RESEARCH NEWS
Industrial inspection quality
In the AutoInspect demonstrator , the car body is transported to the inspection stations on a conveyor system . The image shows the deflectometry portal . © Fraunhofer .
Standard interfaces for sensors
The quality of industrial production processes is ensured by large numbers of sensor-based individual inspections . This generates large amounts of data . However , until now , information from the individual sensors has generally only been looked at in isolation . The AutoInspect solution from the Fraunhofer Institute of Optronics , System Technologies and Image Exploitation IOSB overcomes this issue by linking all the data to create a consolidated overview .
“ Now , for the first time , linking the measured values is facilitating intelligent evaluation and the detection of hidden faults . This increases efficiency and ultimately improves product quality . A demonstrator was presented at the Hannover Messe 2022 from 30 May to 2 June 2022 at our joint booth ,” Fraunhofer told ISMR .
Manual inspection is also integrated into the AutoInspect workflow . Information , inspection tasks and virtual control elements are superimposed on the AR goggles and the user ’ s hand and finger movements are detected . The worker can flag defects on the component itself using a pointing gesture .
A consolidated overview
“ AutoInspect provides a consolidated overview and enables intelligent evaluation of all relevant inspection data and measured values . In this way , previously unrecognized inter-relationships in the manufacturing process suddenly become visible . This makes it easier to identify the causes of faults , which in turn makes the entire production process more efficient . Ultimately , this also improves the quality of the products ,” explained Fraunhofer .
A highlight of AutoInspect is the ability to combine the inspection results with the respective location information . The first step is to create a 3D mesh of the test object based on existing CAD models of the product . However , this depiction in the software goes much further than a conventional 3D computer graphic of an object . This is because every measured value is stored with reference to this 3D mesh i . e ., with the precise location of the measurement position on the test object . This creates a digital twin that contains all relevant sensor data , including the associated location information , plus metainformation such as the batch number of the material used or the time of inspection .
In this way , a consolidated overview of all inspection data is created which , assuming the relevant inspection stations are in place , can cover the entire production process ( from clamping the first piece of sheet metal , shaping the sheet and various bonding and welding processes through to applying the paint ). By linking the measured values in the AutoInspect software , it may now be possible to identify , for example , that a gap dimension is always too large at a certain point if a certain temperature limit value is exceeded during a previous machining step . The inspection team on the shop floor can then follow up on this tip , analyse the cause and ultimately fix the problem . This , in turn , is reflected in altered data and measured values in the 3D mesh . By taking this approach , said Fraunhofer , inspection and production merge seamlessly into “ an optimised and efficient overall process .”
The team at Fraunhofer IOSB developed and tested the technology with sensors for a 3D scan as well as deflectometry and ellipsometry . Ellipsometry , for example , determines the thickness of a surface coating by registering the polarisation state of reflected light . Deflectometry measures and inspects the shape of specular or highgloss surfaces such as painted sheet metal . These measurement techniques and their further development have been a separate object of research at Fraunhofer IOSB for years now .
However , the Fraunhofer solution is not tied to specific sensors , relying instead on the open OPC UA ( OPC Unified Architecture ) interface . “ Any sensor or measuring device that is compatible with OPC UA can be easily integrated into AutoInspect via plug and play ,” said Fraunhofer . It is possible for a worker to carry out manual inspections assisted by augmented reality .
Evaluation of the measurement results is not limited to the current production process or the one just completed . The history of the inspection results within AutoInspect can be analysed beyond the current batch or manufacturing process . This makes it possible to observe the product lifecycle of machines or vehicles across maintenance and inspection intervals . For example , measurement data from previous inspections could be used when inspecting the chassis and wheel tires on an ICE train to better understand the latest measured values .
“ When inspecting safety-relevant components , the ability to consider all of the maintenance cycles , including all of the data in AutoInspect , means that the causes of defects can be traced more quickly . Ideally , problems can even be detected in advance using AI-based data analysis and the safety of the respective machine or plant can be quickly restored ,” concluded Fraunhofer . n
Manual inspection is also integrated into the AutoInspect workflow . © Fraunhofer . www . ismr . net | ISMR July / August 2022 | 19