InterQ

The PPD hallmark ensures the quality of each operation.  To achieve improved product quality, physical and virtual sensors will be recording data about manufacturing conditions and information about machine states will be gathered. Product quality will be approached by in-process sensors and traditional metrology measures to measure the quality of the final part. Data quality will be evaluated by syntactic, semantic and pragmatic aspects which will enable certifying the reliability of the process and product data.

The modules provide a schematic view of the developments concerning the process, product and data quality with the improvement, resulting in zero defect manufacturing and data exchange in a secure environment via the trusted framework.

The process module monitors the manufacturing with new physical and virtual sensors which can measure close to the cutting point and estimate critical process characteristics providing more meaningful data. Possible deviations are detected and avoided by a fingerprint of the machine or the process during normal production. Machine operator knowledge is incorporated to increase automatic data processing.

The product module replaces manual quality inspections with new sensors and processing techniques to provide reliable data. Two digital twins will be implemented: one metrology-based digital twin to predict global production quality and one prediction digital twin giving an estimation on the product quality after each production step by considering process variables and measurements.

The data module is a result of the previous modules on the process and the product. In current manufacturing, data can be corrupted or inconsistent for example due to malfunctioning sensors, unproper signal processing or communication problems with data gathering elements. But since data is the basis for the optimization of algorithms and processes, reliable data is of the utmost importance. For InterQ, data is evaluated on two validation layers. The first layer checks the ontological and temporal consistency of data, while the second uses historical and statistical analyses to detect unusual values.

The ZeroDefect module is again a result of the previous modules which provide reliable data of processes and products. This enables assessing the product quality virtually without relying solely on measurements. Additionally, artificial intelligence production optimizations are used to improve the process by controlling geometrical deviations, surface finish and surface integrity of products.

All modules are embedded in the TrustedFramework which allows safe data exchange using cryptographic protocols and trusted data sharing mechanisms. In this way, a common hurdle of current manufacturing processes is overcome. Traditionally, that data on one production plant and one specific process is collected, neglecting the sequence and diversity of manufacturing processes. This problem is reinforced by a lack of trust between different actors among the supply chain as they do not trust each other enough to disclose their data. The InterQ TrustedFramework faces this problem from a new paradigm that is based on the traceability, integrity and trust of the gathered data to finally provide an environment for overcoming the data silo.

ModuleWorks’ main contribution to the project is building a digital twin which will be able to predict and show the part quality from the process simulations. Beside this, ModuleWorks is partially involved in the work packages on the process, product, and data. The overall result of the project are new solutions to predict the final quality of the product, a holistic approach manufacturing and the surrounding data by using digital twins.