Challenge
Static robot guidance is commonly used at production lines when welding car bodies at robot workstation cells. The car body must be mechanically lifted and placed in a precise position. The ultimate position of the car body is pre-set using the RPS (Reference Position System) and cannot be changed dynamically. The car body is positioned by lowering the RPS holes in the car chassis over guiding pins. A mechanical clamping device is used to hold the car body in a precise position. The technology is not only demanding for the initial investment but also prone to mechanical damage and increased maintenance costs. In addition, the whole process is not efficient because the vehicle body must be mechanically lifted, which consumes production time.
Solution
BeeYard was used as a platform for development of a robust contact-less solution for dynamic robot guidance utilizing machine-learning and AI capabilities. The ultimate requirement was to optimize the process while reducing production costs as well as the risks of mechanical failure.
Dynamic guidance of welding robots means that every time the car body approaches the robot workstation cell, the robots need to adjust their welding paths to the actual location of the car body. This is enabled by a powerful machine learning model that estimates precisely the current position processing data from 2D and 3D images combined with pre-taught position data.
Principles
-
LASER TRIANGULATION
-
COMBINATION OF 2D AND 3D ANALYSIS
-
CAMERA MODULE CALIBRATION
-
SYSTEMATIC DATA COLLECTION
-
BASED ON MACHINE LEARNING
-
FUSION OF FOUR INDEPENDENT MODULES
Significant cycle time reduction
-
24/7 MISSION CRITICAL OPRATION
-
FAST MODULE EXCHANGE
-
HIGH PRECISION
-
CONTINUOUS MONITORING
-
HIGH AVAILABILITY
-
TIME OPTIMIZATION
-
EASY MAINTENENCE
Challenge
Various markers, such as color and adhesive markers, are used as orientation guide while mounting tires on the rim and must therefore be clearly visible and valid. Tire manufacturers must ensure that the pieces produced have markers of the required quality. Otherwise, they risk a complaint, where the customer refuses to take over the entire manufactured batch. So far, the quality inspection has been done manually and has not ensured 100% reliability that the markings on the tires are really free of defects. This generated high additional costs both associated with the manual inspection and with the costs of complaints and returns. Also, the reputation of tire manufacturers and overall brand awareness deteriorated if the quality of supplies did not meet the expectations of customers of these premium brands. The ultimate goal was to automate the manual process of quality verification of each single tire on the manufacturing line utilizing robust machine learning algorithms for overall vision control and monitoring of the production.
Solution
BeeYard helped to develop a robust optical solutions for automated quality control and detection of defective pieces. The system leverages the power of a robust machine learning capabilities that deliver reliable results even for demanding industrial conditions such as various illumination, dirt on the tires, or light reflections on the acquired 2D images.
The deployment of an automated vision system makes the process of quality assurance much more reliable and precise as no defective tire goes undetected.
