Kurzfassung
Betriebe schalten Produktionsmaschinen häufig über das Wochenende aus. Pünktlich zum Schichtbeginn müssen die abgekühlten Maschinen wieder auf Betriebstemperatur gebracht werden, wodurch hohe Energiekosten entstehen [1]. Der Beitrag illustriert anhand der thermischen Modellierung einer Produktion vielfältige Einsatzmöglichkeiten von dynamischen Simulationswerkzeugen zur Reduzierung des Primärenergiebedarfs.
Background:
Staple line leaks are a serious problem in bariatric surgery and a major cause of serious morbidity and mortality. Adverse events caused by medical devices are reported to the Food and Drug Administration which maintains the Manufacturer and User Facility Device Experience (MAUDE) database. We examined adverse stapler events reported to the MAUDE database, specifically with regards to bariatric surgery.
Methods:
The MAUDE database was queried for adverse events caused by staplers between January 1, 2018 – December 31, 2020; events reported by Intuitive, Ethicon, and Medtronic/Covidien; and limited our search to “gastric bypass”, “sleeve gastrectomy”, “stapler malfunction” combined with each company.
Results:
There were 883 adverse events reported for Medtronic, 353 for Ethicon, and 35 for Intuitive. Approximately 3.5 million staple reloads sold in the study period. The reported misfire rate for Medtronic was 0.04% and for Ethicon was 0.02%. Data for Intuitive was unavailable. The most common reported event for Medtronic was failure to fire (n = 349), followed by misfire (n = 186). For Ethicon, the most common event was failure to fire (n = 146), followed by mechanical problems (n = 27). The most common event with the Intuitive stapler was leak (n = 10) and bleeding from staple line (n = 8).
Conclusions:
Stapler malfunction is a very rare event in metabolic and bariatric surgery. All of the major stapler producers have transitioned to powered staplers with excellent safety profiles. Open and honest reporting about stapler malfunction is essential to determine the true safety of these ubiquitous devices.
Process-data-supported process monitoring in injection molding plays an important role in compensating for disturbances in the process. Until now, scalar process data from machine controls have been used to predict part quality. In this paper, we investigated the feasibility of incorporating time series of sensor measurements directly as features for machine learning models, as a suitable method of improving the online prediction of part quality. We present a comparison of several state-of-the-art algorithms, using extensive and realistic data sets. Our comparison demonstrates that time series data allow significantly better predictions of part quality than scalar data alone. In future studies, and in production-use cases, such time series should be taken into account in online quality prediction for injection molding.
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