Smart, SME Friendly, open, Zero-Defect Manufacturing Reference Platform, Apps, SDK, and Marketplace for Product and Process Quality in any factory for achieving excellence in European and Global Manufacturing The ZDMP project combines state of the art technological approaches based on commercial grade standard or open-source or previous-project software with an innovative integration concept based on proven and integrating technologies. It provides Process and Product Quality support on top of a platform layer. These all can utilise ZDMP core services which can also be used to build ZD Apps which are placed on the ZD Marketplace. The ultimate aim is to establish a sustainable business and technological approach at the end of the project and launch “ZDMP Limited” assisted by the possibility of a crowdsourcing approach and ZDMP ambassadors. ZDMPs pan-European consortium entails ICT Technology SMEs (ICE, ASC, SOFT), Manufacturing/ZD Technology SMEs (PROF, CET, VSYS), Technology Corporates (SAG, Scale Focus), 4 Sector and Cross-Domain Use cases (14 Partners SME/Large from Automotive, Machine Tools, Construction, Electronics), Researchers/Associations (IKERLAN, ITI, UNINOVA, TUT, and UPV), specialists (DIN – German Standards, Rooter – Regulation, UOS-ITI – Call Management). ZDMP also is supported by the Coordinating partners of FOF projects CREMA, vf-OS, and C2NET.

The success of 5G technologies depends closely on their ability to attract vertical stakeholders, seeking the move of their services from cloud to the edge to meet unique KPIs. 5G-INDUCE project is based on the belief that such attractiveness requires vertical stakeholders and Network Application (nApp) developers to be able to smoothly deploy and manage applications in distributed 5G network environments, in a secure fashion and with strict KPI requirements. 5G-INDUCE relies on the deployment of an open ETSI NFV compatible 5G orchestration platform for the deployment of advanced 5G nApps. The platform’s unique features provide the capability to the nApp developers to define and modify the application requirements while the underlay intelligent OSS can expose the network capabilities to the end users on the application level without revealing any infrastructure related information. This process enables an application-oriented network management and optimization approach that is in line with the operator’s role as manager of its own facilities, while it offers the operational environment to any developers and service providers through which tailored made applications can be designed and deployed, for the benefit of vertical industries and without any indirect dependency through a cloud provider. The project focuses on the Industry 4.0 vertical sector, as one of the fastest growing and most impactful sectors in European economy with high potentials for service development SMEs and with the capability to tackle all diverse cases of service requirements. The platform is integrated over 3 5G Experimentation Facilities in Spain, Greece, and Italy, and extended with links towards specific Industries, for the showcasing of nApps in real 5G environment. The consortium includes all the required stakeholders (MNOs, Industries, System integrators and SMEs) from the benefited business sectors evaluated in the project, while significant part of the work (>50%) is conducted by innovative SMEs.

Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust. XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI "glass box" models that are explainable to a "human-in-the-loop" and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.

Global economic crises and the COVID19 pandemic have dictated manufacturing firms to rethink their production and business models. Production systems need to adopt both human and automated resources that can work together seamlessly. As a response, CONVERGING aims to Develop, deploy, validate and promote smart and reconfigurable production systems including multiple autonomous agents (collaborative robots, AGVs, humans) that are able to act in diverse production environments. The diversifying factors will be a multi-level AI based cognition (line, station, resource levels) which will exploit the collective perception (Digital Pipeline) of these resources, allowing them to interact with each other and seamlessly coexist with humans under a "social industrial environment" that ensures trustful, safe and inclusive user experience The project proposes the development of systems that can: 1. Perceive: Identify and recognize processes/resources/environment and their status by introducing Big Data, Real Time Integration & Communication Architecture, Digital Twins and Human in the Loop techniques 2. Reason: Analyze the production system status and autonomously formulate plan of actions using AI, Planning and Reconfiguration Algorithms as well as Resource Autonomy solutions 3. Adapt: Automatically modify h/w and control systems to execute the formulated plans through the use of Robotics and Autonomous Systems, Smart Devices and Adaptable Mechatronics 4. Collaborate: Seamlessly work with humans or other resources establishing a social industrial environment which exploits Smart Human Machine Collaboration, User experience assessment and User centric workplace design 5. Innovate: Expand its capabilities and Openness via an Open Pilot Network as well as links to local and international innovation ecosystems CONVERGING will demonstrate its results in the Automotive, Aircraft Production, White Goods and Additive Manufacturing products processing sectors.