LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
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Abstract (arXiv)
In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, etc which are typically what most databases include. LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms. The curated paths are provided as a measure for removing biases in the paths generated by mechanisms that enable a more even design space representation. In this paper, we discuss the details of how we can generate such a large dataset and how we can overcome major issues with such scales. To be able to generate such a large dataset we introduce a new operator to generate 1-DOF mechanism topologies, furthermore, we take many steps to speed up slow simulations of mechanisms by vectorizing our simulations and parallelizing our simulator on a large number of threads, which leads to a simulation 800 times faster than the simple simulation algorithm. This is necessary given on average, 1 out of 500 candidates that are generated are valid~(and all must be simulated to determine their validity), which means billions of simulations must be performed for the generation of this dataset. Then we demonstrate the depth of our dataset through a bi-directional chamfer distance-based shape retrieval study where we show how our dataset can be used directly to find mechanisms that can trace paths very close to desired target paths.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 3 of its 37 references that are in the map. First posted on arXiv 30 Aug 2022.
Builds on
3 of 37 references.
References · 3
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
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Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 1.
9 papers.
- 2023Ship-D: Ship Hull Dataset for Design Optimization using Machine Learningcited by 12
- 2024LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesiscited by 7
- 2023DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applicationscited by 6
- 2025Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoningcited by 2
- 2024Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditionscited by 1
- 2024Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Designcited by 1
- 2025MechaFormer: Sequence Learning for Kinematic Mechanism Design Automationcited by 1
- 2025Creative synthesis of kinematic mechanismscited by 1
- 2026Discrete Autoregressive Transformer for Generative Mechanism Synthesiscited by 0
- 2026Discrete Autoregressive Transformer for Generative Mechanism Synthesis16 Jun 2026
- 2025Creative synthesis of kinematic mechanisms30 Sep 2025
- 2025MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation12 Aug 2025
- 2025Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning23 May 2025
- 2024Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design7 Oct 2024
- 2024LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis31 May 2024
- 2024Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions22 Feb 2024
- 2023DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications15 May 2023
- 2023Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning14 May 2023