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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - iDev40 (Integrated Development 4.0)

Teaser

Summary

Companies aiming to benefit of digitization will have to solve the digitalization challenge by radically thinking on how they can increase their level of digital maturity to integrate their production and development processes within a future digital value chain. To be successful in industrial digitalization requires a tight look at products, ecosystems, and the product life cycle, but also taking seriously the role of digital culture, leadership, skill-sets of the human workforce to implement the digital change.
iDev40 covers the whole value chain to provide sustainable, digital and industrial solutions for integrated ECS development and production, built on common standards and validated by industrial pilots. Tangible results from 18 industrial Use Cases linked to the relevant digitalization facets and evaluating them on a technical and a socio-economic level in operational environments will make the potential impact of digitalization within the European ECS industry visible.
The iDev40 objectives address the following challenges:
â€¢ To develop and demonstrate a secure industrial data management infrastructure capturing the complex interactions between production and development, an automatic knowledge base including knowledge validation fostering the need-to-know-paradigm, as well as a semantically-enriched data adopting Deep Learning and AI system.
â€¢ To facilitate the virtualization of the ECS value chain, providing humans and machines access to data and knowledge, interlinking engineering and manufacturing teams and databases, fostering easy and intuitive collaboration facilities, and demonstrating a virtual live representation of operational processes through a digital factory twin.
â€¢ To enable digitalization across the product lifecycle through a Product Lifecycle Management backbone allowing seamless re-use of information and data across hierarchies and organizations, implementing a digital process management to better integrate engineering and production, as well as showcasing the integration, application, and validation of a digital product twin.
â€¢ To treat the digital enterprise as a socio-technical phenomenon, enhancing human capital, structural capital, and complexity capital through implementing a human-centered knowledge base, demonstrating how to successfully manage virtual high-skilled teams in a remote way, and empower employees to interact with highly-automated complex systems.

Work performed

Providing a concept for implementation of data and knowledge management systems that intelligently manage data in a large heterogeneous ECS environment, the work focused on analyzing the current and target situation. Requirements were collected considering the type of data available, the diversity of data and methods currently used to store and manage data in such environments. Work was performed to gather specification of needed interfaces, requirements for data security and needed data flow. This is an important step towards deriving security concepts for a need-to-know data management. To address the goal of implementing AI in the ECS domain, the State-of-the-Art in artificial intelligence and deep learning in respective fields was examined.
To digitize and virtualize the ESC value chain the focus laid on the establishment of a concept for the supply network virtualization of the entire fab cluster, to prepare for zero-failure production. Therefore, in the running tasks and use cases the requirement definition is almost completed. Research activities regarding visualization tools and multi-factory planning have been performed resulting in valuable findings supporting the development of different concepts for the semiconductor industry like data collection for contamination issues in a fab, new automated sorter scenarios, new simulation models for development lots and digitalization that is used to automate administrative processes.
The introduction of Industry 4.0 principles to legacy systems is essential for a successful migration scenario. A common understanding of the SSoT and Digital Twin concept was developed, covering the underlying challenges when implementing them along value chains. To digitize and optimize the product lifecycle and change management along the value chain different activities started analysing the current situation taking challenges that result from change requests into account. Pain points regarding continuous digitization have been identified and will be tackled under the aspects of SSoT and Digital Twin. Initial requirements and specifications for the underlying product lifecycle backbone were gathered. Based on a specific use case an efficient design methodology was developed which led already to the definition of the best solution for a DC-DC converter. Moreover, the basis for gathering and the analysis of in-field data in the automotive domain was laid, test rigs were set-up and literature studies concerning algorithms for anomaly detection that allow in turn for predictive maintenance were conducted.
The work towards enhancing the innovation capability by a human-centered design of ECS development processes, production systems and value chains focused on defining the current state of required skills, competences and knowledge of workers in digitized systems. The conceptualizing of skill management systems and possible training scenarios for employees started. Requirements for digitally augmenting appropriate workplaces were identified and respective technologies for further testing discussed. Investigations on possible smart collaboration scenarios and appropriate methods for site-across knowledge transfer are ongoing. First functional structures of a framework for simulating actors in complex socio-technical systems were implemented. Relevant data and key performance indices for an advanced return on quality approach were identified and collected for modelling the effects of process improvements.
To demonstrate the effectiveness for the addressed essential technologies at the smart manufacturing application domain, collaborations between the project partners were established that goes beyond the borders of work packages.

Final results

Development of an integrated data life cycle management platform enabling seamless data storage, access, maintenance and sharing. AI algorithms will have the potential to support and automate many processes and combine information from different sources. Consistent IT security throughout the value chain bears the potential for dramatic increase in trust and efficiency. Enriched digital twins of e.g. production equipment or development lots will be created and methods will be defined which still allows intervention by engineers. Hence, production systems become more autonomous and visible on different levels of abstraction. Consequently, engineers will be able to re-configure and continuously optimize the entire value chain. Moreover, a seamless data and information re-use across the hierarchical level of each system and between systems, concerning the whole product lifecycle is targeted. This will be achieved by introducing a SSoT, which will prevent data error-prone redundancy whereby data transformation will increase the level of automation. It is crucial in the current manufacturing and engineering setup to analyze, what new challenges an employee will face in smart factories. By evaluating the outcome in terms of socio-economic impact, iDev40 will approach this digital transformation.