Towards automated Capability Assessment leveraging Deep Learning
This work addresses the need for more objective and efficient automation feasibility assessments in manufacturing, though it appears incremental as it builds on existing evaluation schemes.
The paper tackles the problem of automating the assessment of technical feasibility for assembly automation in manufacturing, which traditionally relies on expert knowledge, by introducing NeuroCAD, a software tool that uses voxelization and deep learning on CAD files to evaluate geometric features.
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.