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Jun 24 – 28, 2019
Crowne Plaza Brussels Le Palace
Europe/Zurich timezone

Industrialization of 16T Nb3Sn magnet production for HE-LHC and FCC

Jun 27, 2019, 9:30 AM
20m
Ballroom II (Ground floor)

Ballroom II

Ground floor

Presentation Superconducting magnets & associated technologies FCC-hh accelerator (EuroCirCol)

Speaker

Mr Ananda Chakraborti (Tampere University (FI))

Description

Cost-effective manufacturing of Nb3Sn magnets for HE-LHC and FCC could be achieved through optimization of HL-LHC magnet manufacturing performance using key performance indicators (KPI) such as cost and quality. However, optimization of Nb3Sn magnet manufacturing performance is computationally expensive due to the large number of manufacturing parameters, design variables, and KPI, whose interrelationships need to be modeled and optimized in order to achieve target performance. Thus, probabilistic modeling using Bayesian networks and dimensional analysis conceptual modeling (DACM) framework is proposed to model production cost. Next, a dimension reduction method using graph centrality theory is proposed to enable screening of variables into groups for optimization, based on their level of influence on performance targets.
To achieve KPI-driven performance optimization based on real data from HL-LHC magnet production, a continuous production monitoring platform known as Manufacturing Execution System (MES) is proposed considering the requirements of the production. The MES implementation is assisted by Leanware (Finland) to provide functionalities such as, resource monitoring, magnet component traceability within facility, production scheduling and execution, and support in-process quality control. The MES, Bayesian Networks and dimension reduction based analytics methods enable accurate cost-drivers identification and cost-driven optimization in Nb3Sn magnet production.

Primary author

Mr Ananda Chakraborti (Tampere University (FI))

Co-authors

Mr Suraj Panicker (Tampere University (FI)) Mr Pekka Saarelainen (Leanware Oy (FI)) Dr Daniel Schoerling (CERN) Prof. Eric Coatanéa (Tampere University (FI)) Prof. Kari Koskinen (Tampere University (FI))

Presentation materials