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Quality and Reliability Engineering International - Volume 39 / Issue 2

Title
Authors Volume Issue Page Year PKV
A Tensor‐based Regression Approach for Human Motion Prediction
Lorena Gril, Philipp Wedenig, Chris Torkar, Ulrike Kleb 39 2 481–499 2022 4.0
Average Rating: 0.0 / 5 (0 Reviews)
Generalized likelihood ratio control charts for high‐purity (high‐quality) processes
Caterina Rizzo, Alessandro Di Bucchianico 39 2 523–531 2022 4.0
Average Rating: 0.0 / 5 (0 Reviews)
Second‐level global sensitivity analysis of numerical simulators with application to an accident scenario in a sodium‐cooled fast reactor
Anouar Meynaoui, Amandine Marrel, Béatrice Laurent 39 2 500–522 2022 4.0
Average Rating: 0.0 / 5 (0 Reviews)
A transformed gamma process for bounded degradation phenomena
Mitra Fouladirad, Massimiliano Giorgio, Gianpaolo Pulcini 39 2 546–564 2022 6.0
Average Rating: 0.0 / 5 (0 Reviews)
Large datasets, bias and model‐oriented optimal design of experiments
Elena Pesce, Francesco Porro, Eva Riccomagno 39 2 532–545 2022 5.0
Average Rating: 0.0 / 5 (0 Reviews)
A data‐driven ensemble ranking system of production quality across manufacturers—A case study for risk assessment in the solar industry
Wing Fung Lai, Inez Maria Zwetsloot 39 2 565–574 2022 5.0
Average Rating: 0.0 / 5 (0 Reviews)
Fuzzy FMEA in risk assessment for test and calibration laboratories
Ozlem Muge Testik, Ezgi Tok Unlu 39 2 575–589 2022 2.0
Average Rating: 0.0 / 5 (0 Reviews)
Metamodel‐based optimization of shift planning in high‐bay warehouse operations
Dominik Kirchhoff, Marc Kirberg, Sonja Kuhnt, Uwe Clausen 39 2 590–608 2022 5.0
Average Rating: 0.0 / 5 (0 Reviews)
Imperfect repair models: Sequential goodness‐of‐fit testing based on predictive performances
Mhamed‐Ali El‐Aroui, Olivier Gaudoin 39 2 609–629 2022 5.0
Average Rating: 0.0 / 5 (0 Reviews)
A tensor‐based hierarchical process monitoring approach for anomaly detection in additive manufacturing
Wei Yang, Marco Grasso, Bianca Maria Colosimo, Kamran Paynabar 39 2 630–650 2022 2.0
Average Rating: 0.0 / 5 (0 Reviews)