Roberto Teti

Digital factory technologies for robotic automation and enhanced manufacturing cell design

Abstract The fourth industrial revolution is characterised by the increased use of digital tools, allowing for the virtual representation of a real production environment at different levels, from the entire production plant to a single machine or a specific process or operation. In this framework, Digital Factory technologies, based on the employment of digital modelling and simulation tools, can be used for short-term analysis and validation of production control strategies or for medium term production planning or production system design/redesign. In this research work, a Digital Factory methodology is proposed to support the enhancement of an existing manufacturing cell for the fabrication of aircraft engine turbine vanes via robotic automation of its deburring station. To configure and verify the correct layout of the upgraded manufacturing cell with the aim to increase its performance in terms of resource utilization and throughput time, 3D Motion Simulation and Discrete Event Simulation are jointly employed for the modeling and simulation of different cell settings for proper layout configuration, safe motion planning and resource utilization improvement. Validation of the simulation model is carried out by collecting actual data from the physical reconfigured manufacturing cell and comparing these data to the model forecast with the aim to adapt the digital model accordingly to closely represent the physical manufacturing system.

Analysis of Force Signals for the Estimation of Surface Roughness during Robot-Assisted Polishing

In this study feature extraction of force signals detected during robot-assisted polishing processes was carried out to estimate the surface roughness during the process. The purpose was to collect significant features from the signal that allow the determination of the end point of the polishing process based on surface roughness. For this objective, dry polishing turning tests were performed on a Robot-Assisted Polishing (RAP) machine (STRECON NanoRAP 200) during three polishing sessions, using the same polishing conditions. Along the tests, force signals were acquired and offline surface roughness measurements were taken at the end of each polishing session. As a main conclusion, it can be affirmed, regarding the force signal, that features extracted from both time and frequency domains are valuable data for the estimation of surface roughness.