SOFTWARE TECH NEWS

AMFG Enhances its AM Execution System with New Build Preparation Solution

Additive Manufacturing Workflow
Additive Manufacturing Workflow
Above: AMFG’s Build Preparation tool/Image Credit: AMFG

London-based software company, AMFG, has announced the release of its new Build Preparation tool to its Additive Manufacturing Execution System (MES). The new solution allows users of AMFG’s MES software to better optimise print builds for end-use applications and automate the build preparation process.

With the Build Preparation tool, users are able to visualise and orient parts on a build, as well as set optimal build parameters to ensure the repeatable production of the finished part.

Since it is integrated with the software’s production scheduling and quality assurance management tools, users are also able to schedule approved builds for production and check that they meet the required quality standards directly within the platform. To ensure traceability, any changes to the build parameters are tracked by the software, with authorised users automatically notified when changes are made.

“Repeatability and traceability are critical requirements for any manufacturer looking to leverage AM for end-use parts,” says Keyvan Karimi, CEO of AMFG. “An integrated sequence of production steps is required to achieve this — and build preparation is an integral part of this process. However, it’s a step that has traditionally been disconnected from the rest of the workflow.

“Instead of viewing build preparation as an isolated step, we’ve integrated it into our MES software so that our customers can manage more of their production processes seamlessly and ensure greater repeatability using our software. There is also an additional level of traceability, as all of the data collected throughout the lifecycle of a part is tracked by the system and can be accessed when necessary.”

The release of AMFG’s Build Preparation tool follows the company’s announcement of its partnerships with EOS and Autodesk in November last year, both of which aim to enable greater connectivity across the additive manufacturing workflow.


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