Optimatization of a multi-product production line
The main challenge was the optimization of production lines that had to deal with a high variation and amount of products. Traditionally, one injection machine can be coupled with a robot system for packaging or assembly, but this is only financially viable for long series and mostly locks the mould in a specific injection machine, limiting the production flexibility.
The new solution is based on a multi-product production line that is combined with a ScalABLE robot to create an efficient production system even for shorter series. The solution is based on the integration of a collaborative robot with a movable platform, a 3D vision system and the associated software.
The system operates in two different safety modes – relying on the collaborative nature of the robot for a slow speed operation and on two safety lasers for high speed operations when nobody is around. To handle the pick and place operation, a Photoneo sensor and a gripper are attached to the robot. The robotic system is vertically integrated with IT production systems and simulation tools that allow the determination of the best robot allocation at a specific point in time.
The customer decided for the Photoneo sensor due its excellent point cloud quality and the sensor size in relation to the working volume. The application requires the capture of the work area with a good resolution to be able to detect and pick the parts. The solution increases the flexibility of the robotic system. This flexibility will allow the end-user to include more robots in the production even for smaller series.
This development was made in the context of the ScalaBLE4.0 project that is coordinated by INESC-TEC and includes the following partners: PSA (France), University of Aalborg (Denmark), University of Lund (Sweden) and Critical Manufacturing (Portugal). The project includes not only the development of the movable robot but several developments related to the integration of the robot work cell into the production environment, such as MES integration, simulation for production optimization and robot allocation, Plug-n-Produce and others.