Tangible Remains

<p>This design led research advocates for an architecture indicative of the preceding history; that engages in an active dialogue with the site. Developed through shifting scales and media, the thesis investigates how architecture can give expression to the singularities of site. The milieu of erasures and the enduring traces are drawn to the surface and manifested across three design phases: 1 Chinese Garden, present condition; 2 Art School, past reflection; 3 Artist Colony, future projection. Through a process of editing, fragments are extended and retracted between the phases as more intense programs are introduced. The final design outcome is the result of an additive design methodology, developed across the course of the thesis. The design methodology is a model for combining sites in contemporary urban development; a model for co-existence of past and present into the future.</p>

Tangible Remains

<p>This design led research advocates for an architecture indicative of the preceding history; that engages in an active dialogue with the site. Developed through shifting scales and media, the thesis investigates how architecture can give expression to the singularities of site. The milieu of erasures and the enduring traces are drawn to the surface and manifested across three design phases: 1 Chinese Garden, present condition; 2 Art School, past reflection; 3 Artist Colony, future projection. Through a process of editing, fragments are extended and retracted between the phases as more intense programs are introduced. The final design outcome is the result of an additive design methodology, developed across the course of the thesis. The design methodology is a model for combining sites in contemporary urban development; a model for co-existence of past and present into the future.</p>

Accelerating Additive Design with Probabilistic Machine Learning

Additive manufacturing (AM) has been growing rapidly to transform industrial applications. However the fundamental mechanism of AM hasn't been fully understood which resulted in low success rate of building. A remedy is to introduce surrogate modeling based on experimental dataset to assist additive design and increase design efficiency. As one of the first papers for predictive modeling of AM especially Direct Energy Deposition (DED), this paper discusses a bidirectional modeling framework and its application to multiple DED benchmark designs including (1) Forward Prediction with Cross Validation, (2) Global Sensitivity Analyses, (3) Backward Prediction and Optimization, (4) Intelligent Data Addition. Approximately 1,150 mechanical tensile test samples were extracted and tested with input variables from machine parameters, post-process, output variables from mechanical, microstructure and physical properties.