Abstract
We investigate the parallel assembly of two-dimensional, geometrically-closed modular target structures out of homogeneous sets of macroscopic components of varying anisotropy. The yield predicted by a chemical reaction network (CRN)-based model is quantitatively shown to reproduce experimental results over a large set of conditions. Scaling laws for parallel assembling systems are then derived from the model. By extending the validity of the CRN-based modelling, this work prompts analysis and solutions to the incompatible substructure problem.
Highlights
Large-scale manufacturing requires fast and efficient fabrication of many exact copies of desired objects
We present a comprehensive study of modelbased prediction of assembly yield for parallel assembling systems
Hosokawa et al described the negative impact of incompatible substructures on assembly yield in their experiments, and first proposed to study their parallel assembly system using the formalism of chemical reaction networks
Summary
Large-scale manufacturing requires fast and efficient fabrication of many exact copies of desired objects. Robot assisted fabrication typically involves serial, deterministic procedures that are reliable but inefficient to assemble vast quantities of products, especially those of small size.[1,2] An alternative manufacturing strategy consists of components assembling with one another autonomously to form many equal copies of a target structure. Such a self-assembly approach[3] is massively parallel and inspired by natural systems that assemble autonomously, such as crystals[4] and viruses.[5]. Hosokawa et al described the negative impact of incompatible substructures on assembly yield in their experiments, and first proposed to study their parallel assembly system using the formalism of chemical reaction networks
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