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 physics-based 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-first-time 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.
Theo Saville, co-founder and CEO of CloudNC, said: “Cutting Parameters AI is the first solution to automatically provide sensible feeds and speeds that can be applied in virtually any machining scenario, by a user of any ability level. It’s a step change in accelerating one of the most time consuming, tricky aspects of machining and will substantially reduce the time that CAM users spend setting up, while also substantially increasing what it’s possible for them to achieve with a CNC machine.”
When making new components with a CNC machine, there are so many factors to consider when selecting feeds and speeds that determining the best option is very time consuming for an experienced CAM engineer, and bewildering for someone new to the industry.
Cutting parameters that are too aggressive cost money through broken or worn out tools and scrapped parts. Equally, sticking to a conservative, safe range of cutting speeds leaves time and money on the table with slow toolpaths.
Furthermore, what are good cutting parameters for one toolpath may be less suitable for other toolpaths - but programming different parameters for every operation is too intricate and difficult for all but the largest batch sizes. Additionally, introducing new types of tooling (or materials) comes with the overhead of creating presets and populating the data into CAM software.
Cutting Parameters AI resolves those problems by applying AI. When using the software, the physics model immediately recommends appropriate feeds and speeds by combining both its embedded domain knowledge and an understanding of the cutting context.
It identifies and models factors that ultimately limit the machining process, including cutting dynamics, workpiece and tool material, tool holder geometry, and surface finish models. It then combines machine learning models and a detailed three-dimensional model of the physics of the cutting process to provide a recommendation to the user.
The user interface also allows the applicable constraints to be configured in a flexible and intuitive way, allowing the user to rapidly reach a recommendation tailored to their specific usage and specifications.
