Smart manufacturing platform based on input-output empirical relationships for process monitoring

Abstract

Intelligent monitoring and maintenance protocols are undoubtedly crucial for improving manufacturing processes. Accordingly, machine learning techniques and predictive control models have been customized and optimized to account for the specific characteristics of the processes under investigation. In this context, the management of manufacturing processes in a “smart way” requires the development of specific models based on input-output empirical data. The aim of the proposed research was to develop an easily customizable application integrated into a milling process executed at the laboratory level. The application was designed to identify and record the operator, the order and the specific work sequences. It also supports the operator in setting processing parameters according to the type of work sequence to be performed. The application analyses specific process outputs, such as the wear growth on the inserts of the cutter in relation to the main input process parameters: depth of cut, feed rate, and spindle speed. This analysis is implemented by leveraging empirical evidence.