In manufacturing, there is increasing recognition of the need to increase energy efficiency, both in order to reduce process cost and improve carbon footprint. To achieve this, it is necessary to understand how manufacturing systems use energy directly and indirectly. These types of analysis have been carried out at the process level for traditional machining processes, as well as at the factory level to understand macro-energy flows and bottlenecks. Other researchers have accomplished considerable energy optimisation work for laser processing. However, the emphasis of this work has been on the optimisation of the laser-material interaction. This focus has overlooked the whole system viewpoint and the significance of supporting equipment.

Laser welding, using a 300W fibre laser, was chosen as the subject for this study due to its ubiquity in many high-value manufacturing industries, and its potential as a gateway into other manufacturing processes, such as Directed Energy Deposition (DED) and Additive Manufacturing (AM). In this paper, the initial work produced a framework for categorising the process states and sub-systems found in a standard or generic laser machine tool. An electrical energy meter was used to measure the energy consumption for individual sub-systems when creating autogenous weld tracks in 316L stainless steel.

Analysis of these data showed that the laser is only 18% of the total power consumption, the most significant being the water cooling sub-system (37%). Metallurgical analysis of the welds was also performed, which correlated laser beam power with the mass of material processed. This provided a direct analysis between the laser melted material and the total electrical energy consumption of the system.

Reported for the first time is a complete analysis of laser welding energy efficiency at a system level. This primary analysis of current equipment typical energy consumption can be used to identify future strategies for energy efficiency improvements beyond the direct laser-material interaction. By focusing on the most energy-inefficient parts of the system, the greatest potential for improvements to the carbon-footprint of laser processing can be quantified.