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Ananda Chakraborti

Researcher at Tampere University of Applied Sciences

Publications -  8
Citations -  76

Ananda Chakraborti is an academic researcher from Tampere University of Applied Sciences. The author has contributed to research in topics: Graph (abstract data type) & Performance indicator. The author has an hindex of 3, co-authored 7 publications receiving 38 citations.

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The 16 T Dipole Development Program for FCC and HE-LHC

Daniel Schoerling, +75 more
TL;DR: Several development programs for these magnets, based on Nb3Sn technology, are being pursued in Europe and in the U.S. as mentioned in this paper summarizes and discusses the status, plans, and preliminary results of these programs.
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Systematic manufacturability evaluation using dimensionless metrics and singular value decomposition: a case study for additive manufacturing

TL;DR: A novel approach derived from latent semantic analysis and dimensional analysis to evaluate parts and their production for a variety of selected metrics is proposed, which can support part design decision-making, process selection, and design and manufacturing optimization.
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Tracing the interrelationship between key performance indicators and production cost using bayesian networks

TL;DR: For an additive manufacturing case, it is shown that the approach enables appropriate value estimation for decisions and variables for achieving desired KPI values and production cost targets in a manufacturing enterprise.
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A Dimension Reduction Method for Efficient Optimization of Manufacturing Performance

TL;DR: The proposed dimension reduction method is demonstrated for an optimization problem, to minimize the production cost of the bladder and key mechanism for a high-field superconducting magnet at CERN, capable of producing a 16 Tesla magnetic field.
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Digital twin: Multi-dimensional model reduction method for performance optimization of the virtual entity

TL;DR: A model reduction method is described for a graph-based representation of multi-dimensional DT model based on spectral clustering and graph centrality metric to minimize the total number of variables required for improving the performance of the DT.