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Call for Proposals for pilots and digital platforms in the field of Zero Defect Manufacturing (ZDM) by SMEs and MidCaps
Call identifier: QU4LITY OC1
Publication date: 2020-06-01
Status: Closed
Opening date: 2020-06-08 12:00:00 (Brussels time)
Closing date: 2020-09-18 17:00:00 (Brussels time)
Call general detailsThematic areasSupporting documentation
Call Summary
The objective of this Open Call is to validate the standardized QU4LITY concepts & platforms through new ZDM pilots. Moreover, the Open Call also aims to enhance the existing QUALITY platform and pilots by adding new value-adding functions. This Open Call will expand the QU4LITY ecosystem, increasing the engagement of SMEs and MidCaps across Europe within the field of Zero-Defect Manufacturing and Autonomous Quality.
Call Keywords
Autonomous Quality.
Artificial Intelligence
Zero Defect manufacturing
Smart Product
Smart Factories
IoT
BigData
Manufacturing
New Autonomous Quality Pilot
The QU4LITY-consortium invites candidates to propose novel pilots that align to the QU4LITY autonomous quality concept, notably pilots that implement features currently not available as part of the large- scale pilots of the consortium partners.
Pilots should be aligned to one or both of the following two objectives/themes:
• Validate the QU4LITY concept, digital platforms and APIs in areas beyond the pilots of the consortium partners: Proposers should present novel pilots in-line with the QU4LITY concept and the QU4LITY Reference Architecture for (Digital) ZDM.
• Demonstrate end-to-end Autonomous quality in a cross-border supply Chain pilot pilot: Proposers should present digital quality management across a supply chain i.e. beyond a single industrial plant.
Relevant Documents for this thematic area include D2.11, D3.5, D3.13, D8.1 and D3.11 (specific for A5). Pilots proposed by applicants must make use of a digital enabler listed in section 2.3.3 in the Guide for Applicants
Topics:
A1: Data Driven AI for pattern recognition in Zero Defect Manufacturing for high performance productPattern recognition is the process of recognizing patterns by using an Artificial Intelligence algorithm, it can be defined as the classification of data based on knowledge already acquired or on statistical information extracted from patterns or their representation. Pattern recognition is able to detect arrangements of characteristics or data that provide value information about a given system or data set.
Applicants to topic A1 are required to design, implement and experiment data driven algorithms for pattern recognition related to Zero Defect Manufacturing for identification of defects, proactive quality control, reverse engineering for high performance products. The aim is to demonstrate the potential of this technology to improve the quality control in any of the critical point of their quality value chain and to analyse its connection and impact on the whole manufacturing process.
A2: Data Driven AI in Human Machine Collaboration for Zero Defect manufacturingPartnering with machines is integral to the future of how we live and work. A new era of intelligent systems will be characterized by trust and understanding between humans and machine. This collaboration can provide many benefits. Machines or robot can assemble and consider more data points than humans, can incorporate and often provide a less biased support to decision and improve the productivity.
Applicants to topic A2 are required to demonstrate the potential of the human machine collaboration for quality control in manufacturing, developing autonomous learning or decision-making algorithms to improve the quality in any of the critical point of their quality value chain and analyse its connection and impact on the whole manufacturing process.
A3: Integration of Data driven inline Autonomous Quality in solutions for Zero Defect ManufacturingTraditional quality control models such as Total Quality Control, end-of-line Statistical process control or in-line multi-stage quality control solutions are not fully capable to deal with the dynamism of the Smart Factory Scenario scenarios, calling for effective support to control smart and connected production processes.
Data Driven inline Autonomous Quality solutions can deliver learning and adaptation capabilities to manufacturing companies that need to quickly scale up from small to big lot-sizes, or between different parts whilst retaining the required quality.
Applicants to topic A3 are required to demonstrate the potential of the Data driven inline Autonomous Quality solution in highly flexible manufacturing scenarios to cover the whole quality value chain.
A4: Edge and/or real time solutions for Zero defect ManufacturingComputing infrastructure that exists close to the sources of data, such as industrial machines, industrial controllers e.g. SCADA systems, and databases aggregating data from a variety of equipment and sensors.
Applicants to topic A4 are required to demonstrate the potential of the Data Driven edge computing enabled applications based on e.g. analytics, machine learning etc. to improve the quality in any of the critical point of their quality value chain and analyse its connection and impact on the whole manufacturing process.
A5: Ensuring Quality Management in supply chain trough blockchain based technologies.Quality assurance in complex production systems is a difficult problem to tackle, given the number of parties involved in the sourcing of raw materials and parts and the extreme customization of products. Moreover, Zero Defect Manufacturing goals require that Autonomous Quality solutions are extended to the entire supply chain, possibly including logistics. This objective poses unique challenges, as it implies that some suppliers, although being autonomous businesses, are fully integrated into the control loop. Blockchain technology can help by providing a common, company-neutral data exchange infrastructure where key information can travel between all stakeholders of a process with top guarantees of provenance, integrity and transparency.
Applicants to topic A5 are required to develop novel distributed applications to control the quality and traceability of materials and products along the supply chain and analyse their connections and impact on the whole manufacturing process.
ManufacturingSmart Supply ChainQuality Value ChainLogisticsAutonomous Quality.
A6 Integrating ZDM solutions in Mass Customization and Lot Size One Manufacturing processesMass customization as a strategy that allows the production of small lots (even as small as lot size one) is becoming more and more popular and is one of the main implementations of the concept of Industry 4.0. Mass customized products, though are complex, feature a significant amount of electronics or micro-features and are composed of advanced (multi-) materials - becoming stronger, lighter and smarter whilst remaining at least as safe or secure as previous versions.
Applicants to topic A6 are required to develop novel applications for a Zero-Defect Manufacturing through the integration of Autonomous Quality (AQ) Control Loops into Mass Customization and Lot Size One processes, using data driven technologies.
ManufacturingSmart FactoriesSmart ProductQuality Value ChainZero Defect manufacturingAutonomous Quality.
Expansion of QU4LITY pilot systems
Expanding the scope of existing digital platforms and pilots with new functionalities and features, which will be contributed by the SMEs, using the Open APIs that will be made available over the project’s platforms. The SMEs will be given the opportunity to validate these enhancements in the project’s experimental infrastructures and testbeds.
Proposed solutions should align to one or both of the following two objectives/themes:
• Validate the expandability of the QU4LITY digital platforms: Proposers should propose and implement extensions to the QU4LITY technologies, notably to the QU4LITY platforms used in the project’s pilots.
• Complement existing pilots and platforms with added-value features and functionalities: Proposer should propose extensions to existing pilot systems.
Topics:
B1: Dryer Factory Holistic Quality Platform (Whirlpool)The pilot carried out by Whirlpool will integrate the QU4LITY digital enablers and platforms and the AQ (Autonomous Quality) control loops. The main innovation will be represented by the introduction in production of MPFQ (Material Process Function Quality) model fused with AQ control loops: Functional Integration and Correlation between Material, Quality, Process and Appliance Functions. This innovative way to control quality and model data inherent to quality will be the fundamental approach that will lead to the vision of holistic Quality system.
The challenge faced by the pilot that should be addressed by the applicant is related with the absence on the market of a graphical editor tool.
The full detailed description of this challenge is described in section 2.3.5 of the "Guide for Applicants" document.