Production Data for Predictive Quality

dc.contributor.authorSchmid, Philipp A. E.
dc.date.accessioned2023-09-12T21:35:49Z
dc.date.available2023-09-12T21:35:49Z
dc.date.issued2023-09-12
dc.description.abstractPerfect quality and ever higher efficiency drive Swiss companies. Many production companies have started to store data from machines and test equipment. But what now? How can they gain a real competitive advantage from the data without simply accumulating a gigantic mountain of data waste? With new concepts such as Predictive Quality, holistic solution approaches are available, which also bring a direct appreciation. In his presentation, Philipp Schmid analyzes where the technology stands today, he points out stumbling blocks and uses concrete examples to show the enormous potential of AI in industry.
dc.description.sponsorshipData Booster Innovation Booster Microtech
dc.identifier.citationWorkshop on Digital Twins and Data Usage
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1258
dc.language.isoen
dc.titleProduction Data for Predictive Quality
dc.typeConference
dc.type.csemdivisionsDiv-R
dc.type.csemresearchareasIndustry 4.0
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
2023-09-12_CSEM_Philipp Schmid_Production Data for Predictive Quality.pdf
Size:
1.78 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.82 KB
Format:
Item-specific license agreed upon to submission
Description: