Assessment of machine tool related environmental impacts for sustainable machining processes

In assessing the environmental impact or sustainability of machining processes and machined products, the characteristics of the machine tool should be considered. Difficulties in carrying out sustainability assessment include (1) collecting comprehensive input data, and (2) production and financial pressures preventing staff and machine availability to conduct in-depth analysis. Life cycle assessment reports are available for specific machine tools online, but these aren’t commonplace. This research investigates how to conduct sustainability assessments on machine tools, with reference to prior research literature, international standards and a new machine tool market study. Science-based and cost-based approaches are considered. A cost-based top-down approach is relatively quick, can meet regulatory needs and would provide reasonably accurate ‘bulk’ results across numerous manufacturing processes and machines. However capturing and scientifically modelling details (bottom-up) of individual machines can drive identification and prioritisation of improvements and innovations to reduce environmental impact and meet future targets. Based on a market study of 120 numerical control (NC) machine tools in this research, cost-derived calculations did not accurately correlate to individual machine tools’ sustainability metrics, due to reasons such as heterogeneity in the market. 55 different manufacturers were encountered in the study. Improved correlation represents an opportunity for future work. 15 years is a standard and commonly quoted value for the lifetime of a machine tool. Analysis of market data in this work indicated a machine tool life of double this value, around 30 years. From the study the average NC machine tool mass, volume and footprint dimension (length or width) were 6.6 metric tons, 23 m^3 and 2.8 m respectively. The monitoring of multiple sustainability-related metrics at high frequency on all machine tools would incur a high sensing and data management footprint, so a balance can be struck in terms of the assessment level, to maximise value-for-effort.