Computational design of fabric formwork

Zhang, Xiaoting; Fang, Guoxin; Skouras, Mélina; Gieseler, Gwenda; Wang, Charlie C. L.; Whiting, Emily

doi:10.1145/3306346.3322988

Cited by 15 publications

(8 citation statements)

References 72 publications

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“…Recent work includes optimization of composite molds for casting [Alderighi et al 2019], tool paths for 3D printing [Etienne et al 2019;Zhao et al 2016], and decomposition for CNC milling [Mahdavi-Amiri et al 2020;Yang et al 2020]. While some of these methods minimize the distance to a target design under fabrication constraints [Duenser et al 2020;Zhang et al 2019], none of them explores a space of design modification to minimize fabrication cost.…”

Section: Related Workmentioning

confidence: 99%

Co-Optimization of Design and Fabrication Plans for Carpentry

et al. 2022

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Past work on optimizing fabrication plans given a carpentry design can provide Pareto-optimal plans trading off between material waste, fabrication time, precision, and other considerations. However, when developing fabrication plans, experts rarely restrict to a single design , instead considering families of design variations , sometimes adjusting designs to simplify fabrication. Jointly exploring the design and fabrication plan spaces for each design is intractable using current techniques. We present a new approach to jointly optimize design and fabrication plans for carpentered objects. To make this bi-level optimization tractable, we adapt recent work from program synthesis based on equality graphs (e-graphs), which encode sets of equivalent programs. Our insight is that subproblems within our bi-level problem share significant substructures. By representing both designs and fabrication plans in a new bag of parts (BOP) e-graph, we amortize the cost of optimizing design components shared among multiple candidates. Even using BOP e-graphs, the optimization space grows quickly in practice. Hence, we also show how a feedback-guided search strategy dubbed Iterative Contraction and Expansion on E-graphs (ICEE) can keep the size of the e-graph manageable and direct the search towards promising candidates. We illustrate the advantages of our pipeline through examples from the carpentry domain.

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Section: Related Workmentioning

confidence: 99%

Co-Optimization of Design and Fabrication Plans for Carpentry

et al. 2022

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“…In their work, Zhang et al . [ZFS*19] propose the first computational method to automate the design of fabric formworks for casting 3D free‐form shapes (Figure 14). In particular, they propose a formulation to find the optimal fabric panel design and casting directions as an inverse design problem.…”

Section: Alternative Mouldsmentioning

confidence: 99%

“…Zhang et al . [ZFS*19] propose a method for the automatic generation of fabric formworks for free‐form shapes. The method optimizes the fabric patterns that, once sewn together, will form the flexible formwork for the target shape.…”

Section: Alternative Mouldsmentioning

confidence: 99%

State of the Art in Computational Mould Design

Alderighi

Malomo

Auzinger³

et al. 2022

Computer Graphics Forum

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Moulding refers to a set of manufacturing techniques in which a mould, usually a cavity or a solid frame, is used to shape a liquid or pliable material into an object of the desired shape. The popularity of moulding comes from its effectiveness, scalability and versatility in terms of employed materials. Its relevance as a fabrication process is demonstrated by the extensive literature covering different aspects related to mould design, from material flow simulation to the automation of mould geometry design. In this state-of-the-art report, we provide an extensive review of the automatic methods for the design of moulds, focusing on contributions from a geometric perspective. We classify existing mould design methods based on their computational approach and the nature of their target moulding process. We summarize the relationships between computational approaches and moulding techniques, highlighting their strengths and limitations. Finally, we discuss potential future research directions.

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“…Small structures [Guseinov et al 2017] and layouts of inextensible polymer [Pérez et al 2017] are 3D printed on stretched fabrics to form a desired shape after releasing stretch. Planar panels of fabric containers inflated by pressurized air [Skouras et al 2014] and pressures of injected viscous fluid [Zhang et al 2019] are inversely designed using orthotropic material properties of fabrics. The same material model is employed in our physics-based inverse design, where the material parameters are measured from physically fabricated SJJ patterns.…”

Section: Related Workmentioning

confidence: 99%

Knitting 4D garments with elasticity controlled for body motion

Han

Zhang²,

Chen³

et al. 2021

ACM Trans. Graph.

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In this paper, we present a new computational pipeline for designing and fabricating 4D garments as knitwear that considers comfort during body movement. This is achieved by careful control of elasticity distribution to reduce uncomfortable pressure and unwanted sliding caused by body motion. We exploit the ability to knit patterns in different elastic levels by single-jersey jacquard (SJJ) with two yarns. We design the distribution of elasticity for a garment by physics-based computation, the optimized elasticity on the garment is then converted into instructions for a digital knitting machine by two algorithms proposed in this paper. Specifically, a graph-based algorithm is proposed to generate knittable stitch meshes that can accurately capture the 3D shape of a garment, and a tiling algorithm is employed to assign SJJ patterns on the stitch mesh to realize the designed distribution of elasticity. The effectiveness of our approach is verified on simulation results and on specimens physically fabricated by knitting machines.

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Computational design of fabric formwork

Cited by 15 publications

References 72 publications

Co-Optimization of Design and Fabrication Plans for Carpentry

Co-Optimization of Design and Fabrication Plans for Carpentry

State of the Art in Computational Mould Design

Knitting 4D garments with elasticity controlled for body motion

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