A DIGITAL TWIN ASSISTED FRAMEWORK FOR QUALITY ASSURANCE IN MOULD MANUFACTURING

This paper discusses a Digital Twin framework for quality assurance in mould manufacturing consisting of the physical and virtual manufacturing process connected by a data lake. To this end, we present the DT framework and demonstrate its applicability at the example of ensuring the quality of milling features on a mould tool part. For the demonstration we build two cascaded models, a tool wear state model and an insert part quality model. The tool wear state model assigns a label corresponding to the tool wear state using cutting force measurements. The part quality model then uses the tool state and engineering data to classify quality of individual milling features on the mould tool part. To develop the cascaded models, we conducted a case study which experimentally collected machine controller data and cutting forces using a Kistler dynamometer for machining a test part on a Makino V33i three-axis vertical milling machine. The test part contains mould tool specific features and is made of hardened tool steel (46-52 HRC). Three milling tools were repeatedly used to machine test parts and gather data at different tool wear states. For validating the part quality model, we further collected metrology data from a coordinate measurement machine. Results show that the developed cascaded models are able to monitor the tool wear stage and to classify quality deviations with a weighted F1 measure of 89.0%.