AI-enhanced control strategy for production environment

The experiment, “AI-enhanced control strategy for production environment”, conducted by ART-ER in Bologna, is an answer to the need of a  comprehensive controlling strategy for the production lines, a strategic need for manufacturing SMEs.

This need is expressed in terms of different goals to be achieved: process efficiency (e.g. in terms of production time, or energy efficiency), quality control, market request readiness. The need is stronger for those SMEs operating in highly dynamic market sectors. Production line controlling strategy has always been an human-centered activity, and this is due to the need for a clear and wide view and understanding of the potential issues coming from the different areas of the production environment, starting from the warehouse management to the final delivery. This activity then strongly depends on the previous SME experience. AI technology allows for an extension of this experience. One of the strongest qualities of AI is that it can help humans preventing events on a real-time basis. This means AI can look forward in a higher-dimension space than humans: different heterogeneous data can drive AI through the suggestion of specific activities to avoid, in example, shortage of raw material due to a sudden higher request on the market, potential damages to machines, of unexpected wear due to incorrect usage for a long time. New technologies and sensors help in collection of data that were never collected before: data from machines and operators that provide a lot of information. Processing information is extremely time-consuming, and not all the SMEs are interested in spending personnel efforts in elaborating data and interpreting them. Artificial Intelligence can help in this task, providing a fast interpretation of data, and also a deep level learning strategy from them. Training  AI algorithms on  previously collected data from production line will provide them a baseline of how operations are made. Improving AI knowledge by mean of other data coming from new sensors will allow for the detection of fail  points in the actual controlling strategy and the definition of a wider and far-in-the-future new strategy.

The experiment provides end-users (SMEs) an instrument to implement AI and AI-related technologies into their production environment. Benefits for end-users can be achieved in terms of time and costs reduction, final product quality and general better production plant management, with the possibility to allow interaction between the production line management and other services (e.g. predictive maintenance, digital twins...). A better production line management also allows for productivity enhancement and reduction of dead costs, such as warehouse management and waste management. Thanks to AI enhanced predictions, activities inside the production environment are fine-tuned, allowing for a better efficiency and cost reduction. More, the AI-enhanced management tool provides support in terms of energy efficiency and machine allocation.

The solution developed is a service provided to SMEs in order to assess their needs and identify the kind of AI-enhanced innovation that they can bring into their production lines, and gain benefit from its application. The final solution makes use of a leverage on novel Ai-related technologies (AR/VR, …).

Training algorithms on different datasets from different experiences allow the acquisition of methodologies from other production sectors, fostering cross-contamination.