Electrical Energy Demand in Mechanical Machining Processes

Abstract

Rising carbon dioxide emissions present a global grand challenge due to their impact on climate. Power generation is one of the major sources of CO2 emissions especially when carbon based fuel such as coal is used. Hence, the electricity that is used in homes and in manufacturing industry has an environmental burden attributable to CO2 emissions when it was generated at the power stations. In the UK on average, industry consumed 292 TWh of the electrical energy according to 2012 statistics. The rising cost of electricity in the UK coupled with the demand for eco-friendly consumer products requires a better understanding of energy demand in manufacture. In manufacturing, mechanical machining is one of the most widely used processes that consumed on average 38 TWh. This amounted to 13% of the average UK industrial energy demand and the reduction of energy intensity in this process is an area of current and urgent focus. In order to control electrical energy usage in mechanical machining, it is essential to understand electrical energy demand by machine tools and associated processes. This requires the development of mathematical models to predict electrical energy demand. The models will support selection of optimum machining process parameters to reduce direct energy demand and associated carbon footprint.Literatures reviewed indicate that energy demand modelling in machining was in its infancy and the integrity of electrical energy data needed to be significantly improved. In particular a number of energy studies had ignored the impact of feedrate, cutting velocity, depth of cut and tooling. It was further observed that where specific energy values were used these were assumed constant irrespective of the thickness of materials to be removed. The motivation for this research work was to improve the integrity of electrical energy demand modelling in mechanical machining addressing current limitations.Based on electrical energy monitoring in mechanical machining, the energy demand for machining processes was characterised. Building on the literature review and the concepts of "Basic and Tip" energy, a new and improved energy model was developed which addressed a number of limitations and omissions from existing models. The modelling of Tip energy is based on a specific energy and material removal rate. Having discovered that the impact of chip thickness had not been considered before in modelling specific energy a follow-on study undertook fundamental modelling of the specific energy as a function of chip thickness. This led to new generic equations for specific energy in machining. These models were developed based on machining of 3 common engineering materials. Furthermore, to raise the practical value of the models and data, the effect of tool wear on energy demand was studied and this was used to develop an improved understanding of the evolution of specific energy with tool wear. By linking the cutting mechanism to specific energy, the use of specific energy coefficients as a surrogate for defining energy efficient machining conditions was identified and is proposed in this thesis. The impact of machine tools on energy demand was investigated in a cooperative study between UK and Singapore. This enabled quantification of the impact of machine tools on energy efficiency and the net result on carbon dioxide footprint when both machine tool energy demand and national carbon emission signatures are considered.The research work provides significant advances in energy demand modelling, presenting new specific energy data for machining three different workpiece materials and 2 generic and novel methodologies and equations for (i) energy demand in machining, (ii) the effect of chip thickness on specific energy. It also for the first time suggests a unique methodology for defining and benchmarking the energy efficiency of cutting based on specific energy range. The energy models and data presented in the thesis provide a foundation

Date of Award	1 Aug 2014
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Paul Mativenga (Supervisor)