Technological developments have made high-speed milling economically attractive. It is now a manufacturing technology that can competitively manufacture thin-walled parts. Such parts however can require a lot of material to be machined. With high-speed milling, this can take a lot of toolpaths. Process planning such products is difficult and time-consuming due to the vast amount of paths to program and the low stiffness of the final part. The workpiece at one point becomes the weakest element during machining, and its stiffness properties change as machining progresses. This thesis presents an error avoidance based approach for computer aided process planning for these parts, to help automate process planning and make it more reliable. The core of process planning thin-walled parts is ensuring that thin workpiece geometry is sufficiently supported at the point of machining. In the approach in this thesis, the support comes from remaining workpiece material. This makes the order of material removal crucial. Material removal strategies can be needed on different levels, depending on the scope of the thinness, and can differ for different shapes. This support-based planning has therefore been detailed differently on different levels, in a feature-based, knowledge-based form. To separate stiffness issues from machining process issues where possible, stiffness features are introduced in addition to machining features. Due to the nature of the parts and the process planning approach – process planning based on the support principle requires control over more or less the whole workpiece – manufacturing strategies need to consider a larger environment. This makes the strategies and knowledge to apply more complex. Therefore, it becomes considerably more difficult to increase the level of automation. The approach and concepts have been implemented into software, based on an existing feature-based, knowledge-based CAPP package. The core steps of planning the volumes to remove, how to machine them, and in which order, have been automated in a knowledge-based way. Also supplementary software utilities and functionality have been implemented. From evaluation of the resulting application for industrial practice, the automatic determination of the machining sequence for thin-walled geometry and the improved overview of the process plan were considered great benefits.
|Award date||2 Jun 2010|
|Place of Publication||Enschede|
|Publication status||Published - 2 Jun 2010|