Advanced AI models and 3D Real-time Production Data Visualizations

Published March 16, 2026

Software Developer Jarkko Tirkkonen

Finnish-Turkish Collaboration Project Brings Automotive Manufacturers to the Next Level with Advanced AI models and 3D Real-time Production Data Visualizations

Robolaunch Industry Cloud Platform, Academic Sight InspectAI, LightningChart Ltd Dashtera®, and Sakarya University of Applied Sciences team join to create an end-to-end industry cloud solution that empowers automotive manufacturers to derive actionable insights from real-time production data through advanced AI models for predictive maintenance and quality control and visualize results through intuitive dashboards.

Co-funded by Business Finland and Tübitak, the consortium combines expertise in industrial cloud infrastructure, robotics, computer vision, and high-performance data visualization in the VICA project (Visualization and Cloud-AI for Automotive) to validate the integration of the technologies through collaborative R&D efforts. The consortium has tailored a solution to meet the specific needs and challenges of automotive manufacturing, offering a powerful solution for real-time production data collection, feeding gathered data to a computer vision pipeline offered by Academic Sight on running on robolaunch Platform and visualizing results through Dashtera® dashboards.

The project, started in 2025, has achieved scientific and technological excellence by leveraging advanced technologies such as Kubernetes, GPU-acceleration, IIoT and AI. The project methodology has involved deploying robolaunch Industry Cloud Platform on GPU-equipped on-prem servers within a factory environment. Through the seamless integration of Dashtera®, GPU-accelerated real-time dashboards to robolaunch Platform, the end users gain intuitive data visualization capabilities.

A Cloud-Native Foundation for Industrial AI & Robotics

In the project, robolaunch Industry Cloud Platform, provides the foundational infrastructure that connects all project building blocks from development to production environments.

The platform offers:

GPU-accelerated Kubernetes infrastructure
Containerized development environments for AI model training and deployment
AI inference capabilities via standardized model-serving frameworks
Robotics simulation and orchestration tools
Secure data streaming and time-series storage systems

By deploying this infrastructure on GPU-equipped on-premise servers within an industrial environment, the project establishes a scalable and secure backbone capable of supporting real-time AI applications in demanding automotive production settings.

Robotics Integration: From Production Line to Intelligent Inspection

A central element of the VICA project is the integration of collaborative robotics (cobots) into AI-supported inspection workflows.

Within the envisioned production scenario, a cobot performs inspection or material handling tasks in coordination with production line signals. The robotic system generates continuous telemetry data — including joint positions, motion trajectories, execution states, and cycle timing — which is streamed into the robolaunch Industry Cloud Platform. At the same time, PLC-originated signals such as line speed and synchronization triggers are integrated to align robotic actions with production flow. This synchronized architecture enables inspection processes where robotic motion, AI inference, and visualization layers operate cohesively.

The result is a structured real-time data ecosystem that combines:

Robotic telemetry
Production line signals
Vision system outputs
AI inference metadata
Infrastructure performance metrics

All data streams are processed within a unified cloud-native environment, preparing the system for scalable AI-driven inspection and monitoring.

Real-Time Visualization with Dashtera®

LightningChart’s Dashtera® dashboards provide GPU-accelerated real-time visualization capabilities integrated directly with the robolaunch Industry Cloud Platform. Through seamless real-time data streaming, users can monitor synchronized robotic motion, production signals, and AI inference outputs in intuitive dashboards. The visualization layer supports:

High-frequency time-series monitoring
Multi-source signal synchronization
AI inference performance metrics
Inspection result visualization
Early-stage 3D spatial data representations

By transforming complex industrial signals into accessible visual intelligence, Dashtera® enhances transparency and supports faster data-driven decision-making in manufacturing environments.

AI for Quality Control and Production Transparency

Academic Sight contributes advanced computer vision and AI algorithms designed for surface defect detection on automotive components. Integrated into the robolaunch Industry Cloud Platform, these models operate within a GPU-accelerated inference pipeline, enabling real-time analysis of inspection data.

The combination of robotics, AI inference, and synchronized visualization aims to strengthen:

Production transparency
Surface quality monitoring
Part-level traceability
Data-driven process optimization

Validation and Academic Testbed

The final-stage physical validation of the VICA project is planned to take place at Sakarya University of Applied Sciences, where the integrated platform will be evaluated in a structured academic–industrial environment.

This validation phase will assess system performance, scalability, and usability, preparing the solution for broader industrial adoption.

Toward an Integrated Industrial AI Ecosystem

Through an edge-to-cloud approach, diverse factory data — including PLC signals, robotic telemetry, vision system outputs, and selected IIoT data streams — is securely collected, processed, and streamed within a unified cloud-native architecture. This enables engineers to leverage containerized development environments and GPU-accelerated infrastructure to develop, deploy, and validate AI models operating on real-time production data. The solution supports automotive manufacturers in building custom industrial applications and monitoring synchronized data streams through intuitive dashboards, enhancing overall visibility and operational insight.

The unique value of this solution lies in the complementary expertise brought together under the VICA project. Robolaunch provides the Industry Cloud Platform, serving as the secure and scalable backbone for data ingestion, storage, orchestration, and AI inference across manufacturing environments. LightningChart complements this infrastructure with GPU-accelerated real-time Dashtera® dashboards, enabling high-performance visualization of complex industrial data streams. Academic Sight contributes advanced computer vision and artificial intelligence algorithms designed for surface defect detection and AI-supported inspection workflows in automotive production scenarios.

This collaboration combines cloud-native infrastructure, real-time industrial visualization, and AI-driven quality inspection capabilities to support automotive manufacturers in optimizing production processes, improving traceability, and strengthening quality assurance practices. The integrated approach lays the foundation for scalable, AI-ready manufacturing systems that enhance operational efficiency and long-term competitiveness.

The project is expected to continue through 2026.

Simulation Scene (NVIDIA Isaac Sim on Robolaunch)

Simulation environment of an automotive inspection scenario developed in NVIDIA Isaac Sim and executed on the robolaunch Industry Cloud Platform. The scene depicts a robotic inspection cell with collaborative robots positioned alongside a production line, simulating surface inspection workflows under GPU-accelerated workloads.

Dashtera Visualization of Simulation Data

Dashtera® dashboard displaying real-time visualization of simulation telemetry, including robotic joint positions, joint velocities, and conveyor belt speed data streamed from NVIDIA Isaac Sim through the robolaunch Industry Cloud Platform

Simulation-to-Physical Validation

Side-by-side view of the digital simulation environment (left) running on the robolaunch Industry Cloud Platform and the corresponding physical test setup (right), illustrating the alignment between simulated robotic motion and real-world collaborative robot deployment on a linear rail system.

Dashtera 3D Visualization of Physical Test Space

Dashtera® 3D dashboard visualizing physical robotic test data, including tool center point (TCP) trajectories and spatial position tracking overlaid with a point cloud representation of the inspected object within a synchronized industrial coordinate system.

Dashtera Time-Series Visualization of Physical Robot Data

Dashtera® time-series dashboard presenting synchronized robotic joint position data and part position tracking from the physical test setup, enabling real-time monitoring of motion behavior and production alignment within the VICA project scenario.

Business Finland

Robolaunch

Tubitak

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