Innovative digitally enhanced technologies are revolutionising the manufacturing systems, including the metal forming sector, at an unprecedented speed and scale. By utilising voluminous metadata collected through sensing networks and experimentally verified simulations during daily production and academic activities, profound insights can be provided to facilitate the ongoing efforts on understanding metal forming processes from the perspective of data. Data science in metal forming has emerged as an interdisciplinary research challenge that requires the knowledge of both manufacturing and data science. To unveil the distinctive thermo-mechanical characteristics of metal forming processes, for example the hot stamping process, the Digital Characteristics (DC), defined as the metadata visualisation that contains essential information spanning over the design, manufacturing and application of the manufactured products, were developed for different geometric features based on a vast number of hot stamping metadata. The analysis on distinctive hot stamping DC led to the development of a digitally enhanced interactive friction model considering complex, transient contact conditions incorporating evolutionary interfacial thermo-mechanical characteristics, such as interfacial temperature, contact pressure and sliding speed. The lubricant limit diagram (LLD) was developed demonstrated by coefficient of friction (COF) and performance grade (PG) to enable the quantitative evaluation of different lubricants considering different geometric features of hot-stamped components. The results advanced the understanding of lubricant performance and further demonstrated a novel methodology that integrates the development of DC in the hot stamping process, data-guided LLD, and a quantitative evaluation of lubricant performance. With the advancements in DC and LLD, a new framework can be established to characterise alternative applications in other forming processes that demand rigorous lubricant evaluation.
Read full abstract