Design Technology Portfolio — Robotic + Design

AI + Robotics + Computation + DfMA Portfolio

Selected works by Deon Kim

Robotics + Design Master’s of Science in Design Robotics & Autonomous System

Design Technology & Robotic Fabrication

Table of contents

MSD-RAS ROBOTICS & DESIGN

02 - 11

Neural Synthesis of Ruled Surfaces:

12 - 23

The Heterotopic Vessels: Industrial Corpse to Autonomous Era

24 - 41

Mapping Latent Space to Robotic Toolpath Dynamic Fabric Formwork: Advanced Additive Manufacturing

1

Neural Synthesis of Ruled Surfaces: Mapping Latent Space to Robotic Toolpaths “We renounce the line as a descriptive value... We affirm the line only as a direction of the static forces and their rhythm in objects.

— Naum Gabo & Antoine Pevsner, The Realistic Manifesto (1920)

Proyect Type Proyect Year Course Institution Instructor Collaborator

A

B

: AI Deep Learning + Robotic Wire-Cut : 2020 : Material Agencies I : University of Pennsylvania, Weitzman School of Design : Andrew Saunders, Associate Professor of Architecture : Riley Studebaker

B’

Neural Synthesis to Robotic Fabrication 36”

A’ 24”

This project reinterprets Naum Gabo’s Linear Construction Series by translating its fundamental geometric logic into a computational framework. By extracting the “ruled surface” signatures from digitally reproduced sculptures, established a discrete ruleset for 3D formal generation.

Gabo’s geometric elements were utilized to train Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), mapping the sculpture’s aesthetic signature into a multi-dimensional latent space.

24”

36”

12”

2

This process allows for the synthesis of novel spatial relationships that maintain Gabo’s distinct visual signatures while enabling robotic instructions. The final output comprises five unique formal interpretations, each decoded from the latent space into actionable blueprints for fabrication.

MATERIAL AGENCIES I: AI Deep Learning & Robotic-Wire Fabrication

MSD-RAS ROBOTICS & DESIGN

Torsion Variation No.2

Robotic Hot Wirecut Prototype

Micro-Relief Bespoke Tooling for Ruled Geometry, High Density EPS Foam (36”x24”x16”)

3

AI Deep Learning & Latent Synthesis Generative Adversarial Networks (GANs) Latent Space

Generator

Back Propagation: Maximize Error

Generator Image Real/Fake

Discriminator Dataset

Real Image Back Propagation: Minimize Error

Morphological Decoding & Feature Extraction

Neural Encoding via CNN & Latent Mapping

The project initiates with a rigorous morphological analysis of Naum Gabo’s Linear Construction series, identifying the ruled surface as the primary tectonic unit.

By deconstructing the geometric relationship between curvilinear rails and linear threads, the workflow extracts fundamental Topological Signatures. These signatures are distilled into a specialized Training Dataset, stripping away visual noise to provide the neural network with high-fidelity geometric instructions.

Utilizing Convolutional Neural Networks (CNNs), the distilled dataset undergoes a systematic process of Feature Extraction. The network identifies recurring mathematical patterns and spatial hierarchies within the Constructivist logic, effectively translating historical artistic intuition into a machine-readable format.

This stage encodes the structural “signature” of the ruled surfaces into a multidimensional Latent Space, establishing a generative foundation for complex formfinding.

Geometry Analysis

AI Deep Learning

Naum Gabo’s Linear Constuction

4

(Real Image)

Latent Space Convolutional Neural Networks (CNNs)

Topological Signature Input Image

(Dataset)

[+] Style Scale: 0.5 [+] Style Weight: 1500

[+] Style Scale: 0.5 [+] Style Weight: 2500

[+] Style Scale: 0.5 [+] Style Weight: 3000

[+] Style Scale: 0.4 [+] Style Weight: 1500

[+] Style Scale: 04 [+] Style Weight: 2500

[+] Style Scale: 0.4 [+] Style Weight: 3000

[+] Style Scale: 0.3 [+] Style Weight: 1500

[+] Style Scale: 0.3 [+] Style Weight: 2500

[+] Style Scale: 0.3 [+] Style Weight: 3000

(Generator)

MATERIAL AGENCIES I: AI Deep Learning & Robotic-Wire Fabrication

MSD-RAS ROBOTICS & DESIGN

Neural Profiles to Robotic Toolpaths

6 Axis - Hot Wire Prodution / 36”x36”x48” Industrial High Density Foam

Latent Space & Generative Synthesis

Robotic Materialization

The transition from analysis to creation occurs within the Latent Manifold. Rather than simple replication, the process involves Parametric Exploration of the latent space. By systematically adjusting hyperparameters— specifically Style Scale (spatial frequency) and Style Weight (feature prominence)—the workflow navigates through infinite formal variations. This iterative tuning, driven by Generative Adversarial Networks (GANs), allows for the discovery of Synthetic Variations that maintain Gabo’s structural rigor while introducing novel algorithmic complexities.

The signature profile is decoded from the latent space and translated into precise 6-axis robotic toolpaths, bridging the gap between abstract computational intelligence and physical reality. The final fabrication utilizes robotic hotwire cutting to realize a signature profile of data, validating the integration of neural synthesis and automated craftsmanship.

Digital Fabrication

Robotics Fabrication

Post-Constructivist Signature

ABB IRB-4600 6-Axis Industrial Robot Arm End Effector : Hot Wire Cut

Signature Profile

Robot Simulation

Robotic Fabrication @ PennDesign Robotics Lab

5

Prototype

Micro-Relief Bespoke Tooling

Prototype

Micro-Relief Bespoke Tooling

Post Constructivist Topological Signature

Latent Feature Distillation from Constructivist Rulesets Micro-Relief Bespoke Tooling for Ruled Geometry, High Density EPS Foam

Signature Profile of Gabo’s Linear Construction

Robotic Hot Wirecut Prototype Micro-Relief Bespoke Tooling for Ruled Geometry, High Density EPS Foam (60”x60”x18”)

2020 Gabo’s Line

Robotic Hot Wirecut Micro-Relief Bespoke Tooling for Rule

ear Construction

Prototype Wall Panel ed Geometry, High Density EPS Foam

The Heterotopic Vessels: Second Breath of the Industrial Corpse Proyect Type Proyect Year Course Instructor Collaborator

: Computation Design + Robotic Carbor-Fiber Weaving : 2020 : Material Agencies II : Ezio Blasetti : Riley Studebaker

Photograph Historic American Engineering Record (HAER) Library of Congress Archive c. 1988 · Philadelphia Navy Yard

“The boat is a floating piece of space, a place without a place, that exists by itself, that is closed in on itself and at the same time is given over to the infinity of the sea...

The ship is the heterotopia par excellence. In civilizations without boats, dreams dry up, espionage takes the place of adventure, and the police take the place of pirates....”

12

— Michel Foucault, “Of Other Spaces: Utopias and Heterotopias,” trans. Jay Miskowiec, Diacritics 16, no.

1 (Spring, 1986): 22–27.

MATERIAL AGENCIES II: CARBON FIBER WEAVING

This project operates on the threshold between industrial obsolescence and autonomous emergence. Located within the decommissioned cargo hold of a vessel—a silent ‘Dead Dry Dock’—the intervention seeks to reclaim the ship’s status as a heterotopia par excellence. We perceive this rusted hull not as a graveyard of 19th-century realism, but as a Heterotopic Incubator: a protective substrate for the birth of a new architectural species.

If the industrial ship was a rigid machine for the transport of matter, our intervention is a metabolic organism designed for the incubation of culture and ecology. By infiltrating the hollowed-out corpse of the vessel with a ‘Synthetic Nervous System’ of robotically woven carbon fiber, we catalyze a transition from Heavy to Light and from Mechanical to Algorithmic. This is the ‘Second Breath’ of the vessel—a metamorphic rebirth where the inorganic steel of the past fosters the autonomous, living agencies of the future. The result is a hybrid ecosystem that remains always complete, yet always under construction.

MSD-RAS ROBOTICS & DESIGN

Engineering Drawing Marine Railway, Boston Navy Yard Crandall Engineering Co., c. 1916

13

Computational Tectonics: The Synthetic Graft Tectonic Transformation Industrial Era

Autonomous Era

LOAD BEARING NODE

Existing Status:

Industrial Steel Grid

Process:

Topology Optimization

Evolution:

Autonomous Lattice System

LOAD BEARING NODE

EXISTING DRY DOCK

Philadelphia Navy Yard South Broad Street Philadelphia, Pennsylvania, U.S.

Topology Optimization : Horizontal Frame Based Generation

14

Diagram Generated

By Millipede / Grasshopper

MATERIAL AGENCIES II: CARBON FIBER WEAVING

MSD-RAS ROBOTICS & DESIGN

Algorithmic Morphogenesis and Multi-axial Discretization Forensic Analysis and Structural Retrofitting

The architectural intervention commences with a quantitative forensic analysis of the maritime railway—a site defined by mechanical obsolescence and advanced structural fatigue. Rather than a traditional restorative preservation, the project proposes a computational graft generated through Topology Optimization (TO). By defining the dry dock as a design domain Ω with specific boundary conditions, the algorithm identifies a distribution of material density ρ that minimizes structural compliance. The resulting non-linear geometry functions as a high-performance load-bearing exoskeleton, stabilizing compromised zones while arresting the entropic decay of the industrial relic.

Topological Morphogenesis: From Mass to Light

The generative process utilizes a Voxel-based Density Map to differentiate architectural programs, facilitating a transition from Industrial Mass to Digital Flow. Through iterative optimization, the structure evolves into a differentiated porous lattice, mimicking the logic of synergetic systems. High-density regions (ρ→1) provide primary structural stabilization for the dry dock walls, while low-density regions (ρ→0) establish an atmospheric porosity. This “Computational Jungle” functions as a metabolic interface, serving as a public park that bridges the rigid orthogonal past with a fluid, ecological future.

Forensic Retrofitting via Topology Optimization

Rather than a traditional restorative preservation, the intervention proposes a computational graft generated through Topology Optimization (TO). By defining the dry dock as a design domain Ω with specific boundary conditions, the algorithm identifies a distribution of material density ρ that minimizes structural compliance.

Performance Metrics [0.0] Level 01

[0.25] |

[0.50] Low Relative Density

[0.75]

[1.0]

| ρ⋲[0.0, 0.25]

Level 02

|

Med-Low Relative Density

| ρ⋲[0.25, 0.50]

Level 04

|

Max Relative Density

| ρ⋲[0.75, 1.00]

Level 03

|

Med-High Relative Density

| ρ⋲[0.50, 0.75]

Topology Optimization : Vertical Frame Based Generation

15

Morphogenetic Discretization: Topology Optimization & Modular Logic Existing Status:

Effective Material Density (ρ): 0.65–0.85 Structural Integrity Index: 0.25–0.40 Crack / Void Ratio: 20–35% Load Path Discontinuity: Very High Environmental Exposure: Extreme (Tidal + salinity + cyclic wetting)

Dry Dock Elevation & TO Diagram

TO (Load Path Formation)

Material Reduction Target: 30–45% Optimized Density (ρ): 0.55–0.70 Compliance Reduction: ~40% Primary Load Paths: Re-established (continuous stress lines) Safety Factor (SF): ~1.2–1.5

Synthesis of Form: Material-Optimized Morphogenesis The initial results of the form-finding process are no longer treated as mere conceptual abstractions but have matured into a variable-density volumetric mesh. This stage represents the definitive state of the graft, operating as a biological-synthetic hybrid that intuitively adapts its thickness and porosity to localized structural demands.

Multi-Network Integration

Porosity Ratio: 35–50% Surface Area Increase: +200–300% Water Flow Permeability (k): High Habitat Nodes Density: ~3–5 nodes/m² Load Sharing System: Hybrid (structural + organic growth)

Through this tectonic rectification, the industrial ruin is healed by a continuous flow of optimized geometry, where areas of high stress manifest as thickened, dense clusters while low-stress zones dissolve into a delicate, airy lattice.

This performance-driven approach ensures that material is only placed where it is mechanically essential, creating a CHOREOHEDRON high-fidelity response to the specific structural failures of HAL2020 the dry dock environment.

1.061

1.061

1.061

1.061

0.884

0.884

0.884

0.884

0.354

0.354

0

1.00

0.866

0.75

0.50

0.433

0.354

0

1.00

0.866

0.75

0.50

0.433

0.25 0

0

0.354

0

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0.866

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0.25 0

0

0

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0.866

0.75

0

0

The Structural Atom: Rhombic Dodecahedron

Weitzman School of Design | Arch 801: Material Agencies | Fall 2020 | Ezio Blasetti

Regenerative Vessel

Material Density (ρ): 0.40–0.60 Self-Supporting Stability Index: >0.9 Biomass Accumulation Rate: Positive (marine colonization) Erosion Resistance: Adaptive (bio-layer protection) Energy Exchange: Passive (tidal-driven system)

16

0.50

0.433

0.25

0.25 0

0

design team: Deon Kim, Riley Studebaker

To materialize this complex morphology, the global geometry is subdivided into a fundamental structural unit: the Rhombic Dodecahedron. This specific module was selected over the conventional cube due to its superior geometric and structural properties within a multi-axial force environment. Unlike the orthogonal bias of a hexahedron, the Rhombic Dodecahedron provides isotropic tessellation, mimicking spherical packing to ensure structural stability across various load trajectories.

A Rhombic Dodecahedron is the most spherical spacefilling polyhedron, it achieves a superior surface-to-volume ratio compared to standard cubic cells. This efficiency is mathematically expressed through the shape factor, where the Dodec module demonstrates an eleven percent increase in material efficiency over the cube.

This reduction in surface area directly translates to less material usage and lower dead weight, allowing for a lighter yet more resilient structure.

MATERIAL AGENCIES II: CARBON FIBER WEAVING

MSD-RAS ROBOTICS & DESIGN Topology Optimization: Partial Elevation

Rhombic Dodecahedron Voxelization

Discretization from Voxel to Molecular System The Structural Atom: Rhombic Dodecahedron To materialize the complex morphology, the geometry is discretized into the Rhombic Dodecahedron—a fundamental structural unit chosen for its superior performance in multi-axial force environments.

Unlike the orthogonal bias of a standard cube, this module provides isotropic tessellation and mimics spherical packing to ensure stability across diverse load trajectories. It serves as a mandate for material economy; as the most spherical spacefilling polyhedron, it achieves a superior surfaceto-volume ratio. Mathematically, this is reflected in the shape factor where the dodecahedron demonstrates an 11% increase in material efficiency over the cube, significantly reducing dead weight while maintaining a resilient, lightweight framework.

17

Robotic Fabrication: Carbon Fiber Weaving Autonomous Fabrication & Robotic Filament Winding

CHOREOHEDRON This chapter documents the transition from computational discretization to physical realization. By utilizingHAL2020 a 6-axis robotic arm, the theoretical Rhombic Dodecahedron modules are translated into high-performance, carbon fiber-reinforced components through a precise, information-driven weaving

robotically wove

Robotic Mandrel System and Kinematic Weaving Path

The fabrication logic employs a specialized robotic mandrel system, where a timber substrate—representing a face of the Rhombic Dodecahedron—serves as the primary geometric guide. A grid of guiding poles is strategically embedded into this panel, acting as temporary anchor points for the carbon fiber filaments. The 6-axis robotic arm executes a complex, continuous toolpath, circulating through the pole matrix to weave a structural skin. This multi-layered winding sequence ensures that the fibers are placed along the exact stress trajectories identified during the topology optimization phase, transforming the abstract vector data into a physical, hightensile mesh.

Digital Simulation & Mandrel System

Substrate Preparation: Three interconnected timber panels are configured to represent the internal dihedral angles of a Rhombic Dodecahedron. The guiding poles are inserted into the grid to define the weaving volume.

Weitzman School of Design | Arch 801: Material Agencies | Fall 2020 | Ezio Blasetti

18

Robotic Fabrication @ PennDesign Robotics Lab

MATERIAL AGENCIES II: HOREOHEDRON CARBON FIBER WEAVING AL2020

MSD-RAS ROBOTICS & DESIGN

robotically woven carbon fiber protoypes:

The Sequential Realization of the Dodec Module Substrate Preparation: Three interconnected timber panels are configured to represent the internal dihedral angles of a Rhombic Dodecahedron. The guiding poles are inserted into the grid to define the weaving volume.

Robotic Filament Winding: The 6-axis arm performs a synchronized deposition of resin-impregnated carbon fiber.

The overlapping patterns create a variable-thickness lattice, where the density of the weave corresponds to the localized load requirements.

Thermal Consolidation: Once the winding is complete, the module undergoes microwave-assisted curing.

This CHOREOHEDRON HAL2020 dielectric heating triggers rapid polymerization, solidifying the flexible fibers into a rigid, carbon-fiber reinforced polymer (CFRP) tectonic unit.

robotically woven carbon fiber protoypes:

Modular Integration: The finished Dodec modules are extracted from the mandrels and assembled into the global “Heterotopia” structure. resulting framework is itzman School of Design | Arch 801: Material Agencies |The Fall 2020 | Ezio Blasetti a seamless, autonomous graft that restores the structural CHOREOHEDRON continuity of the dry dock through advanced robotic agency.

HAL2020

design team: Deon Kim, Riley Studebaker, Xiangguo Cui

CHOREOHEDRON HAL2020 Weitzman School of Design | Arch 801: Material Agencies | Fall 2020 | Ezio Blasetti

zio BlasettiWeitzman School of Design | Arch 801: Material Agencies | Fall 2020 | Ezio Blasetti

Weitzman School of Design | Arch 801: Material Agencies | Fall 2020 | Ezio Blasetti Ezio Blasetti

design team: Deon Kim, R

design team: Deon Kim, Riley Studebaker

design team: Deon Kim, Riley Stude

design team: Deon Kim, Riley Studebaker

Modular Prototype: Robotic Carbon Fiber Weaving design team: Deon Kim, Riley Studeb

Rhombic Dedec Module (24” x 24”) , Carbon Fiber Reinforced Polymer (CFRP)

19

Heterotopia Prototype: Robotic Carbon Fiber Weaving

Rhombic Dedec Module 12 (24” x 24”) , Carbon Fiber Reinforced Polymer (CFRP)

The Heterotopia Manifested The project reclaims a declining mid-century dry dock through the self-organizing poetry of a robotically woven Dodec lattice, ushering in The Autonomous Tectonic Era. The Heterotopia, operates as an open, metabolic interface driven by structural healing and ecological porosity. The computational skeleton creates delicate, airy voids that act as an infrastructural prosthetic to cradle the collapsing concrete walls, arresting their entropic decay. Simultaneously, this extreme porosity transforms the framework into a quiet incubator for life, where the intricate hollows naturally trap wind, moisture, and debris. This allows wild mosses and creeping flora to integrate into the matrix spontaneously, evolving the heavy industrial monument into a fluid, living hybrid landscape where mechanical memory and ecological rebirth seamlessly merge.

Philadelphia Navy Yard “The Autonomous Tectonic Era” South Broad Street Philadelphia, Pennsylvania, U.S.

The Heteroto

“The Autonomou South Broad Street, Philad

opic Vessels

us Tectonic Era” delphia, Pennsylvania, U.S.

Dynamic Fabric Formwork: Advanced Additive Manufacturing Proyect Type Proyect Year Course Instructor Collaborator

24

: Additive Manufacturing + TSO + Robotic Formwork : 2021 : Material Agencies III : Robert-Stuart Smith : Geng Liu, Claire Moriarty

Robotic Clay Printing Experiment Advanced Additive Manufacturing w/ Fabric Membrane with Sensory Tooling @ PennDesign Robotics Lab

Dynamic Materiality: Fabric Formwork & Sensory Robotic Fabrication

MSD-RAS ROBOTICS & DESIGN

Morphogenetic Fabrication for Zero-Waste Architecture

Robotic clay extrusion enables material to be deposited selectively rather than distributed through a uniform panel section. By integrating hollow cavities, differentiated wall thicknesses, and localized reinforcement, the proposed system targets a 20–30% reduction in fired ceramic mass per equivalent façade area.

Because the components are fabricated directly from computational models, geometric variation can be achieved without producing component-specific molds. This moldless workflow reduces tooling requirements and enables non-identical façade elements to be manufactured through a common robotic process.

Under a normalized design scenario, the reduction in ceramic mass corresponds to an estimated 15–30% decrease in productionstage embodied carbon, depending on firing energy, clay composition, component failure rate, and transportation distance.

The façade geometry can additionally function as a passive solarcontrol system, allowing environmental performance to inform both robotic toolpaths and material distribution.

MORPHOLOGICAL DATA: Lifecycle Metrics Formwork Dependency Ratio 100

Embodied Carbon Equivalent 100

High Dependency

High Footprint

75

75

50

50

25

25

0

Zero Dependency

Zero Dependency

25% 30%

Reduced Footprint

0

Reduced Footprint

PROJECTED EMBODIED CARBON REDUCTION

Through reduced fired ceramic mass*

LOWER MATERIAL INPUT

Through hollow and locally differentiated deposition*

Fig. 1 PROJECTED PRODUCTION-STAGE EMBODIED CARBON Normalized GWP Index · Conventional System = 100 Conventional Custom Ceramic Façade: 100 Material-Optimized Robotic Ceramic Façade: 75 Projected Reduction: ≈25% — Projected production-stage carbon reduction through

material-efficient robotic deposition. The comparative index assumes an approximately 25% reduction in fired ceramic mass while maintaining an equivalent functional façade area. Firing energy, material composition, and service life are held constant.

Values represent a design scenario rather than a completed product-level LCA.

Fig. 2 30% LESS FIRED MATERIAL through hollow sections and locally differentiated deposition — Robotic extrusion enables wall thickness, internal cavities, and

structural ribs to be differentiated according to local performance requirements. Under the proposed design scenario, this materially informed geometry reduces fired ceramic consumption by approximately 20–30% compared with a geometrically equivalent solid or uniformly extruded component.

25

Form-Finding and Computational Tectonics: Autonomous Robotic Clay Printing The Synthetic Graft

SYSTEM W DIGITAL - PHYSICA

FABRIC

DIGITAL FORM FINDING (TSO): GH - MILLIPEDE / KANGAROO

FABRIC FORMWORK: ABB IRB-4600 - Clay Extruder /Glue Extruder

AUTONOM ARDUIN

MSD-RAS ROBOTICS & DESIGN

WORKFLOW: AL FEEDBACK LOOP

MOUS PRINTING: NO + FIREFLY

REALTIME SCANNING: XBOX 360 KINECT + UNITY

USER INTERACTION: HUMAN-IN-THE-LOOP CPS

Topology & Shape Optimization: (TSO) Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

28

:5 :0.85 :2.0 :0.1

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Load Condition Setting

Load Factor

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

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Iteration Target Volume Filter Radius Iso-Threshold

Load Factor

:5 :0.85 :2.0 :0.1

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Load Factor

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+

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MSD-RAS ROBOTICS & DESIGN

TSO PATTERN TRANSFER

Dynamic Materiality: Fabric Formwork & Sensory Robotic Fabrication

Localized TSO Pattern

Latex Membrane (Printing Bed)

Robotic Glue Extruder Inhibition Glue (Tension-Inhibiting Boundary - Inspired by TSO pattern)

Modular T-Slot Framework (Printing Area) Fabric Tension Test -Silicone Elastomer Membrane

Glue Test: - Polymer-Modified Gypsum

Fabric Spec:

Material Spec:

- Kevlar Fabric - Kinnted Textile - Heavy Weight Mesh - Fiber Knit

Polymer-Modified Gypsum (Dried) (Strain-Limiting Polymer Ribs - Inspired by TSO pattern)

Clay Printing

- Polyurethane Fast-Cast Resin - Polyurethane Adhesive (PUR)

Programmable Knitted Matrix Utilizing a CNC-knitted porous textile comprising high-tenacity PET and Elastane

Polymer-Modified Gypsum(PMG) Custom-Formulated PolymerModified Gypsum Composite

Load Material Experiment

Material Experiment

Parametric Calibration of Topology Optimization

The generative workflow utilizes 2D Topology Optimization (TSO) to systematically map optimal load-bearing trajectories by modulating force thresholds and boundary conditions. Rather than treating these binary diagrams as definitive top-down geometries, this research interprets them as a Negative Matrix. The computed white pixels (structural solid) do not become the primary building material, but dictate the precise locations of physical constraint lines. Conversely, the black regions (structural void) define unconstrained zones for programmed material deformation.

This parametric translation provides the mathematical baseline to cultivate physical structural depth, establishing a rule-based infrastructure that negotiates directly with gravity.

Material Experiment

Tension & Constrain Experiment

Inverted Materialization: The Negative Matrix Logic

A critical material translation occurs through an Inversion Protocol, where the digital “solid” is radically transposed into a rigid boundary network on a CNC kintted matrix textile. To achieve this, the robotic end-effector extrudes a custom Polymer-Modified Gypsum (PMG) composite—blending plaster’s rapid curing and rigidity with a polymer’s tensile flexibility and adhesion.

Deposited along the optimized stress trajectories, this composite acts as a strain-limiting framework that suppresses localized elasticity. Consequently, under the weight of subsequent clay deposition, the untreated “void” regions dynamically sag into parabolic vaults. This negative form-finding mechanism transforms a flat 2D pattern into a deeply articulated, 3D form-active topology with programmed bending stiffness.

29

TSO to Robotic Transition: Customized End Effector & Dynamic Sensor

Localized TSO Pattern

Topologically Constrained Tension/Compression Path to Robotic Tool Path

Robotic Experiment & Fabrication

Customized 6-Axis PMG Extruder

TSO Transition to Robotic Tool Path

Topologically Constrained PMG Network

Prototype 01:

30

Silicone Elastomer Membrane Polyurethane Fast-Cast Resin

Prototype 02:

CNC Knitted Textile Polymer-Modified Gypsum(PMG)

Dynamic Materiality: Fabric Formwork & Sensory Robotic Fabrication

MSD-RAS ROBOTICS & DESIGN Information Flow Diagram

Customized 6-Axis Sensory End Effector GRASSHOPPER

MACHINA

AAB IRB 4600

PHYTHON

DYNAMIC SURFACE

FIREFLY

ULTRASONIC SENSORS ARDUINO

IRB 4600 AAB AAB IRB 4600 ROBOT

MATERIAL TANK

WASP EXTRUDER WASP EXTRUDER

ARDUINOARDUINO UNO UNO

CUSTOMIZED NOZLE NOZZLE EXTENSION

ULTRASENSOR SONIC SENSOR ARRAY ULTRASONIC ARRAY

DYNAMIC FABRIC FORMWORK FABRIC FORMWORK

Dynamic Sensor Time Lapse

Original Toolpath Real-Time Sensory Toolpath Optimization

@ PennDesign Robotics Lab

31

Real-Time Closed-Loop Extrusion on Variably Stiffened Membranes Strain-Limiting Polymer Ribs

Stress Load

Prototype 01:

Silicone Elastomer Membrane Polyurethane Fast-Cast Resin

Prototype 02:

CNC Knitted Textile Polymer-Modified Gypsum(PMG) Polymer-Modified Gypsum (Dried) (Strain-Limiting Polymer Ribs - Inspired by TSO pattern)

Real-Time Sensory Scanning Timelapse Model

Computational Form-Finding: Non-Linear Membrane Deflection Kinetics

32

Dynamic Materiality: Fabric Formwork & Sensory Robotic Fabrication Panel Distribution & Robotic Tool Path

MSD-RAS ROBOTICS & DESIGN Fabric Formwork

Robotic Experiment & Fabrication

Dynamic Form-Finding and Robotics Fabrication

The structural integrity of robotic clay extrusion relies on stabilizing internal barrel pressure and volumetric flow. Segmented toolpaths cause frequent acceleration/deceleration, inducing material oozing and localized weak points at start-stop junctions. To resolve this, continuous, loop-based patterns are employed as a fabrication-led solution to maintain steady-state material rheology. This fluid, nonbreaking trajectory synchronizes robotic feed rate with extrusion velocity, eliminating nozzle retractions. Ultimately, this algorithmic continuity maximizes interlayer adhesion and mechanical interlocking, ensuring uniform load-bearing capacity across the facade.

33

Shell Prototype Experiment 03

Micro-Relif Robotic Toolpath

Shell Final Panel Kiln-Fired Ceramic Type B (72” x 60”)

Shell Final Panel Kiln-Fired Ceramic Type E (24” x 36”)

Glazing Prototype, Wall-installtion (48” x 42”)

TOBOR 0064 BRI BAA

REDURTXE PSAW

ONU ONIUDRA

YARRA ROSNES CINOSARTLU

NOISNETXE ELZZON

KROWMROF CIRBAF CIMANYD

Post-Production: Panel to Structure Connector

Assembly Concept

Double Side Wall Paneling Mock-up

Post-Production: Ceramic Glazing Test

The Autopoi

Dynamic Formwork Aggreg Turquoise Vitreous Glaze, Oxidi (Silicone Elastomer 72”x60”x

ietic Shells

gation of Ceramic Panels ized Copper Crystalline Glaze / Poly Resin Mold) x4 1/2”

The Autopo

Dynamic Formwork Aggre Chengdu Metro Line

oietic Shells

egation of Ceramic Panels e 18, Chengdu, China

Robotics + Design Design Technology

Selected Work by Deon Kim