Development of a Structured Algorithm to Identify the Status of a Machine Tool to Improve Energy and Time Efficiencies

Abstract

Energy efficiency is a matter of concern for manufacturing industries due to ever increasing energy costs and strict environmental policies. Efficient monitoring of energy consumption of machine tools is first step towards energy conservation. This study presents a non-intrusive energy monitoring technique to quantify the energy consumption of a machine tool at the unit process level and determines the operational status of the machine tool using the energy data profile. A combination of K-nearest neighbors and principal component analysis is used to develop smart energy sensor which can determine the time and energy spent during each operational state of the machine tools. It also identifies the operational state of various machine tool components such as spindle, coolant pump, automatic tool changer, etc. The time and energy map provided by the proposed sensor will help the practitioners to identify the potential areas of energy and time saving.