Parameterized Modeling of the Energy Demand of Machining Processes as a Basis for Reusable Life Cycle Inventory Datasets

Abstract

Manufacturing processes have a significant contribution to energy consumption and related greenhouse gas (GHG) emissions in a product’s life cycle. Today, information on GHG emissions is increasingly demanded from companies in a life cycle perspective, based on the methodology of Life Cycle Assessment. Manufacturing companies supply producers of final products and are, therefore, requested to provide data on GHG of their manufacturing processes and resulting products. Obtaining such data for real-world manufacturing processes represents a huge effort. This challenge can be overcome with the use of a parameterized model, the Extended Energy Modeling Approach (EEMA), that has been developed for the machining process, which is a widespread industrial manufacturing process. The model calculates the total energy demand from power key values, which report the average power consumption of the constant and variable units of the machinery equipment, the consumer groups, as well as the different operating states of the equipment. Therefore, EEMA enables the reuse of a single measurement campaign for follow-up investigations of the specific machine tool, thereby significantly improving the efficiency of data acquisition for the calculation of the total energy demand and life-cycle-based GHG emissions. To use EEMA for the compilation of life cycle inventory datasets, methodological requirements were analyzed to derive a procedure for LCA-compliant datasets for machine tools. The key findings of applying the EEMA for the case study of a turning machine show that the constant consumer groups have a significant influence on the total energy demand. The share of the variable consumer groups in the total energy demand increases with increasing machine utilization but is always below 5%.