PECM Issue 69 2024 | Page 84

New Cutting Parameters AI solution automatically generates appropriate physics-based precision machining feeds and speeds , transforming CNC machining

SOFTWARE & SYSTEMS CUTTING PARAMETERS AI

CLOUDNC
New Cutting Parameters AI solution automatically generates appropriate physics-based precision machining feeds and speeds , transforming CNC machining
CloudNC , the manufacturing technology company , has announced the release of Cutting Parameters AI , a new solution that automatically generates appropriate physics-based feeds and speeds for virtually any CNC machining scenario , in moments .
Defining new feeds and speeds for CNC machining operations is an arduous and time consuming task , involving considerable manual experimentation . As a result , many CAM programmers are forced to rely on a ‘ one-size fits all ’ approach towards machining components , instead of tailoring specific settings for every toolpath - resulting in lower productivity , inefficient cycle times , and sub-optimal surface finishes .
Cutting Parameters AI resolves that problem by employing models that allow users to easily set physicsbased feeds and speeds for every unique toolpath in moments , within their existing CAM software packages and workflows . With Cutting Parameters AI , the largest constraints to removing material faster in any unique cut are always visible to the machinist , enabling them to take immediate action to increase productivity .
In addition , Cutting Parameters AI can provide safe starting feeds and speeds for materials and with tools that the user has never worked with , dramatically increasing right-firsttime operations .
As a result , CloudNC expects users of Cutting Parameters AI - provided as a new module of its existing CAM Assist solution , which generates machining strategies for 3-axis and 3 + 2 axis components - to immediately benefit from instant cutting parameters tailored to any scenario , resulting in productivity optimisations of at least 20 % in their machining operations .
84 PECM Issue 69