Celano, Giovanni
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2

The effect of surface roughness on the performance of 3D pr..:

Saitta, Lorena ; Celano, Giovanni ; Tosto, Claudio...
The International Journal of Advanced Manufacturing Technology.  132 (2024)  11-12 - p. 5503-5519 , 2024
 
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Optimal designs of the omnibus SPRT control chart for joint..:

Teoh, Jing Wei ; Teoh, Wei Lin ; Khoo, Michael B.C...
International Journal of Production Research.  62 (2023)  12 - p. 4202-4225 , 2023
 
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Monitoring the multivariate coefficient of variation in pre..:

Sabahno, Hamed ; Celano, Giovanni
Quality Technology & Quantitative Management.  20 (2022)  2 - p. 184-210 , 2022
 
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Projection micro-stereolithography versus master–slave appr..:

Saitta, Lorena ; Celano, Giovanni ; Cicala, Gianluca...
The International Journal of Advanced Manufacturing Technology.  120 (2022)  7-8 - p. 4443-4460 , 2022
 
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8

Design and manufacturing of a surface plasmon resonance sen..:

Saitta, Lorena ; Arcadio, Francesco ; Celano, Giovanni...
The International Journal of Advanced Manufacturing Technology.  124 (2022)  7-8 - p. 2261-2278 , 2022
 
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9

A number‐between‐events control chart for monitoring finite..:

Johannssen, Arne ; Chukhrova, Nataliya ; Celano, Giovanni.
Quality and Reliability Engineering International.  38 (2022)  4 - p. 2110-2138 , 2022
 
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11

The Shewhart-type RZ control chart for monitoring the ratio..:

Nguyen, Huu Du ; Nadi, Adel Ahmadi ; Tran, Kim Duc...
International Journal of Production Research.  61 (2022)  20 - p. 6746-6771 , 2022
 
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12

A bootstrap method for the measurement error estimation in ..:

Celano, Giovanni
Quality and Reliability Engineering International.  38 (2022)  7 - p. 3404-3421 , 2022
 
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14

Monitoring the covariance matrix of bivariate processes wit..:

Machado, Marcela A. G. ; Lee Ho, Linda ; Quinino, Roberto C..
Applied Stochastic Models in Business and Industry.  38 (2021)  1 - p. 116-132 , 2021
 
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15

Control Charts for Monitoring Time-Between-Events-and-Ampli..:

, In: Springer Series in Reliability Engineering; Control Charts and Machine Learning for Anomaly Detection in Manufacturing,
 
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