Periodic Reporting for period 3 - IQONIC (Innovative strategies, sensing and process Chains for increased Quality, re-configurability, and recyclability of Manufacturing Optolectronics)
Reporting period: 2021-04-01 to 2022-09-30
Currently, manufacturing processes, evolution of equipment and instrumentation are challenges generally affecting industries that are determined to follow higher customization and individualization. Thus, the project iQonic establishes a multi-sensorial component manufacturing and photonic system assembly hardware and software environment for zero-defect manufacturing. Its data represents descriptors for main sources of defects, in particular contamination and surface damages, as well as component irregularities such as geometry deviations. The near real time data, in conjunction with historian data, flows via a unified middleware layer from the sensorial network into a software based manufacturing model, based on artificial intelligence and a decision support system that allows for error compensation throughout the complete manufacturing chain, early malfunction detection on the component and system level, component-rework and re-assessment. The iQonic supply chain scalable and holistic approach was developed with the goal to provide flexibility, efficiency and sustainability to the manufacturing industry. The iQonic architecture have been developed and validated vs. four different photonics use cases.
WP1: The Ethics Assessment work package lasts throughout the duration of the project and covers a continuous monitoring of potential ethical issues within the project. Respective deliverables have been filed within the first reporting period based on checks at the beginning of the project about three required ethics issues criteria. The continuously monitoring of these checks throughout the project and updating them accordingly was carried out in RP2.
WP2: A final revision of the user requirements took place. Monitoring the progress of the technical developments in the different use-cases was carried out. The initial deployment plan of the iQonic system was finalised, use case scenarios adapted and respective design of system operational environments took place while s/w and h/w components delivery estimation was finalised, and the RQ quality status was refined.
WP3: The requirements of optoelectric functionalities in different micro-optics assemblies and use cases has been analyzed. The assembly and disassembly processes were finalized and defined. The design and development of the process chain design model defining also the data exchange and the interfaces between h/w and s/w components is under final preparations. Moreover, a visual inspection infrastructure is developed to annotate possible defects from the components during the assembly process chain. The design of smart tagging to use, store and retrieve component, assembly, and assembly station specific data automatically is carried out.
WP4: The components "Sensor Network", "Electronic Nose", "Adaptive Optics scanning Microscope", "flexible handling Tool", and "KBS" have been implemented in their lab environments and tested, connection to the middleware was established.
WP5: A "Defect severity and decision support DSS or optimum response to defect" was establied and first introduced to the PRIMA use case. Techniques for product reuse and requalification were applied to the PRIMA and the BRIGHTERWAVE use cases mainly. The Cyber-Physical System (CPS) for product reuse and requalification was applied to the PRIMA use case, while an end-of life management and decision support for reverse supply chain RSC was established in general for the photonics manufacturing process chain.
WP6: An early malfunction detection and prediction inference engine was implemented on PRIMA and ALPES use case data-logs, deriving single station near real-time defect assessment models mainly for the ALPES use case, while the event modelling for fast forward cost functions and validation of KPIs was applied to the PRIMA use case. The respective results were interfaced with MES and other higher level management systems.
WP7: Integration of software – hardware platforms was conducted, in particular for the ficonTEC bonder platform to be used in several use cases. Planning for the technology validation and solution validation through demonstration in relevant environment was done.
WP8: The iQonic technologies were validated vs. the use case. 68% of "factorial" KPI's were fully or partially achieved, and 81% of technical KPI's were fully or partially achieved. Based on that lessons learned as well as a roadmap have been derived.
WP9: The development of the Plan for the Exploitation and Dissemination of Results was continued, several instruments, activities, events, and thematic workshops have been attended, activities for exploitation have been conducted and discussion about a Joint Venture establishment started. Within innovation management potential emerging markets for the iQonic photonics manufacturing architecture have been identified. Knowledge management, the Data Management Plan & IPR Protection were followed-up.
WP10: Project management was done by means of various online WP-thematic meetings as well as general assembly meetings online and in presence, with the challenge of handling the Covid pandemic issues troughout most of the duration of the project.
WP2: A final revision of the user requirements took place. Monitoring the progress of the technical developments in the different use-cases was carried out. The initial deployment plan of the iQonic system was finalised, use case scenarios adapted and respective design of system operational environments took place while s/w and h/w components delivery estimation was finalised, and the RQ quality status was refined.
WP3: The requirements of optoelectric functionalities in different micro-optics assemblies and use cases has been analyzed. The assembly and disassembly processes were finalized and defined. The design and development of the process chain design model defining also the data exchange and the interfaces between h/w and s/w components is under final preparations. Moreover, a visual inspection infrastructure is developed to annotate possible defects from the components during the assembly process chain. The design of smart tagging to use, store and retrieve component, assembly, and assembly station specific data automatically is carried out.
WP4: The components "Sensor Network", "Electronic Nose", "Adaptive Optics scanning Microscope", "flexible handling Tool", and "KBS" have been implemented in their lab environments and tested, connection to the middleware was established.
WP5: A "Defect severity and decision support DSS or optimum response to defect" was establied and first introduced to the PRIMA use case. Techniques for product reuse and requalification were applied to the PRIMA and the BRIGHTERWAVE use cases mainly. The Cyber-Physical System (CPS) for product reuse and requalification was applied to the PRIMA use case, while an end-of life management and decision support for reverse supply chain RSC was established in general for the photonics manufacturing process chain.
WP6: An early malfunction detection and prediction inference engine was implemented on PRIMA and ALPES use case data-logs, deriving single station near real-time defect assessment models mainly for the ALPES use case, while the event modelling for fast forward cost functions and validation of KPIs was applied to the PRIMA use case. The respective results were interfaced with MES and other higher level management systems.
WP7: Integration of software – hardware platforms was conducted, in particular for the ficonTEC bonder platform to be used in several use cases. Planning for the technology validation and solution validation through demonstration in relevant environment was done.
WP8: The iQonic technologies were validated vs. the use case. 68% of "factorial" KPI's were fully or partially achieved, and 81% of technical KPI's were fully or partially achieved. Based on that lessons learned as well as a roadmap have been derived.
WP9: The development of the Plan for the Exploitation and Dissemination of Results was continued, several instruments, activities, events, and thematic workshops have been attended, activities for exploitation have been conducted and discussion about a Joint Venture establishment started. Within innovation management potential emerging markets for the iQonic photonics manufacturing architecture have been identified. Knowledge management, the Data Management Plan & IPR Protection were followed-up.
WP10: Project management was done by means of various online WP-thematic meetings as well as general assembly meetings online and in presence, with the challenge of handling the Covid pandemic issues troughout most of the duration of the project.
The production yield was addressed by several KPI:
- PRIMA use case
* The production efficiency was improved by means of a reduction of the final testing time for defective parts from 28 h to 14 h.
* The production efficiency was improved by means of improving the detection of anomalous optics assembly from 1 % to >10%.
* The production efficiency was improved by means of decreasing the cycle time for defective part from 40 h to 20 h.
* The production efficiency (an indirect measure for the yield) was improved by means of reducing the relative rework time from 20 to 0.5.
* The production efficiency was improved by means of reducing the costs vs. operational costs for defective parts from 149 Euro to 23 Euro.
- ALPES use case
* Production yield was improved from 97 % before iQonic to 97.5 % (target 99 %).
* A reduction of production costs of 5 % was achieved (target 10 %).
- FILAR I use case
* The production efficiency regarding a first-time right ratio was improved by 20 % (target 25 %) from an 0.3 ratio value before iQonic.
* The throughput was increased by 30 % from an100 items / year value before iQonic.
* The scrap rate was improved by 15 % (target 20%) from a 0.2 ratio before iQonic.
The re-configurability was addressed by the following KPI:
- PRIMA use case
* The RSC decreased the manufacturing lead time from a change in the optical design to the final product (which is a measure for production efficiency) from 8 days to 6 days, and from 13 days to 11 days respectively.
* The CPS reduced the time of machine allocation for a new product (which is a measure for production efficiency) from 15 min to 5 min.
- ALPES use case
* The manufacturing lead time for a specific product (which is a measure for production efficiency) was reduced from six weeks to 29 days.
- FILAR I use case
* The responsiveness for time to make changeovers decreased for about 5 % from a two-week value before iQonic.
* The manufacturing lead time was decreased for ca. 6 % (target 10 %) from a three-week value before iQonic.
- PRIMA use case
* The production efficiency was improved by means of a reduction of the final testing time for defective parts from 28 h to 14 h.
* The production efficiency was improved by means of improving the detection of anomalous optics assembly from 1 % to >10%.
* The production efficiency was improved by means of decreasing the cycle time for defective part from 40 h to 20 h.
* The production efficiency (an indirect measure for the yield) was improved by means of reducing the relative rework time from 20 to 0.5.
* The production efficiency was improved by means of reducing the costs vs. operational costs for defective parts from 149 Euro to 23 Euro.
- ALPES use case
* Production yield was improved from 97 % before iQonic to 97.5 % (target 99 %).
* A reduction of production costs of 5 % was achieved (target 10 %).
- FILAR I use case
* The production efficiency regarding a first-time right ratio was improved by 20 % (target 25 %) from an 0.3 ratio value before iQonic.
* The throughput was increased by 30 % from an100 items / year value before iQonic.
* The scrap rate was improved by 15 % (target 20%) from a 0.2 ratio before iQonic.
The re-configurability was addressed by the following KPI:
- PRIMA use case
* The RSC decreased the manufacturing lead time from a change in the optical design to the final product (which is a measure for production efficiency) from 8 days to 6 days, and from 13 days to 11 days respectively.
* The CPS reduced the time of machine allocation for a new product (which is a measure for production efficiency) from 15 min to 5 min.
- ALPES use case
* The manufacturing lead time for a specific product (which is a measure for production efficiency) was reduced from six weeks to 29 days.
- FILAR I use case
* The responsiveness for time to make changeovers decreased for about 5 % from a two-week value before iQonic.
* The manufacturing lead time was decreased for ca. 6 % (target 10 %) from a three-week value before iQonic.