Can the hanger a cloth simulation designed be 3D-printed, and does it hold a garment?

What it found

Exports hanger-fit’s adjustable shape and four-width rack as parametric STL and OpenSCAD, with a cantilever strength, deflection and print-bed check. Printed flat in PLA under assumed constants (1.5 kg garment, 2× motion factor), the round-ended arm (end radius 2.86 cm) has a safety factor of 40–70 at 12 mm depth and bends under 0.04 mm, so strength never limits it; a wire-like arm (end radius 1.5 mm) has a safety factor of 0.13 and fails. No hanger of useful width fits a 220 mm bed whole, but one arm with its hub does up to 24.8 cm. Stylised: assumed material constants, flat extrusion, OpenSCAD files not rendered, no two-piece joint.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Laundry-hanging robots

• How should a laundry robot pick one garment out of a tangled dryer load?How should a laundry robot pick one garment out of a tangled dryer load? In a stylised disc-pile model aiming at a visible opening takes 13.4 assumed seconds per garment against 22.7 for pinching the top and regrasping, and pushing garments aside first is slower (17.7 s); one arm then serves 151, 110 and 127 items an hour. Assumed times, not a robot.
• How long may a laundry robot spend finding a garment's opening before blind pinching is faster?How long may a laundry robot spend finding a garment's opening before blind pinching is faster? On paired piles of the stylised pick model, aiming wins only if the detector costs under about 9 s per pick (95% interval 8.9 to 9.6, assumed times); the limit falls as regrasps get likelier to succeed or more openings are hidden. Stylised.
• How often may a laundry robot's opening detector aim wrong before blind pinching is faster?How often may a laundry robot's opening detector aim wrong before blind pinching is faster? In the stylised hanger-pick model each 10 points of detector error cost about 1 s of look-time budget: 9.2 s per pick when perfect, 4.5 s at 50% wrong, none when always wrong. Stylised.
• Does the 2D hanger result survive in 3D, with front and back panels and a hanger arm of finite depth?Does the 2D hanger result survive in 3D? With an arm as deep as the garment a 3D cloth sheet matches the 2D drape (within 0.05 cm for round-ended hangers) and the best width is again 1 cm over the half shoulder; a shallow arm lets the front and back panels fall 5–8 cm and raises a wooden hanger's error from 0.4 to 2.0 cm. Stylised.
• Does a 3D-designed hanger keep improving when the end-radius cap is lifted past 6 cm?No: with caps of 10 and 15 cm the whole-sheet error is 1.08 and 1.21 cm against 1.12 cm at 6 cm (8 seeds each, p = 0.77 and 0.57), so the optimum sat on a flat ridge. Stylised.
• Is a hanger designed in 3D really better than one designed on the 2D centre line, once seed noise is counted?Yes, by 0.26 cm on the whole sheet with hanger-fit's bounds (8 seeds each, p = 0.03) and by 0.68 cm once the end-radius and bow limits are widened (p < 0.001), but the 3D-designed hangers have a worse centre line. Stylised.

All 25 Laundry-hanging robots projects →

Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.