利用深度学习实现自动化能力评估
Towards automated Capability Assessment leveraging Deep Learning
摘要：
为了提高制造业的经济效率，提高自动化程度是一个关键因素。然而，评估专用工艺的自动化装配解决方案的技术可行性是困难的，并且通常由给定产品零件的几何形状决定。其中，自动化可行性的决定性标准是分离和隔离单个部件的能力或在最终位置的部件自对准的能力。为了评估的可行性，一个基于问卷的评估方案已经开发和应用弗劳恩霍夫研究人员。然而，结果强烈地依赖于执行评估的单个工程师的隐含知识和经验。本文介绍了NeuroCAD，一个软件工具，使用体素化技术自动化的评估。该方法可以通过基于CAD文件的深度学习来评估抽象和生产相关的几何特征。
英文摘要：
Aiming for a higher economic efficiency in manufacturing, an increased degree of automation is a key enabler. However, assessing the technical feasibility of an automated assembly solution for a dedicated process is difficult and often determined by the geometry of the given product parts. Among others, decisive criterions of the automation feasibility are the ability to separate and isolate single parts or the capability of component self-alignment in final position. To assess the feasibility, a questionnaire based evaluation scheme has been developed and applied by Fraunhofer researchers. However, the results strongly depend on the implicit knowledge and experience of the single engineer performing the assessment. This paper presents NeuroCAD, a software tool that automates the assessment using voxelization techniques. The approach enables the assessment of abstract and production relevant geometries features through deep-learning based on CAD files.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
