you may have engaged in as a kid and are now integrated in the art practices of three artists, Núria Güell, Jordi Canudas and Nicolás Dumit-Estévez. Their practices defy the traditional conceptions of both art and play as ends in themselves. This study is contextualized as phenomenological research that aims at understanding what role play can assume in socially engaged art practices, and in what ways it provides a dynamic filter or trajectory for carrying each work forward. It is centered on the experiences of three artists who have developed practices that are participatory, presented in public spaces, open to diverse audiences, and whose design seeks at questioning, transforming or experimenting with new forms of sociability. The study presents the artists’ narratives through interviews and intertwined with the researcher’s experience with the data and documentation, acting as a site for shared meaning making. The findings of the study suggest that essential to play is movement, and that play’s integration in socially engaged art practices opens up transitional or permeable spaces in which previously discrete identities become border crossings opening to the potential emergence of new ideas about self and societ

Modeling the perceptual component of conceptual learning—A coordination perspective

Although a picture may be worth a thousand words, modeling diagrams as propositions and modeling visual processing as search through a database of verbal descriptions obscures what is problematic for the learner. Cognitive modeling of language learning and geometry has obscured the learner's problem of knowing where to look—what spaces, markings, and orientations constitute the objects of interest? Today we are launching into widespread use of multimedia instructional technology, without an adequate theory to relate perceptual processes to conceptual learning. Does this matter? In this article, I review the symbolic approach to modeling perceptual processing and show its limitations for explaining difficulties children encounter in interpreting a graphic display. I present an alternative analysis by which perceptual categorization is coupled to behavior sequences, where gesturing and emotional changes are essential for resolving impasses and breaking out of loops. I conclude by asking what kind of cognitive theory we need to exploit communication technology. Have we been correct to assume that pedagogy must be grounded in an accurate psychological model of knowledge, memory, and learning