CloudNC Cutting Parameters

Owned by Geoffrey Brennan
Sep 11, 2024
2 min read

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CloudNC Cutting Parameters is used to calculate feeds and speeds for all toolpath strategies created when CAM Assist runs. Depending on the Tool Database configuration, this data will be used in the toolpaths generated. Cutting Parameters AI is a module for CAM Assist software that automatically recommends appropriate feeds and speeds for virtually any CNC machining scenario, in moments.

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Cutting Parameters AI resolves that problem. It allows users to easily set physics-based feeds and speeds for every toolpath - bearing in mind the cut geometry, tool, machine, workholding, material and more - in moments, directly in CAM. 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 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-first-time.  

Cutting Parameters AI is therefore the first feeds and speeds solution to provide users with a holistic understanding of the cutting environment and the constraints which apply to it, fully integrated within their existing CAM software packages and workflows. For most machinists, it substantially increases what it is possible for them to achieve with a CNC machine.

The models consider many aspects of the machining context, identifying and modelling factors that ultimately limit the machining process. These include:

  • A detailed three-dimensional model of cutting dynamics (forces acting on the tool and workpiece) which impact tool deflection, stresses, and process stability

  • Workpiece and tool material, which affect forces and temperatures generated at the rake face during the cutting process, and which impact wear rates

  • Tool / tool holder geometry and other attributes (for example run-out), which impact macro factors such as stiffness and geometric accuracy of the cutting process, down to micro factors such as the susceptibility of each cutting edge to chipping

  • Surface finish models, which often limit process speed for finishing usages

The model combines machine learning models and a detailed three-dimensional model of the physics of the cutting process, alongside a multi-dimensional constrained optimisation to calculate appropriate speeds and feeds across the full "design space" of possible input parameters for the tool, workpiece material and usage. This allows a global recommendation to be calculated.

The Cutting Parameters Explorer is an interactive visual representation of these variables, and provides a way to adjust the variables dynamically.