The theory of affordances

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Narrative Methods for the Human Sciences

Additive manufacturing (AM) alias 3D printing translates computer-aided design (CAD) virtual 3D models into physical objects. By digital slicing of CAD, 3D scan, or tomography data, AM builds objects layer by layer without the need for molds or machining. AM enables decentralized fabrication of customized objects on demand by exploiting digital information storage and retrieval via the Internet. The ongoing transition from rapid prototyping to rapid manufacturing prompts new challenges for mechanical engineers and materials scientists alike. Because polymers are by far the most utilized class of materials for AM, this Review focuses on polymer processing and the development of polymers and advanced polymer systems specifically for AM. AM techniques covered include vat photopolymerization (stereolithography), powder bed fusion (SLS), material and binder jetting (inkjet and aerosol 3D printing), sheet lamination (LOM), extrusion (FDM, 3D dispensing, 3D fiber deposition, and 3D plotting), and 3D bioprinting....

Polymers for 3D Printing and Customized Additive Manufacturing

Additive manufacturing processes take the information from a computer-aided design (CAD) file that is later converted to a stereolithography (STL) file. In this process, the drawing made in the CAD software is approximated by triangles and sliced containing the information of each layer that is going to be printed. There is a discussion of the relevant additive manufacturing processes and their applications. The aerospace industry employs them because of the possibility of manufacturing lighter structures to reduce weight. Additive manufacturing is transforming the practice of medicine and making work easier for architects. In 2004, the Society of Manufacturing Engineers did a classification of the various technologies and there are at least four additional significant technologies in 2012. Studies are reviewed which were about the strength of products made in additive manufacturing processes. However, there is still a lot of work and research to be accomplished before additive manufacturing technologies become standard in the manufacturing industry because not every commonly used manufacturing material can be handled. The accuracy needs improvement to eliminate the necessity of a finishing process. The continuous and increasing growth experienced since the early days and the successful results up to the present time allow for optimism that additive manufacturing has a significant place in the future of manufacturing.

/pdf/a-review-of-additive-manufacturing-2g0afwxkp6.pdf

A Review of Additive Manufacturing

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

Over the past decade, ‘smart’ cities have capitalized on new technologies and insights to transform their systems, operations and services. The rationale behind the use of these technologies is that an evidence-based, analytical approach to decision-making will lead to more robust and sustainable outcomes. However, harvesting high-quality data from the dense network of sensors embedded in the urban infrastructure, and combining this data with social network data, poses many challenges. In this paper, we investigate the use of an intelligent middleware – Device Nimbus – to support data capture and analysis techniques to inform urban planning and design. We report results from a ‘Living Campus’ experiment at the University of Melbourne, Australia focused on a public learning space case study. Local perspectives, collected via crowd sourcing, are combined with distributed and heterogeneous environmental sensor data. Our analysis shows that Device Nimbus’ data integration and intelligent modules provide high-quality support for decision-making and planning.

Distributed and Heterogeneous Data Analysis for Smart Urban Planning

Recent history has seen many schools shift from their original purpose of standardization and facts to focus on soft skills and global preparedness. The physical design of a school follows suit, shifting from identical classrooms and autonomous teachers to more collaborative shared spaces deemed 'innovative'. While those who formulate such schools and school designs (i.e. the architects and school leaders) often have clear anticipations of the teaching and learning behaviors, these expectations oftentimes go unrealized and educators maintain traditional practice despite the innovative spaces. It is proposed that this misalignment between expectation and reality is due to a lack of holistic change in the organizational system underpinning the new spatial design leaving the enactors of the envisioned environment (i.e. the educators) without clear expectations and supports to successfully shift their practice. To answer this need, this paper advances the Burke-Litwin Model for Organizational Performance and Change as a theoretical lens for understanding the holistic system involved in the transition of schools from traditional learning spaces to more innovative learning environments.

Viewing the transition to innovative learning environments through the lens of the burke-litwin model for organizational performance and change

What is the role of representation and simulation in the design process and how do these support learning in the design studio? How can augmented reality assist such learning? To examine this, the chapter will first consider how modes of representation have been used in architectural design, with particular focus on the role of models, then consider how these modes affect the design student’s understanding of their work, and finally postulate the manner in which augmented reality contributes to this process.

Debating Opportunities: Learning Design Through Different Structures

Postscript: the virtual design studio 1994

This section presents research in managing change and risk inherent in the introduction of new learning environments. The topic is introduced, and a brief review is given for each chapter in the following section.

/pdf/introduction-to-part-i-change-and-risk-2g54f41xhl.pdf

Thomas Kvan

Papers

Distributed and Heterogeneous Data Analysis for Smart Urban Planning

Viewing the transition to innovative learning environments through the lens of the burke-litwin model for organizational performance and change

Debating Opportunities: Learning Design Through Different Structures

Postscript: the virtual design studio 1994

Introduction to Part I: Change and Risk