Fraunhofer Institute for Industrial Mathematics ITWMWhat is the detailed structure of a tire? Which layers are located where, how uniform are they, and does the manufactured tire correspond exactly to the specifications? With the »Tire Section Scanner« at Fraunhofer ITWM, tire cross-sections can be captured in high resolution and evaluated automatically using modern AI methods.
Our section scanner can capture high-resolution images of tire cross-sections and evaluate them automatically using modern AI methods.
Accelerate your tire development and quality assurance with AI-supported analysis of tire sections. Our »Tire Section Scanner« combines high-resolution image capture with modern Deep Learning segmentation.
© Fraunhofer ITWM
Our Tire Section Scanner
Analysis of Tire Sections Using AI Image Segmentation at Fraunhofer ITWM
High-resolution imaging forms the basis of the system. A color line scan camera from Basler with 4096 pixel resolution, dual sensor technology, and a 5 GigE interface generates consistent and detailed scans of tire cross-sections. The resulting image data preserves even the finest structures and clear material and layer boundaries, thus creating the conditions for precise analysis of the inner tire structure.
Integration Into Simulation and Engineering Workflows
The processed data can be used directly as preprocessing for »CDTire Parameter Identification« and for FE simulations. The PI (Parameter Identification) module is used to systematically identify and calibrate model parameters from measurement data, such as from test bench trials or structural analyses. This makes simulation-based analyses significantly more efficient, as geometry and material information is provided in a structured and consistent manner.
AI-supported segmentation and structural analysis
Our customized deep learning pipeline for pixel-precise segmentation is applied to this data. It is optimized for execution exclusively on the CPU and delivers high throughput for full-resolution images without dedicated GPUs.
The system automatically detects and identifies key reinforcement layers, including:
- Bead
- Carcass
- Cap Ply
- Belt
The precise localization and classification of these components creates a transparent image of the inner tire structure. This forms the basis for objective, reproducible, and quantitatively evaluable assessment.
AI driven identification of different rubber zones
Quality assurance in development and production
In addition, the Tire Sectional Scanner is suitable for quality control in tire production and enables the collection of precise, reproducible, and adjustable key figures, including:
- Belt Width
- Bead Geometry
- Sidewall Thickness
- Tread Gauge
- Bead Filler Height
Deviations in layer thickness, position, or material distribution can be detected and evaluated at an early stage. In this way, the system helps to accelerate development processes, ensure manufacturing quality, and optimize the performance of modern tires in the long term.
From research and development to production, our Tire Section Scanner delivers quickly available and reliable data. The results obtained can be seamlessly integrated into existing technical workflows.
Contact us to integrate AI-supported cross-sectional analysis into your laboratory or production line and efficiently accelerate simulation and quality control.