Business Reinforcement by integrating Artificial Intelligence in New advanced manufacturing Solutions

Tembo’s use case addresses the challenge of varying sealing strength leading to leaking pods, causing system shutdowns and cleaning. Their approach focuses on two main objectives: optimising parameter control to minimise leaks by determining and measuring influencing parameters, and reducing machine footprint by optimising relevant design variables and process parameters.

 
Zuidberg’s use case aims to optimise the PTO assembly process by collecting and using real-time process data for operator instructions. In particular, it looks at using AI to improve and simplify quality control for use with multiple product types.

 
AWL’s use case focuses on creating jig-free machine concepts for joining products, specifically through welding or assembling individual parts. Their proposed solution involves utilising a robotic system equipped with simple grippers, each capable of picking up its assigned part and bringing them together. Technical challenges include ensuring the absolute accuracy of the robots, generating optimal robot paths, developing flexible gripper concepts, and effectively connecting products using welding equipment or assembly tooling. Additionally, a quality inspection process will be implemented at the end of the assembly process, utilising vision technology and artificial intelligence for thorough quality control.

 
Bond3D’s use case focuses on improving the flow control of material during 3D printing, particularly for printing PEEK material whose physical properties are highly complex to model. The BRAINS project aims to replace the existing simplified model with a machine learning model which can also take this complexity into account. Because an accurate model is at the basis of good flow rate control performance, the new model will allow for more precise and consistent printing of PEEK material, ultimately enhancing product quality and manufacturing efficiency.

 
Bond3D’s other use case focuses on optimising autofill geometry during printing to improve efficiency, especially for long-duration prints where even small optimisations can save significant time. While standard geometries like squares and rectangles are well-optimised, the challenge lies in efficiently filling exotic shapes that are less common.