Predictive Modelling for Energy Consumption in Machining using Artificial Neural Network

The energy efficiency is important evaluation criterion for new investment in machinery and equipment in addition to the classical parameters accuracy, performance, cost and reliability. Even the users in the automotive industry demand new acquisitions of energy consumed by a machine tool during machining. Large interrelated parameters that influence the energy consumption of a machine tool make the development of an appropriate predictive model a very difficult task. In this paper, a real machining experiment is referred to investigate the capability of artificial neural network model for predicting the value of energy consumption. Results indicate that the model proposed in the research is capable of predicting the energy consumption. The present scenario demands such type of models so that the acceptability of prediction models can be raised and can be applied in sustainable process planning during the manufacturing phase of life cycle of a machine tool. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of CIRPe 2015 - Understanding the life cycle implications of manufacturing