The SonicShark® system from Hufschmied Zerspanungssysteme GmbH is used for inline quality control and process monitoring during milling processes. In cooperation with nebumind an extension of the system was implemented. The previously purely visual quality control of a worker is expanded into an automated, self-learning monitoring. With each captured workpiece, the extended system reports rejects „inline“ and constantly optimizes its recognition, independently of adjustments in the milling process. The self-learning defect detection opens up new potential in manufacturing to save time and money in quality assurance. Less resources are required and also the CO2 footprint of each manufactured component is shown.
Ralph Rudolf Hufschmied
Hufschmied Zerspanungssysteme GmbH
„There are many solutions mapping manufacturing data onto a workpiece. But until now, none of these solutions could perform calculations with the data and automatically give recommendations for action. The software from nebumind is unique in this respect and, as an extension of our SonicShark® system, will make quality assurance much more efficient in the future.“
For its quality control during the process, the tool manufacturer Hufschmied Zerspanungssysteme GmbH
has developed a new system inspecting quality inline. Nowadays, the actual milling process is so much optimized in terms of required time, that only little savings potentials is left here. The efficiency potential has rather shifted to quality assurance.
On the search for more efficient quality assurance
The structure-borne sound sensor of the intelligent SonicShark® system evaluates vibrations and acoustic signals in the machine tool room to monitor process, workpiece and tool quality. The sensor records the data time-based in combination with machine data such as position and feed values. The aim of Hufschmied Zerspanungssysteme GmbH is to make such quality assurance even more efficient: On the one hand, the defect detection of the SonicShark® system shall take place inline. On the other hand, the time required to machine, evaluate and report an n.o.k. component shall be significantly reduced. Automated acquisition and evaluation of sensor data is indispensable for this.
For an automated evaluation of the sensor data on the workpiece, the nebumind software was used. The software evaluates sensor data not only time-based, but above all location-based, and can thus locate defects precisely in the component. The worker is automatically shown where a defect has occurred in the workpiece. This way, an n.o.k. component can be identified already during the milling process and replaced right away. In addition, the time required by the operator to find and evaluate the defect is reduced to a minimum.
Improving defect recognition with every new defect
Through the evaluation of new, so far unknown, defects by the worker, a self-learning process takes place in the software. With each newly manufactured workpiece, the software becomes more intelligent – only relevant defects are reported and immediately unloaded.
Another special feature of the software is that the automatic evaluation of sensor data works independently of the process control. This means that a worker can change the milling speed for different workpieces or start the milling process in different areas of the workpieces without disturbing the self-learning quality control. This makes the manufacturing process much more flexible.
Saving time and resources
The recording of CO2 emissions during the process is also enabled through the SonicShark® system, which can also lead to adjustments in the process. Since on average 25 – 30 % of manufacturing costs are caused by quality assurance and testing, there is very large savings potential here. This can be achieved by the SonicShark® system and the software extension by nebumind.