Abstract
Faster optimization algorithms, increased computer power and amount of available data, can leverage the area of simulation towards real-time control and optimization of products and production systems. This concept – often referred to as Digital Twin – enables real-time geometry assurance and allows moving from mass production to more individualized production.
To master the challenges of a Digital Twin for Geometry Assurance the project Smart Assembly 4.0 gathers Swedish researchers within product development, automation, virtual manufacturing, control theory, data analysis and machine learning. The vision of Smart Assembly 4.0 is the autonomous, self-optimizing robotized assembly factory, which maximizes quality and throughput, while keeping flexibility and reducing cost, by a sensing, thinking and acting strategy. The concept is based on active part matching and self-adjusting equipment which improves geometric quality without tightening the tolerances of incoming parts. The goal is to assemble products with higher quality than the incoming parts. The concept utilizes information about individual parts to be joined (sensing), selects the best combination of parts (thinking) and adjust locator positions, clamps, weld/rivet positions and sequences (acting).
The project is ongoing, and this paper specifies and highlights the infrastructure, components and data flows necessary in the Digital Twin in order to realize Smart Assembly 4.0. The framework is generic, but the paper focuses on a spot weld station where two robots join two sheet metal parts in an adjustable fixture.