AI-Driven Inline Inspection to Prevent Defects and Process Drift in Advanced Manufacturing

At robolaunch, we develop and deploy automated inline Vision AI systems for surface inspection in industrial production environments, with a particular focus on pre-paint stages of automotive manufacturing, where defects first originate and are most costly to correct. Our current systems operate at high TRL in real OEM environments, detecting subtle surface defects on unpainted metal under challenging lighting and process conditions.

Building on this mature technological baseline, we are interested in forming a collaborative R&D project under Horizon Europe Cluster 4 – Industry to extend automated inspection towards proactive quality assurance and process optimisation, in line with the objectives of the Work Programme.

The envisioned project would focus on avoiding the production of defective parts by advancing AI-based inspection systems to:

• detect process drift and anomalies in real time through continuous visual and process data analysis,

• support early corrective actions before defects propagate downstream, and

• contribute to improved operational efficiency, reduced scrap, and lower environmental impact.

Key R&D directions include:

• combining Vision AI, synthetic data, and digital twins to model expected surface and process behaviour,

• enabling data-efficient and explainable AI models suitable for dynamic production environments,

• extending inspection from isolated checkpoints to multi-stage, inline quality pipelines, and

• integrating inspection outputs with industrial AI infrastructures and factory-level optimisation systems.

Robolaunch aims to contribute as a technical partner or technology lead, providing validated inline inspection systems, applied expertise in computer vision and synthetic data, and access to real industrial use cases. We are open to collaborating with experienced Horizon Europe coordinators, industrial partners, and research organisations to jointly shape a competitive consortium and proposal aligned with upcoming DIGITAL-EMERGING Cluster 4 calls.