Updated: Oct 17, 2022
September was the last month of the project, when we completed PIQ-ME-NOW project validation and worked on its exploitation.
We succeeded in development of 3 new components:
IQModelGenerator- component generating a process model (including the inline quality control step) executed by the Orchestration designer component for each of the production operations in the recipe,
IQProcessesLibrary – set of parametrizable templates for exemplary production operation process models (e.g. bending a pipe, cutting it to length, picking materials from the input slots etc),
IQStepsLibrary - set of parametrizable templates for low-level robot actions needed to implement the inline quality checks,
and their integration with a pre-existing capability-based, scheduling and production tracking component CaCaScheduler. Moreover, we integrated our solution with 3 ZDMP components to create a complete solution enabling inline quality control after each production step.
We have kept to the Asset Administration Shell (AAS) standards in our works and created a set of AAS sub-model template for defining models of quality requirement in the manufacturing operations as well as their exemplary instances for cutting, bending and transportation operations characteristic for metal processing.
In order to test the functionality of the components developed and integrated within the PIQ-ME-NOW experiment, we have selected 5 metal products from the portfolio of our consortium partner - CMBIT. Each of them has been manufactured 8 times in different scenarios and has been described with the AAS models mentioned earlier.
In all the 40 test runs the system performed as expected – both in terms of tasks scheduling and manufacturing the goods according to the specification, as well as quality checks according to the requirements and reacting to any defects detected. All the injected quality issues were properly detected and either redirected for manual reworking or for scrap.
Moreover, we have analysed the quality of the manufactured goods – both among the test cases and the normal, commercial activities of CMBIT. The trials have shown a significant improvement in the observed quality and reduction in the required reworks or unfixable rejections: the number of products requiring manual reworking after the final inspection was reduced by 36% and generated scrap was reduced by 25%.
Learn more on the usability of our solution from the video below.
This project has received funding within the FSTP mechanism of the ZDMP project founded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825631.