Joshua Bard
Associate Professor and Associate Head Carnegie Mellon University
- Pittsburgh PA
Joshua Bard is an architectural educator conducting applied research at the intersection of construction culture and robotic technology.
Biography
Joshua is a founding partner of Archolab, an award winning research collaborative finding their bearings at the intersection of architecture’s emerging techno-future(s) and a historically grounded commitment to making. Archolab’s research includes Morphfaux, a project that recovers ancient techniques of applied architectural plaster through the lens of robotic manufacturing, and Spring Back, a reformulation of steam bending using advanced parametric modeling and digital fabrication tools. Archolab’s work has been recognized with Architect Magazine’s R+D Award, an Unbuilt Architecture Citation from the Boston Society of Architects, and a Merit Award from the Canadian Wood Council.
Joshua received his M.Arch with distinction from the University of Michigan where he also served on the faculty and as Director of Taubman College’s Digital Fabrication Laboratory. Joshua holds a BA in literature and philosophy from Wheaton College and has worked for PLY Architecture (Ann Arbor, MI) and M1/DTW (Detroit, MI).
Areas of Expertise
Media Appearances
Architecture comes alive through Carnegie Museum of Art’s new Plaster ReCast augmented reality app
NEXTpittsburgh online
2017-10-30
With a Google Tango tablet in hand, Josh Bard, an assistant professor at the Carnegie Mellon University School of Architecture, points at the column and taps a button. On the screen, a 3D animation of the Tomb of Mausolus in Turkey, a historical site built circa 350 BC, takes shape. It’s surrounded by columns on all sides.
Media
Industry Expertise
Accomplishments
R+D First Award
2013
Architect magazine
Education
Wheaton College
B.A.
Literature and Philosophy
2002
minor in German
University of Michigan
M.Arch.
Architecture
2007
Affiliations
- Integrative Design, Arts, and Technology (IDeATe) : Intelligent Environments curriculum development committee member
- Association for Robotics in Architecture : Member
- Association for Computer Aided Design in Architecture (ACADIA) : Member
Links
Event Appearances
Expressive Interactive Surfaces
SXSW Interactive Festival Austin, TX
Decorative Robotic Plastering: Real-Time Human Machine-Collaboration in High-Skill Domains
Real Time, eCAADe 33rd conference Vienna, Austria
Material Affordance in Robotic Fabrication
David Lawrence Convention Center Pittsburgh, PA
Research Grants
Through Thick and Thin: Recovering the Craft of Architectural Plaster
College of Fine Arts(CFA) Fund for Research and Creativity, Carnegie Mellon University
With Richard Tursky co-PI
Human Machine Virtuosity: building bridges for accelerated digital-physical iteration
Integrative Design, Arts, and Technology (IDeATe), Carnegie Mellon University
With Garth Zeglin co-PI
Tooled Deposition of HighPerformance Building Components for PostProcessing of 3D printed Architectures
Manufacturing Futures Initiative, Carnegie Mellon University
With Dana Cupkova co-PI, Garth Zeglin and Newell Washburn Collaborators
Articles
Reality is interface: Two motion capture case studies of human–machine collaboration in high-skill domains
International Journal of Architectural Computing2016
This article explores hybrid digital/physical workflows in the building trades, a high-skill domain where human dexterity and craft can be augmented by the precision and repeatability of digital design and fabrication tools. In particular, the article highlights two projects where historic construction techniques were extended through live motion capture of human gesture, information-rich visualization projected in the space of fabrication and custom robotic tooling to generate free-form running moulds.
Robotic concrete surface finishing: a moldless approach to creating thermally tuned surface geometry for architectural building components using Profile-3D-Printing
Construction Robotics2018
This paper focuses on describing a novel hybrid concrete printing/casting process we term Profile-3D-Printing. Profile-3D-Printing is an additive/subtractive manufacturing process that combines deposition of concrete for rough layup with precision tooling for surface finishing of architectural building components commonly found in the architectural precast industry. Our research team from Architecture, the Robotics Institute, and Material Science invented this novel hybrid manufacturing process for robotically printing architectural facade panels with complex surface geometries.
Thermally Informed Robotic Topologies: Profile-3D-Printing for the Robotic Construction of Concrete Panels, Thermally Tuned Through High Resolution Surface Geometry
Robotic Fabrication in Architecture, Art and Design2018
This paper explores the thermal design and robotic construction of high-performance building components. The complex surface geometry of these components actuate specific thermal behavior in passive building systems through implementing the principles of convection in thermal mass. Our seamless design-to-fabrication workflow uses optimization methods that combine measured thermal data and simulation feedback with advanced modeling and emerging robotic manufacturing techniques.
Material characterization of workability and process imaging for robotic concrete finishing
Construction Robotics2021
In this paper, we discuss a robotic-assisted concrete finishing method for fabricating architectural panels. Concrete finishing is an important process for producing architectural elements with acceptable surface quality. It is also a challenging process conventionally relying on skillful laborers. We describe a hybrid framework incorporating both human skill and robotics in the concrete finishing process and a multi-phase sensing strategy to assist in part touch-up and to validate final surface quality.
Image Classification for Robotic Plastering with Convolutional Neural Network
Robotic Fabrication in Architecture, Art and Design2018
Inspecting robotically fabricated objects to detect and classify discrepancies between virtual target models and as-built realities is one of the challenges that faces robotic fabrication. Industrial-grade computer vision methods have been widely used to detect manufacturing flaws in mass production lines. However, in mass-customization, a versatile and robust method should be flexible enough to ignore construction tolerances while detecting specified flaws in varied parts.
