Modelling of Energy Requirements in Turning Operations

Abstract

Electrical energy is mainly derived from fossil fuels and carries a carbon dioxide (CO2) footprint. To improve energy effectiveness in and reduce the CO2 footprint of the manufacturing sector, an assessment of energy consumption in manufacturing processes is required. Such an assessment can reveal how energy consumption is distributed and inform energy reduction strategies. Improving machine tool performance has been a significant concern for machine tool manufacturers. As the performance of machine tools increases, the complexity of the design and electronic features correspondingly increases, leading to a rise in the amount of energy needed to operate the machines. Therefore, various strategies to reduce energy in manufacturing operations, especially in machining, have been developed and proposed in recent years, which encompass machine tool design, process planning and the optimisation of machining conditions. This research project contributes to the development of more comprehensive mathematical models to improve the integrity of energy modelling in machining and support better decision making. This research project led to several novel contributions to knowledge. First, new energy footprint models that take into account chips, cutting fluid and lubrication were developed. Accordingly, a comprehensive energy model for system-level analysis of machining processes was proposed, which included consideration of workpiece material, cutting tools, cutting fluid and lubricant oil to optimise the energy footprints in machining. In addition, an equation for an optimum tool life that satisfies the minimum energy requirements was developed to identify the optimum cutting parameters to reduce the environmental footprint of machining processes. Second, the impact of tool-wear progression on the specific cutting energy and hence the cutting power was captured and modelled. Third, the impact of the total energy saving attributed to the energy models developed in this research project was quantified, taking into consideration machine tools with different basic powers (impact of direct energy by machine tool) and cutting tools with different tool-life exponents (impact of embodied energy by cutting tool); the optimum cutting parameters were selected accordingly. Finally, the direct electrical energy consumption in machining was evaluated alongside the associated CO2 emissions in an international context. The thesis thus contributes to modelling the direct and embodied energy of machining operations and provides new insights into machining’s energy consumption, which can be used to drive energy reduction strategies. The energy models and data presented in this research project provide a foundation for developing software for the operation of energy-smart machining.