Smart Control is unique multifunction control tool which can assist the manual layup or robotic pick and place of preform from automated cutting tables. It also adds to quality management by detecting faults in the ply weave, contamination or debris throughout the layup process.
It utilises a camera, a laser and multi-use software system to enable key features of ply recognition and identification, fibre orientation, ply positioning and fault detection.
Most importantly; it can not only identify where the ply should be placed on the mould but unlike conventional laser systems it confirms the exact position is correct to within 0.2mm.
It can identify randomly placed ply shapes in order for robotic pickup and placement. Smart Control identifies the direction or orientation of the fibre within ply in order to layup multidirectional layers correctly whilst simultaneously detecting defects in the material as well as any stray fibre or debris within the process. Smart Control can measure three dimensionally to ensure the layup is without creases, kinks or wrinkles and can be utilised as a manual aid or fully automated robotic solution.
After calibration, SMART CONTROL camera will start by taking and saving pictures, correct the fisheye effect, and compare, identify them to the CAD database supplied by the customer.
Different modules are available according to your needs:
SHAPE IDENTIFICATION: SMART CONTROL calculates the precise position and orientation on the table and gives support to the TME supervision. This allows the robot to get the instruction of when and how to pick up and place the plies in the right sequence and timing.
ORIENTATION CONTROL: SMART CONTROL controls the good fiber orientation of all the plies before and after positioning.
PLY POSITIONING: SMART CONTROL can realize a comparison between the theoretical and real position of the ply edges thanks to the ply contour detection. This secures the automated process as it controls the right positioning during stacking.
DEFAULT DETECTION: SMART CONTROL also detects defects, wrinkles, or foreign objects that might pollute an area of the composite ply via actual-theoretical comparison or without image references. The list of potential defects can be upgraded continuously according to new data. TME also introduced the concept of Deep-Learning in this defect research.