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
Machine Shop Excellence Your 7 step roadmap to transformation
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 via the Cloud NC website, and the Autodesk App Store. n www.cloudnc.com
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