EOS and 3YourMind Automate Identification of Parts with AM Part Identifier

The two companies are cooperating on the AM Part Identifier (AMPI) tool.

EOS and 3YourMind have collaborated to create the AM Part Identifier (AMPI), a tool that digitizes the analysis process and automatically delivers an analysis of component suitability for industrial 3D printing. Its aim is to scan an entire part inventory and provide a detailed report of parts that represent the highest potential gain by transitioning to industrial 3D printing.

After entering basic information about the parts that need to be produced, enterprise businesses can perform an automated scan of their entire inventory database. The analytic reports leverage experience from additive manufacturing experts to determine exactly which parts are best suited for industrial 3D printing. According to the developers, companies save time and money when they implement 3D inventory analysis by identifying the parts that are technologically viable and economically profitable. Additional tools to input information about in-house 3D production resources and external industrial 3D printing suppliers will further customize the component recommendations to the specific capacity.

For companies seeking to optimize the use of additive manufacturing and managing largevolumes of new and legacy parts, the AM Part Identifier can also be linked directly to a part database to receive updated reports in real time as additional parts are added or as advances in 3D printing technologies enable cost-effective production of components and materials. It enables enterprise and medium-sized companies to make smart decisions about where to begin implementing industrial 3D printing, the developers say.

“For several years we have helped our customers to identify the most suitable parts for industrial 3D printing," says Fabian Krauß, business development director at EOS. "We are very pleased to see that thanks to our cooperation with 3YourMind an automated software solution now enables a faster part identification than ever before.”

Related: