QIF for Integrated Information and Improved Quality

The digitalization of manufacturing means that data is being produced at every stage of the process chain, providing the opportunity to make improved decisions for process improvement. Reliable, traceable and accurate data is the key to the success of these decisions. Measurement is, therefore, becoming integrated into production processes as a trusted source of data. In-line, in-process, and end of line inspection can assist engineering, and potentially feedback to design and manufacturing.

The traditional method of product definition via 2D engineering drawings, is not conducive to traceable multi-stage measurement processes, resulting in significant duplication of effort downstream. Model-Based Definition (MBD) and the concept of the digital thread provides a more appropriate scenario. It allows machine readable design requirements to be re-used downstream through measurement planning, which leads to inspection results that link directly to design characteristics in the model. Using the same design characteristics, manufacturing process parameters can also be mapped to the model, and subsequently be linked to final part quality information. This unlocks a host of benefits to manufacturing organizations, where inspection results can help identify process improvements throughout manufacturing and highlight development opportunities in component designs.

This presentation will discuss the ‘Integrated Information for Improved Quality Demonstrators (IIIQ-D)’ core research project at the MTC, involving a large consortium of manufacturing primes and technology providers. The project objective was to create a manufacturing system and production cycle to demonstrate a range of benefits derived from the integration of design, manufacturing and quality data. Commercially available technologies were integrated into a manufacturing system through development of a digital thread, using the Quality Information Framework (QIF). An explanation will be provided of how the technologies within the system interacted with product definition data throughout the entire process chain, demonstrating data traceability for root cause analysis, and data driven decision support for process improvement.