联动装置(软件)
四连杆机构
巴(单位)
平面的
路径(计算)
计算机科学
物理
人工智能
计算机视觉
几何学
数学
计算机图形学(图像)
生物化学
运动(物理)
基因
气象学
化学
程序设计语言
作者
Anar Nurizada,Rohit Dhaipule,Zhijie Lyu,Anurag Purwar
摘要
Abstract In recent years, there has been a strong interest in applying machine learning techniques to path synthesis of linkage mechanisms. However, progress has been stymied due to a scarcity of high-quality datasets. In this article, we present a comprehensive dataset comprising nearly three million samples of 4-, 6-, and 8-bar linkage mechanisms with open and closed coupler curves. Current machine learning approaches to path synthesis also lack standardized metrics for evaluating outcomes. To address this gap, we propose six key metrics to quantify results, providing a foundational framework for researchers to compare new models with existing ones. We also present a variational autoencoder-based model in conjunction with a k-nearest neighbor search approach to demonstrate the utility of our dataset. In the end, we provide example mechanisms that generate various curves along with a numerical evaluation of the proposed metrics.
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