The manufacturing sector is one of the significant consumers of electricity, with about 42.3% (8249 TWh) of the global electricity consumption attributable to this sector. This electricity is generated from fossil fuels at the power stations, resulting in increased CO2 emission and subsequently global warming. Thus, energy efficiency could play a vital role in reducing electrical energy demand and environmental impacts in the manufacturing sector.Mechanical machining is one of the widely used techniques in manufacturing. Machine tools consist of auxiliary units, spindle, feed axes including the x-axis, y-axis, z-axis, and the tool change system which are the main electrical energy consumers. The feed axes control the relative motion between the workpiece and cutter, and also determine the workpiece geometry. In literature, a number of studies focused on the machining process as a whole, while the energy demand for axis and toolpaths was relatively unexplored. This PhD research was aimed at assessing the electrical energy demand in mechanical machining, focusing on feed motions and toolpaths in order to identify energy saving strategies of the machine tool. To achieve this, a current measurement device was used to acquire the current and voltage, from which the power and electrical energy requirements were evaluated. This study included (i) energy consumption analyses of the machine tool in different feed axes directions, (ii) cutting of components in different axes orientations (iii) and electrical energy demand studies of different toolpath strategies. From the study, a new method and model for predicting the electrical energy demand of feed axes was developed. This model encompasses the weights of feed axes, machine tool vice, and workpiece placed on the machine table. Moreover, the newly developed feed axes energy demand model was integrated into other energy consumption models to predict the energy demand for toolpaths. CNC toolpaths are generated manually or by computer aided manufacturing (CAM). Enabling an energy rating of CNC toolpaths is vital to be able to quantify energy demand, compare toolpaths, and develop energy demand reduction strategies.The results show that machining along the x-axis which carries minimal weights significantly reduced the energy demand of the feed drive, which in turn reduces the non-cutting energy demand of the machine tool. Thus, this Thesis contributes to the improvement of energy efficiency in machining through the development of a new and novel model and method for predicting the feed axes energy demand; determining the most efficient axes and component orientation; as well as the most efficient toolpath strategy for minimal energy demand in machining. This PhD Thesis has laid the foundation model and information source for a post processor to estimate energy demand from CNC toolpaths. Such a capability was not available in CAM software or on CNC machines.