Validation of the experiment in the AIM2ASSIST project

2024-03-07

Validation of the experiment in the AIM2ASSIST project

The aim of the AIM2ASSIST project, conducted in collaboration with MASTA, MCH Poland and the Poznan Supercomputing and Networking Center, is to overcome the barrier of time and workload required to define production technology in a MeMOM-type system. During the recently completed experiment, the expert team developed and extended the MeMOM platform with three new software components.

he three new software components of the MeMOM platform consist of:

  • CADImporter – The CADImporter component is responsible for storing and presenting CAD designs and generating data for AI learning,
  • AI Technology Predictor (AITP) – A component for predicting manufacturing tasks based on a CAD design,
  • TechnologistWizard (TWizard) – component for creating production technology models (manually or using AITP).

One thousand production designs were defined, which included a total of 4,000 parts. Based on this, the team developed and trained a predictive model built on a dedicated neural architecture using convolutional neural units (CNNs).

The solution was tested and validated in the operating environment of MCH Poland. The result of the test is really promising; the time required for an engineer to implement a complete technology with AI support was reduced on average from 80 minutes to 32 minutes. The tested solution requires further development, but it has the potential to revolutionize the market for small and medium-sized manufacturing companies.

By integrating the solution with MeMOM, one can expect easier customer implementation, which is important to every participant in this process – the enterprise, its employees and software consultants. The trained artificial intelligence model has already been published on the AI4EU Experiments Marketplace.

The project is funded on an FSTP basis from the DIH4AI project under the European Horizon 2020 research and innovation programme – Grant No. 101017057.