Travelled to:1 × Sweden
1 × Taiwan
1 × USA
Collaborated with:G.A.Susto A.B.Johnston P.G.O'Hara S.Pampuri J.Wan Marco Maggipinto Federico Zocco P.Kern C.Wolf D.Gaida M.Bongards A.Schirru D.Pagano A.Beghi M.Zanon
Talks about:predict (5) machin (4) process (3) stage (3) learn (3) manufactur (2) metrolog (2) virtual (2) mainten (2) system (2)
Person: Seán F. McLoone
DBLP: McLoone:Se=aacute=n_F=
Contributed to:
2015201420132019Wrote 7 papers:
- CASE-2015-SustoM #approach #machine learning #multi #predict
- Slow release drug dissolution profile prediction in pharmaceutical manufacturing: A multivariate and machine learning approach (GAS, SFM), pp. 1218–1223.
- CASE-2014-KernWGBM #estimation #machine learning #using
- COD and NH4-N estimation in the inflow of Wastewater Treatment Plants using Machine Learning Techniques (PK, CW, DG, MB, SFM), pp. 812–817.
- CASE-2014-PampuriSWJOM #process
- Insight extraction for semiconductor manufacturing processes (SP, GAS, JW, ABJ, PGO, SFM), pp. 786–791.
- CASE-2014-SustoWPZJOM #adaptation #flexibility #machine learning #maintenance #predict
- An adaptive machine learning decision system for flexible predictive maintenance (GAS, JW, SP, MZ, ABJ, PGO, SFM), pp. 806–811.
- CASE-2013-SustoJOM #multi #predict #process
- Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes (GAS, ABJ, PGO, SFM), pp. 201–206.
- CASE-2013-SustoSPPMB #fault #maintenance #predict
- A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation (GAS, AS, SP, DP, SFM, AB), pp. 195–200.
- CASE-2019-MaggipintoSZM #case study #fault #multi #predict #process #what
- What are the Most Informative Data for Virtual Metrology? A use case on Multi-Stage Processes Fault Prediction (MM, GAS, FZ, SFM), pp. 1796–1801.