This study discusses the high speed turning of the hardened mold steel by ceramic cutting
tools. From the experiments, we can understand the tool wear condition, tool failure mode and the
surface roughness variation of the workpiece.
In order to understand the tool wear and surface roughness characteristics during the high speed
turning process of the hardened mold steel by ceramic cutting tools, the polynomial network was
used to construct the tool wear and surface roughness prediction model. The polynomial network is
constituted of several function nodes; these function nodes can be self-organizing into the optimal
network structures according to the predicted square error (PSE) criteria.
It is shown that the polynomial network can correctly correlate the input variables (cutting speed,
feed rate and cutting time) with the output variables (tool wear and surface roughness). Based on
the tool wear and surface roughness prediction model constructed, the wear amount of the ceramic
cutting tools and the surface roughness of the workpiece can be predicted with reasonable accuracy
if the turning conditions are given and it is also consistent with the experimental results very well.
The manufacturing engineers can then , according to the prediction results, execute the process
planning, decide the manufacturing process and the tool change time, thus preventing the cutting
tool from being over-worn or failing when it is in use.
Surface Roughness Prediction in High Speed Turning of Ti-6Al-4V: A Comparison of Techniques
