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. Feature extraction was realised by processing the detected sensor signals in order to reduce the high dimensionality of the sensorial data. The extracted features were fused to realise a sensor fusion methodology based on neural network pattern recognition for decision making on tool wear condition

This is an open access article published by Elsevier and distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/.One third of energy consumption is attributable to the industrial sector, with as much as half ultimately wasted as heat. Consequently, research has focused on technologies for harvesting this waste heat energy, however, the adoption of such technologies can be costly with long payback time. A decision support tool is presented which computes the compatibility of waste heat source(s) and sink(s), namely the exergy balance and temporal availability, along with economic and environmental benefits of available heat exchanger technologies to propose a streamlined and optimised heat recovery strategy. Substantial improvement in plant energy efficiency together with reduction in the payback time for heat recovery has been demonstrated in the included case study