The innovative project “EIT-Manufacturing-RIS-Intrapreneurship” uses Artificial Intelligence and Machine Learning in order to provide INLINE and REAL-TIME quality inspection for all the automotive parts on an aluminum production line.
The system’s profound impact on the manufacturing process includes substantial improvements in productivity and environmental footprint through scrap minimization.
Furthermore, increases part production capacity and ensures business continuity and stability through its intelligent nature.
Machined aluminum parts for the automotive industry, often suffer from structural defects.
These defects are reflected to the surface and to deviations from the expected Computer-aided designs (CAD).
Deviations from this strict quality assurance framework have a significant societal, environmental and financial impact and trigger cascading clauses on the whole automotive value chain (tiers, automotive brands, insurance companies).
A holistic state-of-the-art inline Quality Control system for inspecting aluminum parts before down streamed to assembling operations.
The proposed solution uses Artificial Intelligence and Machine Learning in order to provide INLINE and REAL-TIME quality inspection for all parts on the production line.
Furthermore, it incorporates Active Leaning technologies in order to be able to generalize in a variety of aluminum parts while minimizing human intervention.
The main solution:
The profound impact on AI-driven factory transformation will enable manufacturers and other supply chain stakeholders to redefine the industrial processes towards substantial improvements in productivity (through improved efficiency) and environment performance (through scrap minimization).
Furthermore, increasing part production capacity while reducing high-scale claims through intelligent quality control, ensures business continuity, stability and prosperity. Taking over manual or semi-supervised quality control tasks, increases personnel safety and sets a solid ground on human resource upskilling.