Investigating the performance of 410, PH13-8Mo and 300M steels in a turning process with a focus on surface finish

This study generated novel behavioural data for three engineering steels undergoing a turning process. The materials were hardened 410, PH13-8Mo and 300M, two stainless steels and one high strength steel respectively. A primary aim was obtaining low machined surface roughness. A surface finish investigation compared tool geometries and tool materials. Multi-response cutting parameter screening was undertaken using a novel trade study and iteration method, where the calculated cut quality was used to identify better feed rates and surface speeds. In addition the sub-surface machined microstructure was examined. Tools with a small nose radius produced the roughest surfaces. A surface roughness below 0.4 μm Ra could be consistently achieved on all three materials using rhombic wiper inserts and a feed rate up to 0.1 mm/rev. PH13-8Mo had the lowest machined surface roughness, as low as 0.11 μm in terms of Ra. In the parameter screening stage a generalised recommendation for good cut quality was a surface speed of at least 120 m/min and a feed rate of 0.088 mm/rev. The microstructure examination showed that for all materials under the conditions tested, there was no evidence of white amorphous layer formation and there was grain deformation for the 410 material only

Model-based Validation of Streaming Data

An approach is developed where functions are used in a data stream management system to continuously validate data streaming from industrial equipment based on mathematical models of the expected behavior of the equipment. The models are expressed declaratively using a data stream query language. To validate and detect abnormality in data streams, a model can be defined either as an analytical model in terms of functions over sensor measurements or be based on learning a statistical model of the expected behavior of the streams during training sessions. It is shown how parallel data stream processing enables equipment validation based on expensive models while scaling the number of sensor streams without causing increasing delays. The paper presents two demonstrators based on industrial cases and scenarios where the approach has been implemented