Cinzia Giannetti
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A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in …
RS Ransing, C Giannetti, MR Ransing, MW James
Computers in Industry 64 (5), 514-523, 2013
A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data
C Giannetti, RS Ransing, MR Ransing, DC Bould, DT Gethin, J Sienz
Computers & Industrial Engineering 72, 217-229, 2014
Risk based uncertainty quantification to improve robustness of manufacturing operations
C Giannetti, RS Ransing
Computers & Industrial Engineering 101, 70-80, 2016
A quality correlation algorithm for tolerance synthesis in manufacturing operations
RS Ransing, RS Batbooti, C Giannetti, MR Ransing
Computers & Industrial Engineering 93, 1-11, 2016
Knowledge management and knowledge discovery for process improvement and sustainable manufacturing: a foundry case study
C Giannetti, RS Ransing, MR Ransing, DC Bould, DT Gethin, J Sienz
Sustainable Design and Manufacturing, 254-266, 2014
If only my foundry knew what it knows…”: A 7Epsilon perspective on root cause analysis and corrective action plans for ISO9001: 2008
HM Roshan, C Giannetti, MR Ransing, RS Ransing
71st World Foundry Congress, Bilbao, Spain, 2014
Machine Learning as a universal tool for quantitative investigations of phase transitions
C Giannetti, B Lucini, D Vadacchino
Nuclear Physics B 944, 114639, 2019
Optimisation process for robotic assembly of electronic components
KT Andrzejewski, MP Cooper, CA Griffiths, C Giannetti
The International Journal of Advanced Manufacturing Technology 99 (9-12 …, 2018
A framework for improving process robustness with quantification of uncertainties in Industry 4.0
C Giannetti
2017 IEEE International Conference on INnovations in Intelligent SysTems and …, 2017
Organisational knowledge management for defect reduction and sustainable development in foundries
C Giannetti, MR Ransing, RS Ransing, DC Bould, DT Gethin, J Sienz
International Journal of Knowledge and Systems Science (IJKSS) 6 (3), 18-37, 2015
A deep learning framework for univariate time series prediction using convolutional LSTM stacked autoencoders
A Essien, C Giannetti
2019 IEEE International Symposium on INnovations in Intelligent SysTems and …, 2019
Segmentation and generalisation for writing skills transfer from humans to robots
C Li, C Yang, C Giannetti
Cognitive Computation and Systems 1 (1), 20-25, 2019
Product specific process knowledge discovery using co-linearity index and penalty functions to support process FMEA in the steel industry
C Giannetti, MR Ransing, RS Ransing, DC Bould, DT Gethin, J Sienz
Proceedings of the 44th International Conference Computer Industrial …, 2014
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
A Essien, C Giannetti
IEEE Transactions on Industrial Informatics 16 (9), 6069-6078, 2020
Motion optimisation for improved cycle time and reduced vibration in robotic assembly of electronic components
MP Cooper, CA Griffiths, KT Andrzejewski, C Giannetti
AIMS Electronics and Electrical Engineering 3 (3), 274-28, 2019
A Scalable Deep Convolutional LSTM Neural Network for Large-Scale Urban Traffic Flow Prediction using Recurrence Plots
AE Essien, G Chukwkelu, C Giannetti
2019 IEEE AFRICON, 1-7, 2019
A robust design of an innovative shaped rebar system using a novel uncertainty model
N Aldoumani, HH Khodaparast, C Giannetti, Z Abdallah, IM Cameron, ...
Structural and Multidisciplinary Optimization 58 (4), 1351-1365, 2018
Risk Based Tolerance Synthesis and Uncertainty Quantification to Improve Robustness of Industry 4.0 Processes
C Giannetti, S Campus, RS Ransing, B Campus, C Burrows
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