MULTIFILE
In the past decades, we have faced an increase in the digitization, digitalization, and digital transformation of our work and daily life. Breakthroughs of digital technologies in fields such as artificial intelligence, telecommunications, and data science bring solutions for large societal questions but also pose a new challenge: how to equip our (future)workforce with the necessary digital skills, knowledge, and mindset to respond to and drive digital transformation?Developing and supporting our human capital is paramount and failure to do so may leave us behind on individual (digital divide), organizational (economic disadvantages), and societal level (failure in addressing grand societal challenges). Digital transformation necessitates continuous learning approaches and scaffolding of interdisciplinary collaboration and innovation practices that match complex real-world problems. Research and industry have advocated for setting up learning communities as a space in which (future) professionals of different backgrounds can work, learn, and innovate together. However, insights into how and under which circumstances learning communities contribute to accelerated learning and innovation for digital transformation are lacking. In this project, we will study 13 existing and developing learning communities that work on challenges related to digital transformation to understand their working mechanisms. We will develop a wide variety of methods and tools to support learning communities and integrate these in a Learning Communities Incubator. These insights, methods and tools will result in more effective learning communities that will eventually (a) increase the potential of human capital to innovate and (b) accelerate the innovation for digital transformation
In the past decade additive manufacturing has gained an incredible traction in the construction industry. The field of 3D concrete printing (3DCP) has advanced significantly, leading to commercially viable housing projects. The use of concrete represents a challenge because of its environmental impact and CO2 footprint. Due to its material properties, structural capacity and ability to take on complex geometries with relative ease, concrete is and will remain for the foreseeable future a key construction material. The framework required for casting concrete, in particular non-orthogonal geometries, is in itself wasteful, not reusable, contributing to its negative environmental impact. Non-standard, complex geometries generally require the use of moulds and subsystems to be produced, leading to wasteful, material-intense manufacturing processes, with high carbon footprints. This research proposal bypasses the use of wasteful scaffolding and moulds, by exploring 3D printing with concrete on reusable substructures made of sand, clay or aggregate. Optimised material depositing strategies for 3DCP will be explored, by making use of algorithmic structural optimisation. This way, material is deposited only where structurally needed, allowing for further reduction of raw-material use. This collaboration between Neutelings Riedijk Architects, Vertico and the Architectural Design and Engineering Chair of the TU Eindhoven, investigates full-scale additive manufacturing of spatially complex 3D-concrete printed components using multi-material support systems (clay, sand and aggregates). These materials can be easily shaped multiple times into substrates with complex geometries, without generating material waste. The 3D concrete printed full-scale prototypes can be used as lightweight façade elements, screens or spatial dividers. To generate waterproof components, the cavities of the extruded lattices can be filled up with lightweight clay or cement. This process allows for the exploration of new aesthetic, creative and circular possibilities, complex geometries and new material expressions in architecture and construction, while reducing raw-material use and waste.
In this project, the AGM R&D team developed and refined the use of a facial scanning rig. The rig is a physical device comprising multiple cameras and lighting that are mounted on scaffolding around a 'scanning volume'. This is an area at which objects are placed before being photographed from multiple angles. The object is typically a person's head, but it can be anything of this approximate size. Software compares the photographs to create a digital 3D recreation - this process is called photogrammetry. The 3D model is then processed by further pieces of software and eventually becomes a face that can be animated inside in Unreal Engine, which is a popular piece of game development software made by the company Epic. This project was funded by Epic's 'Megagrant' system, and the focus of the work is on streamlining and automating the processing pipeline, and on improving the quality of the resulting output. Additional work has been done on skin shaders (simulating the quality of real skin in a digital form) and the use of AI to re/create lifelike hair styles. The R&D work has produced significant savings in regards to the processing time and the quality of facial scans, has produced a system that has benefitted the educational offering of BUas, and has attracted collaborators from the commercial entertainment/simulation industries. This work complements and extends previous work done on the VIBE project, where the focus was on creating lifelike human avatars for the medical industry.
