Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement

Abstract

Nowadays, injection molds are manually designed by humans using computer-aided design (CAD) systems. The placement of ejector pins is a critical step in the injection mold design to enable demolding complex parts in the production. Since each injection mold is unique, designers are limited in using standard ejector layouts or previous mold designs, which results in high design time so that an automation of the design process is needed. For such a system, human knowledge is essential. Therefore, we propose a human-centric machine learning (HCML) approach for the automatic placement of ejector pins for injection molds. In this work, we extract mental models of injection mold designers to obtain machine-readable fundamental design rules and train a machine learning model using an ongoing human-machine learning approach.