Manufacturing (WP3)
WP3 focuses on increasing the efficiency and quality of production machinery and technological processes through artificial intelligence methods capable of processing and interpreting large-scale multimodal data. The goal is to improve key performance characteristics such as accuracy, efficiency, reliability, cost-effectiveness, and sustainability.
The main method in WP3 is the development of AI models for predicting the behavior and characteristics of manufacturing machines based on sensor data, and their application for feedback control of manufacturing machines and processes. A key step is the creation of datasets for training AI models based on experiments and synthetic data generated by physical models. This strategy will reduce the costs of experimental testing and also support the applicability of the solution to various types of machines. WP3 will result in adaptive manufacturing systems capable of actively responding to changes in operating conditions and autonomously increasing both productivity and production quality.
What problem does the WP solve, and why is it important?
01
Manufacturing machines and processes exhibit complex behavior that is difficult to model using traditional methods.
02
Low accuracy and high defect rates, requiring time-consuming process fine-tuning by experienced specialists.
03
The available sensor and operational data are not being used effectively for real-time control.
04
Experimental debugging and testing are costly and time-consuming.
05
Growing demands for precision, quality, and sustainability in manufacturing are increasing the pressure for adaptive control.
Examples of use / areas of application and benefits
Machine Tools and Mechanical Engineering
- improved machining accuracy and process stability
- reduction in scrap rates and operational deviations
- faster and more reliable prediction of spatially and temporally dependent machine characteristics
Optomechanical systems and precision engineering
- optimisation of the assembly of high-tech systems
- greater accuracy and long-term stability of the equipment
- elimination of faults in monitoring equipment caused by environmental factors
Additive manufacturing (3D printing)
- proposal of default technical and print parameters using an AI assistant
- detection of areas with sub-optimal temperature on the track, with a proposed correction
- improved stability, repeatability and geometric accuracy of the manufacturing process
Management of iron and steelworks
- the deployment of robust machine vision in the demanding environment of a steelworks
- automatic capture of CCTV images with anomalies or uncertain interpretation
- real-time optimisation of process parameters and ongoing model training by the operator
Key people
WP Leader
doc. Ing. Petr Kolář Ph.D.
Department of Production Machinery and Equipment | Faculty of Mechanical Engineering CTU (FME CTU)
Pavel Švarc
| AIRS, s.r.o.
Ing. Viktor Kulíšek, Ph.D.
| Meopta s.r.o.
Ing. Petr Šrachta
| TGS tools-machines-technological services spol. s.r.o.
Ing. Tomáš Kozlok, Ph.D.
| TOS VARNSDORF a.s.
Participating institutions
Použité technologie a postupy
- Advanced physical modelling of machine and structural properties using the finite element method (FEM)
- Synthetic data and data augmentation to expand training datasets and cover a wide range of operating conditions
- Industrial machine vision, advanced metrology systems and in-process sensor technology
- Compensatory and adaptive algorithms for real-time process control
- Training AI models on hybrid datasets (a combination of experimental measurements and synthetic data)
- Transfer learning for the efficient transfer of trained models between different machines, processes and application domains
- Advanced measurement methods for characterising machine properties and validating models
Used technologies and procedures
2027
Automatic annotation software based on the human-in-the-loop principle, designed for computer vision
R- software
A digital model of the accuracy of an opto-mechanical assembly based on an AI model
Gfunk – funkční vzorek
2028
AI assistant for designing the process parameters for PEM extrusion
R- software
An AI model of a machining centre with enhanced thermal-mechanical accuracy across the working range
Gfunk – funkční vzorek
2029
A high-precision digital model of a device for quality control of opto-mechanical assemblies based on the application of an AI model
Gfunk – funkční vzorek
2031
A tool for managing data pipelines, training models and deploying them into production
R- software
AI assistant for detecting problem areas in 3D printing
R- software
An AI model for a machining centre to improve production efficiency within the working area
Gfunk – funkční vzorek