. Testing the immersive interaction in a digital twin environment of a real scenario.
. Evaluation of AI-provider capabilities of automatically creating source code and 3D environments
. Evaluate and visualize in 3D data of the AI-provider neural network
Manufactuirng companies and asset provider - Software solution provider for digital twin and XR services
Test and validation of existing advanced AI-based solutions that can add health monitoring and predictive maintenance functions, with which the plant could predict potential failures before they occur and could provide the suggestion on how to solve them.
Tool, machinery and equipoment provider
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Report with recommendations and profiles of identified solution elements
Industrial engineers, systems engineers, manufacturing companies
The aim is to validate an AI-based solution able to continuously adjust its configuration in order to improve its performance and robustness over time
Manufacturing companies, Equipment provider, OEM
Significant reduction of AI hallucinations, improved data retrieval accuracy, validation of groundedness (adherence to factual information), and increased reliability of agents in multi-step tasks.
Manufacturing & Automotive
Feasibility report, measurement results
WPT technology provider
Feasibility report, measurement results
Robotics / Intralogistics technology provider
Feasibility report, measurement results
Robotics / Intralogistics technology provider / user
Feasibility report, measurement results
Robotics / Intralogistics technology provider / user
Feasibility report, measurement results
WPT technology provider
AI – based solution: 1) Automated workflow of production planning activities; 2) Automated data collection from market and specific clusterization of customer requirements; 3) Intelligent forecasting of market demand
Industrial Company – S&O planning tool provider
AI-based software for manufacturing where each application feature works as per the identified software requirements
AI-based software for manufacturing Provider
A documented AI development and testing outcome for the selected quality control application, including trained model results, evaluation of smart camera performance, and recommendations for implementation, further development, and user adoption.
Manufacturing, Quality control and inspection, Machine vision, Robotics and automation, Industrial AI applications
User - Manufacturing companies operating in high-mix, low-volume and high-complexity environments that need reliable part identification to automate post-processing