Extrusion International 4-2024-USA

41 Extrusion International 4/2024 ing on a single point allowed sen- sor measurements with an accuracy of within 0.00046mm; the accuracy when sample positioning of within 0.0045mm reflected the ability to place the liner consistently and pre - cisely. In dynamic scanning scenarios, where the robot performs linescans, the system maintains a level of re - peatability of within 0.0084mm, cru- cial for capturing detailed measure - ments during movements. Taking into account positioning thresholds for the measurement itself, an angle deviation within 5 degrees still ensures continuous re - sults. The absolute position of the workspace is more flexible and can be varied in a range of within 10mm. Compared with cobots, industrial robots used in inline applications are even more accurate. Benefits of Using Radar Technology Compared to other technologies, radar technology provides users with a number of advantages: Quality improvement: Constantly increasing demands on component quality, process efficiency and the documentation of process and qual - ity data make it necessary to reduce manual quality assurance processes and to push digitalization. The high precision and extreme robustness of radar-based measurement technol- ogy make a decisive contribution to this, as components can be mea - sured automatically and without a significant amount of manpower. Data quantity: Radar technology increases the amount of data that can be measured, either manu - ally or automatically, over a certain amount of time. Plug-and-play operation: The inline-capable Warp Gauge radar solution is suitable to be used as a browser-based system, with an in- tegrated user interface to visualize measurement results. Alternatively, measurement data can be read out via an OPC-UA interface, processed and combined with other data. This simplifies integration and makes the measuring system flexible with- out any additional hardware. Transparency: Radar technology measures geometries such as wall thickness, distance and diameter of suitable parallel walls. In combina- tion with the sensor position data, further component properties such as contour and ovality are derived. Since the Warp Gauge can perform several measurements per second, a comprehensive picture of the com - ponents is created and local devia - tions are identified. The measuring accuracy and reproducibility of the technology is in the range of a few hundredths of a millimeter. Thin spot detection: In addition to performing visual inspection, weight checks and pressure tests, radar detects thin spots in critical ar - eas. With radar measurements, us- ers can determine if observed thick- ness distribution meets or exceeds given tolerances. Given more time, more areas can be measured. Material savings: An unevenly distributed wall thickness increases cooling time and reduces produc - tivity. Homogeneous wall thickness distribution saves energy andmakes it possible to use up to 5 percent less material. Process control: Access to mea - surements derived from data allows the system to react immediately to batch fluctuations or drifts in the process. The importance of manual component checks and employees’ process knowledge is reduced. Integration of Radar Technology To estimate the potential savings radar technology can offer for a specific blow molding process, it is essential to conduct an analysis of the current situation. This analysis involves evaluating various factors such as the current quality assurance methodology, the effort required, component variability, the number of measurement points, cycle time and level of automation. Radar measurements conducted in a labo - ratory setting with the assistance of robotics can help determine the quality and quantity of data that can be obtained, thus facilitating an ROI calculation. Both iNOEX and the blowmolding machine OEM can provide recommendations or offer individualized designs for assisting kinematics, taking into account the complexity of the components and their measurement tasks. Additionally, a radar solution in - tegration partner, such as the OEM, can play a fundamental role in de - veloping control loops or imple - menting AI-based machine learning algorithms. iNOEX GmbH Maschweg 70, 49324 Melle, Germany www.inoex.de