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.
