Periodic Reporting for period 1 - i4Q (Industrial Data Services for Quality Control in Smart Manufacturing)
Période du rapport: 2021-01-01 au 2022-06-30
i4Q Project aims to provide an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework will guarantee data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimization. i4Q RIDS will include simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zerodefect manufacturing.
With i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zero-defect manufacturing. i4Q Solutions will efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology. i4Q Project will provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.
The i4Q RIDS will be demonstrated in 6 Uses Cases from relevant industrial sectors and representing two different levels of the manufacturing process: machine tool providers and production companies. i4Q pan-European consortium entails Industrial partners: WHIRLPOOL (White goods manufacturer), BIESSE (Wood industrial equipment), FACTOR (Metal machining), RIASTONE (Ceramic pressing), FARPLAS (Plastic injection) and FIDIA (Metal industrial equipment); Implementers: TIAG (Industrial Communication Protocols and Standards), CESI (Machine tools, Advanced Materials, Micro-technology) and AIMPLAS (Thermoplastic and thermosetting plastic materials); Technology Providers: IBM (Information Technologies Company), ENGINEERING (Software and ServicesCompany), ITI (Information Technologies Institute), KNOWLEDGEBIZ (Information Systems Company), EXOS (Operations Consulting Company); R&D partners: CERTH (Research Institute), IKERLAN (Technological Centre),BIBA (Research Institute), UPV (University), TUBERLIN (University), UNINOVA (Research Institute); Specialist partners: FUNDINGBOX (Exploitation), INTEROP-VLAB (Dissemination), DIN (Standardisation), LIF (Legal).
With i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zero-defect manufacturing. i4Q Solutions will efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology. i4Q Project will provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.
The i4Q RIDS will be demonstrated in 6 Uses Cases from relevant industrial sectors and representing two different levels of the manufacturing process: machine tool providers and production companies. i4Q pan-European consortium entails Industrial partners: WHIRLPOOL (White goods manufacturer), BIESSE (Wood industrial equipment), FACTOR (Metal machining), RIASTONE (Ceramic pressing), FARPLAS (Plastic injection) and FIDIA (Metal industrial equipment); Implementers: TIAG (Industrial Communication Protocols and Standards), CESI (Machine tools, Advanced Materials, Micro-technology) and AIMPLAS (Thermoplastic and thermosetting plastic materials); Technology Providers: IBM (Information Technologies Company), ENGINEERING (Software and ServicesCompany), ITI (Information Technologies Institute), KNOWLEDGEBIZ (Information Systems Company), EXOS (Operations Consulting Company); R&D partners: CERTH (Research Institute), IKERLAN (Technological Centre),BIBA (Research Institute), UPV (University), TUBERLIN (University), UNINOVA (Research Institute); Specialist partners: FUNDINGBOX (Exploitation), INTEROP-VLAB (Dissemination), DIN (Standardisation), LIF (Legal).
By the beginning of the project, a conceptual project vision was defined, establishing the state of art in terms of technologies for quality in manufacturing. Besides, a regulation and trustworthy system for data management was defined and the specifications driving the creation of i4Q solutions and key performance indicators were being successfully set. By M9, the i4Q framework was designed, based on a clear and detailed reference architecture; viewpoints, KPIs, requirements and functional specifications were defined. A detailed analysis of various ontologies and data models for supporting interoperability and data exchange among tools, platforms and organisations were also addressed. Multiple perspectives were used to describe the reference framework, such as viewpoints related to business, usage, functional and implementation.
By M18 the first version of the i4Q solutions was released, including the principles of each solution, development of the idea, design, as well as communications among solutions. Deliverables and demos including all the necessary information for the development and function of these solutions were also released.
Next, the experimental base for the i4Q Solutions was initiated. Methodologies were applied in the 6 pilots that are participating in the project, as well as software tools, and technologies developed against specified and real-world scenarios and requirements. Their exploitation potential was also investigated here. The first pipelines for the 6 pilots were finally prepared. A set of solutions is collaborating in these pipelines, in order to collect data, process these and give the required result for each industry. Last but not least, a message broker was also developed in order to facilitate the communication between the solutions.
To conclude, the i4Q project from M1 to M21 is participating in multiple events, conferences and publications, having an active dissemination activity, with several interesting papers, and presentations. Extra dissemination materials providing information on the progress of the project, i4Q events, workshops, publications and other exploitable results, include the project website, as well as links to Twitter, LinkedIn and YouTube that are all publicly available.
By M18 the first version of the i4Q solutions was released, including the principles of each solution, development of the idea, design, as well as communications among solutions. Deliverables and demos including all the necessary information for the development and function of these solutions were also released.
Next, the experimental base for the i4Q Solutions was initiated. Methodologies were applied in the 6 pilots that are participating in the project, as well as software tools, and technologies developed against specified and real-world scenarios and requirements. Their exploitation potential was also investigated here. The first pipelines for the 6 pilots were finally prepared. A set of solutions is collaborating in these pipelines, in order to collect data, process these and give the required result for each industry. Last but not least, a message broker was also developed in order to facilitate the communication between the solutions.
To conclude, the i4Q project from M1 to M21 is participating in multiple events, conferences and publications, having an active dissemination activity, with several interesting papers, and presentations. Extra dissemination materials providing information on the progress of the project, i4Q events, workshops, publications and other exploitable results, include the project website, as well as links to Twitter, LinkedIn and YouTube that are all publicly available.
i4Q solutions will:
o tackle the analysis of manufacturing data by combining simulation and real data in the form of digital twins, while employing data fusion techniques and supporting the distribution, deployment, and monitoring of AI analysis models in real manufacturing environments with the final aim of increasing the industrial equipment productivity through real-time error localisation.
o include manufacturing process qualification and reconfiguration as an essential step during ramp-up and after reconfiguration of production processes and ensuring good manufacturing practice and final product quality through adequate control over processes, data collection and statistical procedures for evaluation of process stability and process performance.
o decrease the time-to-market of new products or variants, through the inclusion of Digital Twins using simulation and optimisation strategies for rapid line reconfiguration considering the intelligibility of the needed upgrades, ensuring that non-simulation experts may also exploit the prescriptive analyses.
o potentiate the role of AI in manufacturing for taking actions and decisions, identifying defects in products in a manufacturing lines, identifying hazards, or tuning machines based on dynamically changing conditions, connecting devices, gathering, collecting, and storing data for being analysed with the aim of help on taking the pertinent decisions and actions to increase the quality of manufactured products leading to zero-defects and the elimination of scrap.
o tackle the analysis of manufacturing data by combining simulation and real data in the form of digital twins, while employing data fusion techniques and supporting the distribution, deployment, and monitoring of AI analysis models in real manufacturing environments with the final aim of increasing the industrial equipment productivity through real-time error localisation.
o include manufacturing process qualification and reconfiguration as an essential step during ramp-up and after reconfiguration of production processes and ensuring good manufacturing practice and final product quality through adequate control over processes, data collection and statistical procedures for evaluation of process stability and process performance.
o decrease the time-to-market of new products or variants, through the inclusion of Digital Twins using simulation and optimisation strategies for rapid line reconfiguration considering the intelligibility of the needed upgrades, ensuring that non-simulation experts may also exploit the prescriptive analyses.
o potentiate the role of AI in manufacturing for taking actions and decisions, identifying defects in products in a manufacturing lines, identifying hazards, or tuning machines based on dynamically changing conditions, connecting devices, gathering, collecting, and storing data for being analysed with the aim of help on taking the pertinent decisions and actions to increase the quality of manufactured products leading to zero-defects and the elimination of scrap.