CAD/CAM

Ph.D. Project (Sponsored by NCAIR-National center of aerospace innovation and research)

Efficient tool path planning strategies for CNC machining using the cloud of points

(Computational Algorithms, Modelling & Simulation, CNC Programming, Manufacturing Automation)

Freeform surfaces are represented as parametric equations or mesh based tessellated CAD models during design. In practice, there are situations such as styling, reverse engineering on organ (medical) modelling and worn out dies/molds wherein the exact surface equations or tessellated models are not available. Point cloud data is gathered from the actual parts using laser scanner to create the part geometry. In our study, we have designed and developed efficient CNC tool path planning strategies/software for machining of freeform surfaces directly from their representation in the form of point cloud without interim surface fitting. This approach has the advantages of reducing computational effort, increasing accuracy and shortening the product development cycle of a component during reverse engineering and in turn, reduce the costs.

The key findings from our research are as follows:

• The system generates a geometrically adaptive CNC tool path directly on the point cloud. No commercial CAM software does this as of now

• The strategies developed are robust and efficient in generating tool path directly from the point cloud

• Proposed method is novel and proven much superior to conventional methods and commercial system

• Validation of the developed system has been carried out through simulation and verified experimentally

• Reduces the tool path redundancy, improves productivity and part surface accuracy

• Focused upon application in reverse engineering, automation and integration of CAD to CAM

• Good impact on automobile, aerospace, dies/molds industry using multi-axis CNC machines

• The strategies are generic and primarily focused on 3-axis CNC machining. The work can be extended to 5-axis CNC machining and Additive Manufacturing