Using kernel data in machine tools for the indirect evaluation of surface roughness in vertical milling operations

The goal of this research is to compare the capabilities of kernel data and external sensor data, captured with piezoelectric accelerometers, for the indirect evaluation of surface roughness in vertical milling operations. Experiments were conducted to obtain data for developing algorithmic models that will be utilized to predict surface roughness. Seventy-two samples were used to develop two neural networks; one based on accelerometer inputs and the other on kernel inputs, and to compare the performance of the data source when calculating the average surface roughness parameter (Ra). Results show that accelerometer data and numerical control kernel (NCK) data can be useful for the indirect evaluation of average surface roughness as shown by a high correlation between outputs and targets. The main conclusion of this work is that when evaluating the average surface roughness parameter, it is more interesting to use the data obtained directly from the NCK than from external accelerometers.

► The performance of kernel data to make indirect evaluations of surface roughness.
► Data provided by NC kernel is useful for surface roughness identification.
► Data provided by accelerometers is useful for surface roughness identification.
► Using NC kernel data is more interesting than data provided by accelerometers.
► This work is a step towards the fully controlled and monitored milling process.