Tag Archives: PLM

Unlock material intelligence for PLM

GRANTA MI:Materials Gateway for Creo

GRANTA MI:Materials Gateway for Creo – an upgrade option that enables Creo users to access their corporate materials database within Creo

 

Manufacturing organizations are increasingly recognizing that the critical IP they have developed in relation to the engineering materials that they use needs to be managed in a comprehensive and cohesive way. In our latest blog post over at #LiveWorx, we look how to more effectively digitalize and then apply this evolving materials information, in order to save time and cost, drive innovation, and reduce risk.

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PLM Integrated Material Intelligence: Can We Get the Best of Both Worlds?

PLM Integrated Material Intelligence: Can We Get the Best of Both Worlds? It’s an interesting question, posed in a recent blog post by Siemens PLM Software. 

The post begins by highlighting a very important point: consistency should rule where materials data management is concerned. It goes on to highlight that there’s too much scope for error if an engineer in one part of a company works off a different material definition from a designer somewhere else. But, at the same time, companies need depth. They’re looking to squeeze every drop from the rich materials data available to them.

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Materials data, Additive Manufacturing, and magic at Siemens PLM event

MII attended the Siemens PLM Connection event in Berlin last week – a gathering of over 1,000 users of engineering and product lifecycle software applications such as Teamcenter, Simcenter, and NX. Aside from the very entertaining iPad magician at the gala dinner, two things struck me from the conference sessions and discussions with other delegates.

The first was the emphasis on Additive Manufacturing (AM), with Siemens PLM launching new capabilities such as topology optimization for additive applications. There was a strong sense from attendees that this is a technology coming into its own, and an interest in how it applies to them. Of course, data about materials, processing parameters, and the relationship between the two is vital to developing effective AM.

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