AMFG provides automated production scheduling tools
AMFG provides automated production scheduling tools
AMFG has made available a new Holistic Build Analysis tool for its automation additive manufacturing software to enable users to instantly estimate how full a machine build is without nesting.
The tool uses machines learning technology to provide instantaneous feedback, helping operators to better manage manufacturing processes and predict how cost-effective a part is to produce. It will also help companies to decide which jobs to prioritise, which means more streamlined processes.
Using this tool, parts can be assigned to a build and machine learning algorithms will then generate estimates of the build’s fill rate in quick time, representing it as a percentage. Taking into consideration variables such as deadlines, machine availability and optimal arrangement of parts, businesses can then make judgements on which part(s) to manufacture next. The alternative is to use nesting software packages which often require the user to set time limits before the process can be completed, which is more time-consuming.
“With Holistic Build Analysis, instead of waiting hours to see how full your build is, our customers can receive an accurate capacity estimation in only a matter of seconds,” stressed Felix Doerr, Head of Business Development at AMFG. “Our new tool is a radical alternative not only in terms of the time savings it delivers, but also because of its potential it has to change the way we optimise production scheduling for additive manufacturing.
“Our software’s estimations are becoming ever-more accurate thanks to our algorithms. Over time, we anticipate that automated build analysis and scheduling will become an integrated part of the end-to-end manufacturing process, taking additive manufacturing another step closer to a fully automated, autonomous manufacturing future.”
AMFG launched its automation additive manufacturing software after completing a brand name change from RP Platform earlier this summer. The aim is to tackle manual and unscalable workflows, taking advantage of machine learning algorithms to generate more efficient production processes. The development of the Holistic Build Analysis tool is a continuation of that mission.