Tiziana Segreto

TL;DR: In this paper, a multiple sensor monitoring system comprising cutting force, acoustic emission and vibration sensing units was employed for tool state assessment during turning of Inconel 718 nickel alloy.

TL;DR: A cloud manufacturing framework is developed to realize on-line smart process monitoring in the machining of difficult-to-machine materials through cognitive paradigms able to learn from sensorial data.

TL;DR: In this article, the authors draw on the activities of the CIRP Collaborative Work on "Round Robin on Chip Form Monitoring" carried out within the Scientific-Technical Committee Cutting (STC-C), which involved the following main round robin activities: (a) generation, detection, storage and exchange of cutting force sensor signals obtained at different Laboratories during sensor-based monitoring of machining processes with variable cutting conditions yielding diverse chip forms, and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies.

TL;DR: In this article, a Principal Component Analysis (PCA) algorithm was implemented to extract characteristic features from acquired sensor signals and a pattern recognition decision-making support system was performed by inputting the extracted features into feed-forward back-propagation neural networks aimed at single chip form classification and favorable/unfavourable chip type identification.

TL;DR: In this paper, a cognitive modelling of tool wear progress based on neural network supervised training is employed to obtain a dependable trend of tool wearing curves for optimal utilisation of tool life and step increase of productivity, while preserving the surface integrity of the machined parts.