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Bio-Fabrication and Bio-Inspired Manufacturing Techniques for the Built Environment
Published in Yoseph Bar-Cohen, Advances in Manufacturing and Processing of Materials and Structures, 2018
Brook S. Kennedy, Christopher Maurer, William Sullivan
Creating the complex geometries of the gradient chair relies on abundant computing power, which grows daily through hardware improvements and the cloud. CAD programs like Solidworks, Rhinoceros, Fusion 3D, and computer-aided manufacturing programs used to translate CAD data into code that a robotic manufacturing tool can understand will continue to become more powerful. Now, software has the ability to not only calculate and evaluate complex 3D geometry to fulfill human design intention, but it can also harness through a form of artificial intelligence the ability to generate a wide range of alternative design options beyond what humans might envision on their own. This form of parameter-based artificial intelligence for design is most commonly referred to as “generative design.” Generative design has the ability to accelerate the iterative cycle of design by allowing designers to try, assess, and eliminate more design options in an evolutionary sense (Menges, 2012).
Integrated functional metro station design using BIM tools
Published in Daniele Peila, Giulia Viggiani, Tarcisio Celestino, Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art, 2020
C. Pallaria, D. Vercellino, A. Bolzonello
Generative Design is an evolution of computational design in which algorithms that mimic natural evolution are used. Designers insert the expected results together with a series of boundary conditions such as materials, manufacturing methods, costs and physical constraints. Subsequently, it is possible to explore an almost unlimited number of alternatives, deriving from all the possible permutations generated by the defined algorithm. With generative design there is no single solution but, potentially, thousands of excellent solutions. It will be up to the designer to identify the one that best meets his needs.
Integrated functional metro station design using BIM tools
Published in Daniele Peila, Giulia Viggiani, Tarcisio Celestino, Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art, 2019
C. Pallaria, D. Vercellino, A. Bolzonello
Generative Design is an evolution of computational design in which algorithms that mimic natural evolution are used. Designers insert the expected results together with a series of boundary conditions such as materials, manufacturing methods, costs and physical constraints. Subsequently, it is possible to explore an almost unlimited number of alternatives, deriving from all the possible permutations generated by the defined algorithm. With generative design there is no single solution but, potentially, thousands of excellent solutions. It will be up to the designer to identify the one that best meets his needs.
Six Human-Centered Artificial Intelligence Grand Challenges
Published in International Journal of Human–Computer Interaction, 2023
Ozlem Ozmen Garibay, Brent Winslow, Salvatore Andolina, Margherita Antona, Anja Bodenschatz, Constantinos Coursaris, Gregory Falco, Stephen M. Fiore, Ivan Garibay, Keri Grieman, John C. Havens, Marina Jirotka, Hernisa Kacorri, Waldemar Karwowski, Joe Kider, Joseph Konstan, Sean Koon, Monica Lopez-Gonzalez, Iliana Maifeld-Carucci, Sean McGregor, Gavriel Salvendy, Ben Shneiderman, Constantine Stephanidis, Christina Strobel, Carolyn Ten Holter, Wei Xu
Generative design involves an algorithm that iteratively generate outputs that meet certain constrains, the design candidates, and a designer that select and fine tune these outputs (Kallioras & Lagaros, 2020). In fact, the designer itself can be an AI, resulting in a fully automated design process. For example, in generative adversarial networks, two different artificial neural networks take the roles of generator and designer (Goodfellow et al., 2020). Another approach uses Darwinian evolution algorithms (AI) to create neural networks algorithms (AI) that become progressively good at solving problems (Lipson & Pollack, 2000). In this context, on which an AI is designing another AI, the situation is more complex, but the human meta-designer should ascribe to the same human-centered design and evaluation principles as any other AI designer.
The ASSISTANT project: AI for high level decisions in manufacturing
Published in International Journal of Production Research, 2023
G. Castañé, A. Dolgui, N. Kousi, B. Meyers, S. Thevenin, E. Vyhmeister, P-O. Östberg
AI technologies are extensively used today for automation and learning in the delivery of digital services in the cloud computing offerings of companies such as Google, Facebook, and Amazon. However, while these services are commercially successful, scalable, and widely applicable in conventional ICT environments, large parts of the underlying business models are based on harnessing the economy of scale effects of massive data centres which is not directly transferable to manufacturing. The reality and needs of manufacturing industries that make this approach challenging include the fact that manufacturing often targets production of dedicated products (instead of generalised services), using specialised tools (instead of generic ICT platforms), and addresses small markets (instead of broad ICT-based or integrated business segments). Certain AI-related technologies have since long been adapted and developed for manufacturing, e.g. automated machine tuning and predictive quality inspection, but manufacturing AI tends to be applied at fine-grained decision making levels which makes it hard to scale these efforts. To extend the capabilities of manufacturing AI systems and make them more applicable in broader scenarios, ASSISTANT employs a combination of predictive and prescriptive analytics techniques to embed AI technologies in higher-level manufacturing decision making such as process planning, production planning, and scheduling. To retain human control and system accountability, ASSISTANT develops a generative design framework for optimal interaction between human decision making and AI-based decision support. As defined in Aameri, Cheong, and Beck (2019), generative design is an approach to iterative design where users can specify goals expressed in terms of objectives and mathematical constraints, and software applications develop sets of feasible and/or optimal design solutions for human evaluation. While generative design was originally developed for iterative product design, the ASSISTANT project develops tools to extend the technique to other manufacturing decisions.