Periodic Reporting for period 2 - DAT4.ZERO (Data Reliability and Digitally-enhanced Quality Management for Zero Defect Manufacturing in Smart Factories and Ecosystems)
Reporting period: 2022-04-01 to 2023-09-30
Smart factories are characterised by smart processes and smart manufacturing systems that involve interlinked smart machines, smart tools and smart products as well as smart logistics operations. These generate large amounts of data, which can be used for analysis and fault prevention, as well as for the continuous improvement of both manufacturing processes and products. However, a major challenge for manufacturing is the quality and reliability of data. To address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process – need to provide adequate levels of data accuracy and precision. Measurement traceability should ensure optimal manufacturing quality. DAT4.ZERO aims to develop and implement a Digitally-enhanced Quality Management (DQM) system, characterized by real-time feedback and feed-forward loops using robust, quality assured data that offers a huge potential for the advancement of the Zero Defect Manufacturing (ZDM) paradigm. At the heart of DAT4.ZERO is a DQM System that gathers and organizes data from a Distributed Multi-sensor Network, which, when combined with a DQM Toolkit and Modeling and Simulation Layer and further integrated with existing Cyber-Physical Systems (CPS), offers adequate levels of data accuracy and precision for effective decision-support and problem-solving – utilizing smart, dynamic feedback and feed-forward mechanisms to contribute towards the achievement of a near zero-defect level of manufacturing in smart factories and their ecosystems. This shall be achieved through the following objectives:
1) To develop and demonstrate an innovative DQM system and deployment strategy for supporting European manufacturing industry in realizing a near-zero defect level of manufacturing in highly dynamic, high-value, high-mix, low-volume production contexts.
2) The effective selection and integration of sensors and actuators for process monitoring and control within intra- and inter-organizational production processes, systems and networks;
3) Developing a DQM platform with an architecture that provides reliable and secure data management and knowledge extraction to ensure integrity of data;
4) Creating strategies for advanced, real-time data analysis and modelling – exploiting artificial intelligence (AI) and CPS capabilities combined with smart human-in-the-loop technologies for rapid qualification, configuration, adaptation and reconfiguration of multi-stage process chains within and across organizational boundaries;
5) Five industrial pilots; developing optimized metrology and control strategies applicable to multiple domains/sectors, demonstrating first pass yield improvements, improved product quality and improved data reliability.
Overall, DAT4.ZERO will support the European factories of the future by Increasing equipment productivity through rapid detection and correction of product- and process errors, reducing ramp-up time, decreasing time-to-market of existing and evolving high-value products and increasing product quality, also reducing quality costs, scrap, and rework.
1) To develop and demonstrate an innovative DQM system and deployment strategy for supporting European manufacturing industry in realizing a near-zero defect level of manufacturing in highly dynamic, high-value, high-mix, low-volume production contexts.
2) The effective selection and integration of sensors and actuators for process monitoring and control within intra- and inter-organizational production processes, systems and networks;
3) Developing a DQM platform with an architecture that provides reliable and secure data management and knowledge extraction to ensure integrity of data;
4) Creating strategies for advanced, real-time data analysis and modelling – exploiting artificial intelligence (AI) and CPS capabilities combined with smart human-in-the-loop technologies for rapid qualification, configuration, adaptation and reconfiguration of multi-stage process chains within and across organizational boundaries;
5) Five industrial pilots; developing optimized metrology and control strategies applicable to multiple domains/sectors, demonstrating first pass yield improvements, improved product quality and improved data reliability.
Overall, DAT4.ZERO will support the European factories of the future by Increasing equipment productivity through rapid detection and correction of product- and process errors, reducing ramp-up time, decreasing time-to-market of existing and evolving high-value products and increasing product quality, also reducing quality costs, scrap, and rework.
The industril pilot lines have been described in in terms of process inputs, outputs, characteristics, constraints and targets. A special focus has been put on a general characterization of the use case process chain, as well as the identification of focus defects, critical processes and challenges to be addressed in DAT4.ZERO. Activities for Reference descriptions of the use-cases are completed. This contains mapping of functions of To-Be scenarios and mapping to the data architecture. The first version of the hardware and software specifications of the Data Quality Managemnet DQM platform, addressing the identified high-level solutions and requirements for each use case are made. Hardware (HW) and software (SW) requirements are identified: HW and SW tools, functionality connected to the HW and SW tools, interfaces (internal in the project or external), technical constraints, and use case constraints.
Multi-sensor and actuator system selection strategy for the demonstrations for shortlisted commercial sensing systems have been carried out or scheduled with specified testing setups of the use case scenario. In addition, several test samples from multiple use cases have been received by technical partners, and initial measurements have been performed. Selected sensors are being tested on sample parts and installed in prototype scenarios for feasibility analysis. Sensing technology KPI documents for human feedback codification and data gathering were created based on the requirement for data reliability and repeatability. Development of inspection solutions for product data gathering for the demonstrations for shortlisted commercial sensing systems have been carried out or scheduled with specified testing setups indicative of the key performance requirements of the demonstration scenario.
The definition and development of the DQM platform architecture are among several WPs in the project. The design of the architecture of the data management layer, DQM Core, has been developed, leaving the deployment. Industrial IoT-based infrastructure enabling both real-time data exchange and big-data storage has been designed, cloud-deployed and made available in its first release for testing. Industrial IoT software module for communication between different IT modules, big-data management, monitoring of machinery and industrial assets–in principle, for all pilot DQM instances have also been designed. Work has also been performed on development of various modules for the Quality Analytics System, (QAS) according to the industrial Pilots.
Multi-sensor and actuator system selection strategy for the demonstrations for shortlisted commercial sensing systems have been carried out or scheduled with specified testing setups of the use case scenario. In addition, several test samples from multiple use cases have been received by technical partners, and initial measurements have been performed. Selected sensors are being tested on sample parts and installed in prototype scenarios for feasibility analysis. Sensing technology KPI documents for human feedback codification and data gathering were created based on the requirement for data reliability and repeatability. Development of inspection solutions for product data gathering for the demonstrations for shortlisted commercial sensing systems have been carried out or scheduled with specified testing setups indicative of the key performance requirements of the demonstration scenario.
The definition and development of the DQM platform architecture are among several WPs in the project. The design of the architecture of the data management layer, DQM Core, has been developed, leaving the deployment. Industrial IoT-based infrastructure enabling both real-time data exchange and big-data storage has been designed, cloud-deployed and made available in its first release for testing. Industrial IoT software module for communication between different IT modules, big-data management, monitoring of machinery and industrial assets–in principle, for all pilot DQM instances have also been designed. Work has also been performed on development of various modules for the Quality Analytics System, (QAS) according to the industrial Pilots.
To serve global markets, e.g. vehicle manufacturers pooled production volumes to achieve economies of scale but lost sight of regional dependencies. The coronavirus pandemic exposed the vulnerability of global production and sourcing structures in the automotive industry.Global automobile production proved too fragile to meet the needs of a temporarily de-globalized market.
The medical sector was consolidated during the pandemic as a critical sector for countries and their economies. Investments have multiplied and previous growth forecasts will fall short. As in the automotive sector, reshoring will have a positive impact on the metal fabrication sector. The forecasts made may be difficult to achieve in the automotive and metalworking sectors as discussed, but regionalization / re-shoring (transference of a business operation that was moved overseas back to the country from which it was originally relocated, in Europe) and the need for reliability and quality are a great opportunity. The opposite occurs in the medical sector, where forecasts should fall short. DAT4Zero's developments will help the European companies gain a larger market share,independant of the market volume.
DAT4.ZERO aims to provide European companies with essential tools that enable them to successfully reduce production errors and non-conformities – with the goal of zero defect manufacturing (ZDM). The project uses digital technologies such as artificial intelligence and machine learning in addition to the human aspect to prevent errors. Zero defect manufacturing not only helps improve quality and productivity, it also contributes an important dimension to sustainability with reduced waste, reprocessing and material consumption.
The medical sector was consolidated during the pandemic as a critical sector for countries and their economies. Investments have multiplied and previous growth forecasts will fall short. As in the automotive sector, reshoring will have a positive impact on the metal fabrication sector. The forecasts made may be difficult to achieve in the automotive and metalworking sectors as discussed, but regionalization / re-shoring (transference of a business operation that was moved overseas back to the country from which it was originally relocated, in Europe) and the need for reliability and quality are a great opportunity. The opposite occurs in the medical sector, where forecasts should fall short. DAT4Zero's developments will help the European companies gain a larger market share,independant of the market volume.
DAT4.ZERO aims to provide European companies with essential tools that enable them to successfully reduce production errors and non-conformities – with the goal of zero defect manufacturing (ZDM). The project uses digital technologies such as artificial intelligence and machine learning in addition to the human aspect to prevent errors. Zero defect manufacturing not only helps improve quality and productivity, it also contributes an important dimension to sustainability with reduced waste, reprocessing and material consumption.