论文标题
迈向自动化评估，利用深度学习
Towards automated Capability Assessment leveraging Deep Learning
论文作者
论文摘要
旨在提高制造业的经济效率，自动化程度提高是关键的推动力。但是，在专用过程中评估自动组装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确定。除其他外，自动化可行性的决定性标准是能够在最终位置分离和隔离组件自我对准的能力的能力。为了评估可行性，Fraunhofer研究人员已经制定并应用了基于问卷的评估计划。但是，结果在很大程度上取决于执行评估的单个工程师的内在知识和经验。本文介绍了NeuroCAD，这是一种软件工具，可以使用体素化技术自动化评估。该方法可以通过基于CAD文件进行深入学习来评估抽象和生产相关的几何特征。
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.
