Towards automated Capability Assessment leveraging Deep Learning

TL;DR

This paper introduces NeuroCAD, a deep learning-based software tool that automates the assessment of manufacturing automation feasibility from CAD geometries, reducing reliance on expert judgment.

Contribution

The paper presents NeuroCAD, a novel deep learning approach utilizing voxelization of CAD files to automate capability assessment in manufacturing automation.

Findings

NeuroCAD effectively automates feasibility assessments.

Deep learning accurately interprets CAD geometries.

Reduces dependency on expert evaluations.

Abstract

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.