AMFG adds Holistic Build Analysis tool to its AM production management software

August 2, 2018

August 2, 2018

AMFG, London, UK, has introduced a Holistic Build Analysis tool into its production management software for Additive Manufacturing. The new tool is designed to enable users to instantly estimate the capacity of an AM machine build, and thereby optimise production scheduling, without using nesting.

“Accurately predicting the capacity of your machine builds is a vital part of the production planning process for Additive Manufacturing,” the company stated, “and yet, it’s an aspect of additive production that remains challenging for manufacturers.”

While nesting has been the primary method used by manufacturers to determine build capacity to-date, the company explained that nesting software offers an iterative solution; one which requires users to set time limits or an ideal density capacity before the nesting process is completed. Running nesting software in the background is said to be a very time-consuming process, and can take days or even hours to complete.

According to AMFG, its Holistic Build Analysis tool uses machine learning technology to produce accurate estimations of machine build capacity almost instantly. 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.

Optimising the production process at the planning stage can enable significant time savings as well as reduced production costs by enabling businesses to allocate the correct resources to a production facility at the right time. According to AMFG, its new tool factors in key variables such deadlines and optimal part arrangement to allow production teams to optimise their production scheduling, make judgements on which parts to manufacture next and pack builds based on a variety of factors.

As this new release represents the first phase of the Holistic Build Analysis tool’s implementation, AMFG stated that it is currently allowing its users to bypass the system’s estimates and manually fill in their own estimates where necessary. The system will use this manually inputted data to improve its estimates on a continuous basis, which the company expects to enable it to become more accurate in future.

August 2, 2018

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