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FUNDED BY

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NEWSFEED

AI REVOLUTION IN TECHNOLOGY DEFINITION

November 30, 2023

Read on the project results.

PCSS AS A PROJECT PARTNER

October 30, 2023

Visit a PCSS website to learn about the project.

WE ARE STARTING A NEW PROJECT AIM2ASSIST

June 1, 2023

We wanted to share the happy news of the launch of a new project, this time in the field of AI supporting manufacturers.

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ABOUT AIM2ASSIST

AI Manufacturing technologist ASSISTant for highly variable lot-size-one production

PROBLEM

Today's SME lot-size-one manufacturers are struggling with production efficiency compared to large manufacturers, due to the fact that they do not have enough resources to use the most modern tools for managing production processes, such as MOM or MES systems, not to mention AI solutions. The entry barrier for them is, on the one hand, the cost of such solutions and, on the other, the lack of human resources to exploit the full potential of these tools. Among all MOM deployment preparatory steps, production technologies data input is the most significant barrier to entry, as it is highly labour-intensive and at the same time is critical for the successful deployment of the system. This very often discourages SMEs from investing in solutions that automate and optimize production forecasting and planning.
In many industries, use of CAD tools at the beginning of the production process is a standard. Based on such design, the entire production technology is planned, i.e. the resources needed (workers, machines, robots, materials, semi-products), the individual tasks and production steps and their execution time. Entering data from the CAD projects into the software supporting manufacturing operations management is highly time-consuming, as is further manual planning of required resources and time needed. Such an approach also results in a high degree of inaccuracy in the abovementioned predictions and is fraught with additional time and workload should any changes or deviations from the original assumptions occur. The more variable is the production, the more effort needs to be put into the technology preparation process and its later adoption to changing circumstances.

PROJECT PARTNERS

MCH POLSKA

PSNC

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This project has received funding within the FSTP mechanism of the DIH4AI project founded from the European Union Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement n° 101017057.

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