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 .
Defining new feeds and speeds for CNC machining operations is an arduous and time consuming task , involving considerable manual experimentation .
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 .
Cutting Parameters AI is available now as a module for CloudNC ’ s CAM Assist solution , which is available today via www . cloudnc . com , and the Autodesk App Store .
For further information , please visit www . cloudnc . com
Issue 69 PECM 85