Additive Manufacturing (AM) is the process of part building by stacking layers of material on top of each other. Various challenges for a metal powder based process include reducing the staircase effect which leads to poor surface finish of the part, and minimal use of support structures for regions with overhangs or internal hollow volumes. Part build orientation is a crucial process parameter which affects part quality, in particular, Geometric Dimensioning & Tolerancing (GD&T) errors on the part, the energy expended and the extent of support structures required. This paper provides an approach to identify an optimal build orientation which will minimize the volume of support structures while meeting the specified GD&T criteria of the part for a DMLS based process. Siemens PLM NX API is used to extract the GD&T callouts and associated geometric information of the CAD model. The regions requiring support structures are identified and a Quadtree decomposition is used to find the volume of support structures. The mathematical relationships between build orientation and GD&T are developed as part of a combined optimization model to identify best build orientations for minimizing support structures while meeting the design tolerances. The feasible build orientations along with the corresponding support structures are depicted using a visual model.

The recent attention towards cultural preservation and heritage studies has positioned design to redefine cultural experiences in the contemporary context. Against this backdrop, design is marked by an ability to transform and revitalise cultural practices to change and alter perceptions, generate and disseminate knowledge, and create new value through the curation of experience. A case-study on temple architecture in Tamil Nadu, India presents the tensions posed by globalisation to discuss and explore the development of design tools, evaluation of the design process, and the creation of a design-based framework for intangible culture and heritage. This paper introduces a future mode for designing cultural experiences through community engagement by identifying four key design principles guiding the preservation and sustainability of endangered cultural traditions, practices, and spaces.

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This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through analytical and semi-analytical techniques which explore worst-case bounds and the generalization to k treatments. We determine theoretical limits for the experimental budget across all possible scenarios. The optimal level of experimentation that is recommended by this study varies extensively and depends on the experimental environment as well as the number of available units. This highlights the importance of such an approach which incorporates these factors to determine the budget.

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