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leArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments

Periodic Reporting for period 1 - ASSISTANT (leArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments)

Reporting period: 2020-11-01 to 2022-04-30

The industry 4.0 technologies bring considerable advantages to the manufacturing systems: new products can be launched frequently, delivery times reduced, and uncertainties can be handled with greater flexibility. However, these technological advances also bring many challenges such as the difficulty in designing and controlling a complex manufacturing system, and in managing several sources of data, large number of end items and components, which make production and supply planning extremely difficult to manage. There is also the fact that the system is constantly changing, which introduces variability on many parameters that are difficult to predict with precision. On the other hand, with frequent system reconfigurations, new tools and resources are frequently acquired or changed, and the performance of these tools and their impact on the production system are difficult to predict. Manufacturing systems are becoming hybrid with humans and machines, where robots manage a steady flow and operators provide flexibility. This leads to the development of cobots where humans help machines. In these highly automated and reconfigurable manufacturing systems, workers must be versatile, work on various tasks and move to different positions. In such a system, workers are more prone to injury, and it is crucial to consider safety and ergonomics. Finally, the development of AI components in manufacturing requires an ethics-based and trustworthy framework that should guide this development to avoid all the negative impacts that untrusted AI can bring in manufacturing, such that inaccurate decisions, discriminatory results, etc. To provide solutions to these challenges, ASSISTANT project provides Artificial Intelligence-based decision support tools to make tactical and operational decisions in manufacturing. It includes:
• An intelligent digital twin for process design or redesign: What resources, tools, skills do we need, and how can we organize them?
• An intelligent digital twin for production planning: How much do we produce each week? ASSISTANT will integrate machine learning techniques (with optimization) to process and exploit small and large datasets.
• An intelligent digital twin for scheduling: Assignment of products to the machine and the order in which operations are performed. ASSISTANT will provide a model acquisition tool that enriches basic scheduling models with learned constraints.
• A data fabric that collects data from IOT devices, machines, operators, and existing software. Data will be cleaned and stored in dynamic knowledge bases.
• Tools for safe decision actuation, and control. This includes the development of flexible cognition methods and resource management for collision-free planning and adaptive motion planning on the shop floor. These actuation tools will allow one to react in a timely manner to unforeseen events due to autonomous, real-time, and optimization-based methods improving time, costs, and safety conditions, among other economical, environmental or social considerations.
• ASSISTANT will provide AI Management and Assessment Plan on Ethics to guide ASSISTANT partners in the development and deployment of responsible AI solutions.
To achieve the project objectives, the first months of the project were used to define and describe the specifications of the tools to be developed in the project as well as the requirements of the use cases (Siemens Energy, Atlas Copco and PSA) and collecting data from use case providers. This step was followed by the definition and design of an ethical and human-centric architecture showing how the developed components will be integrated.
Once the requirements and specifications were defined, and a first version of the architecture defined, the project moved into the development phase. At this stage of the project, the main achievements are:

• 21 deliverables were submitted out of a total of 51 deliverables
• 4 milestones have been achieved. These milestones relate to (a) the kickoff of the project, (b) the first report on the architecture of the project, (c) the development of the Graphical User Interfaces and, (d) the development of the first prototypes on the digital twins on process planning, production planning and scheduling, and real-time control and actuation. In terms of dissemination, the project has 26 scientific articles in total, of which 9 published, 9 accepted and 8 under review. Regarding communication, the project has set up a website and a web page with more than 2,000 unique visitors, social media, a video explaining the concepts of the project and the expected results with more than 12,000 views on all channels for communication and dissemination of the ASSISTANT project. Seven press releases were published by the whole consortium, 8 newsletters including 4 special newsletters at the project level and 7 interviews where ASSISTANT concepts and expected results were communicated. ASSISTANT attended 8 workshops related to Ecosystem Building Activities Targeting EU Projects, EU Initiatives, Researchers, and Industry.
ASSISTANT will boost digital twins with state-of-the-art AI technologies, to create intelligent twins able to help make manufacturing decisions. It will include the following AI technologies:
• Statistical machine learning techniques to produce simulated data in digital twins and to estimate parameters and cost functions in decision aid system.
• Bayesian network and moment matchings to acquire the probability distribution of unknown parameters
• Reasoning under uncertainty for robust and flexible manufacturing design and operations.
• Machine learning for the acquisition of high-level understandable prescriptive analytic models based on historical data and/or simulation data.
• Use generative design in synergy with constraint programming to help the user exploring alternatives for reconfiguring a production system.
• Programming abstractions for data streams to synthesize safe code for capturing qualitative and quantitative patterns on data streams in the context of IoT applications for real-time control in the shop floor.

As for expected result until the end of the project, ASSISTANT aims to provide intelligent twins for the design and operation of agile production systems. A process planning intelligent twin helps the design of production systems with a high level of flexibility while maintaining product and process quality. The production planning and scheduling twins deal with various sources of uncertainties encountered in production systems, and the real-time twin helps to react in a timely manner to unforeseen events. ASSISTANT will contribute to six impacts described below:
(1) Products and services usable in a wide range of manufacturing processes leading to agile production processes and improved quality of products and processes.
(2) Humans working together with AI systems in optimal complementarity.
(3) ASSISTANT leverages on AI to positively impact employment and quality of jobs.
(4) ASSISTANT will support the production of a legal and ethical framework for AI at the European level.
(5) ASSISTANT will contribute to breaking down silos of research in manufacturing by putting AI research community to the benefit of the manufacturing community.
(6) ASSISTANT decision support system tools consider sustainable metrics, allowing to improve the economic, environmental, and/or social perspectives of manufacturing companies.
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