AI-based Approach for Predicting the Machinability under Consideration of Material Batch Deviations in Turning Processes

Abstract A significant difference in the machinability of hard-to-machine materials can be observed among different batches of the same specified material. Thus, for cost-efficient machining, the ideal point of operation may vary for each batch. To investigate this, more than 1000 experiments at different cutting conditions with ten material batches are carried out. This data is used to train a support vector machine, capable of predicting a machinability index based on the material batch and the cutting conditions. The derived model is implemented as a cloud-based service, enabling its integration into a smart manufacturing assistant. It can be used for process optimization by predicting the machinability for given situations and, thus, finding the point of operation with favorable machinability.