Documents disponibles écrits par cet auteur

Artificial vision system for the automatic measurement of interfiber pore characteristics and fiber diameter distribution in nanofiber assemblies / Emanuele Tomba in Industrial & engineering chemistry research, Vol. 49 N° 6 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 6 (Mars 2010) . - pp. 2957–2968 Titre : Artificial vision system for the automatic measurement of interfiber pore characteristics and fiber diameter distribution in nanofiber assemblies Type de document : texte imprimé Auteurs : Emanuele Tomba, Auteur ; Pierantonio Facco, Auteur ; Martina Roso, Auteur Année de publication : 2010 Article en page(s) : pp. 2957–2968 Note générale : Industrial Chemestry Langues : Anglais (eng) Mots-clés : Artificial--Vision--System--Automatic--Measurement--Interfiber--PoreFiber--Diameter--Fiberribution--Nanofiber Résumé : Nanofiber structures are used in several technologies such as membranes, reinforced materials, textiles, catalysts, sensors, and biomedical materials. For all these applications, it is important to know the morphology of the assemblies, in particular their pore and fiber dimension distributions. However, the current methods used to measure pore sizes are all experimental and indirect; furthermore, the fiber diameter distribution is usually determined manually using a digital image of the nanofiber web. In this paper an artificial vision system is proposed to characterize the nanofiber web by automatically measuring several properties related to the interfiber pore distribution and to the nanofiber diameter distribution. The artificial vision system is characterized by a two-section structure: an image processing section and a property measurement section. The image processing section is centered on a multivariate image analysis procedure for the extraction of morphological features from the image. The property measurement section comprises an algorithm for interfiber pore area and pore morphology evaluation and one for fiber diameter distribution measurement that also accounts for the effect of perspective on the lower-level fiber diameters. Because the proposed artificial vision system is completely automatic, measurements can be taken without the need of any experimental setup and with no human intervention. Therefore, besides being fast and accurate, measurements do not suffer from repeatability issues. The ability of the proposed automatic system in characterizing the morphology of a thin nonwoven nanofiber fabric is demonstrated by application to polymer nanofiber membranes obtained by electrospinning. Note de contenu : Bbibiogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901179m [article] Artificial vision system for the automatic measurement of interfiber pore characteristics and fiber diameter distribution in nanofiber assemblies [texte imprimé] / Emanuele Tomba, Auteur ; Pierantonio Facco, Auteur ; Martina Roso, Auteur . - 2010 . - pp. 2957–2968. Industrial Chemestry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 6 (Mars 2010) . - pp. 2957–2968 Mots-clés : Artificial--Vision--System--Automatic--Measurement--Interfiber--PoreFiber--Diameter--Fiberribution--Nanofiber Résumé : Nanofiber structures are used in several technologies such as membranes, reinforced materials, textiles, catalysts, sensors, and biomedical materials. For all these applications, it is important to know the morphology of the assemblies, in particular their pore and fiber dimension distributions. However, the current methods used to measure pore sizes are all experimental and indirect; furthermore, the fiber diameter distribution is usually determined manually using a digital image of the nanofiber web. In this paper an artificial vision system is proposed to characterize the nanofiber web by automatically measuring several properties related to the interfiber pore distribution and to the nanofiber diameter distribution. The artificial vision system is characterized by a two-section structure: an image processing section and a property measurement section. The image processing section is centered on a multivariate image analysis procedure for the extraction of morphological features from the image. The property measurement section comprises an algorithm for interfiber pore area and pore morphology evaluation and one for fiber diameter distribution measurement that also accounts for the effect of perspective on the lower-level fiber diameters. Because the proposed artificial vision system is completely automatic, measurements can be taken without the need of any experimental setup and with no human intervention. Therefore, besides being fast and accurate, measurements do not suffer from repeatability issues. The ability of the proposed automatic system in characterizing the morphology of a thin nonwoven nanofiber fabric is demonstrated by application to polymer nanofiber membranes obtained by electrospinning. Note de contenu : Bbibiogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901179m

General framework for latent variable model inversion for the design and manufacturing of new products / Emanuele Tomba in Industrial & engineering chemistry research, Vol. 51 N° 39 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 39 (Octobre 2012) . - pp. 12886-12900 Titre : General framework for latent variable model inversion for the design and manufacturing of new products Type de document : texte imprimé Auteurs : Emanuele Tomba, Auteur ; Massimiliano Barolo, Auteur ; Salvador García-Muñoz, Auteur Année de publication : 2012 Article en page(s) : pp. 12886-12900 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Manufacturing  Design  Modeling Résumé : Latent variable regression model (LVRM) inversion is a useful tool to support the development of new products and their manufacturing conditions. The objective of the model inversion exercise is that ot finding the best combination of regressors (e.g., raw material properties, process parameters) that are needed to obtain a desired response (e.g., product quality) from the model. Each of the published applications where model inversion has been applied utilizes a tailored approach to achieve the inversion, given the specific objectives and needs. These approaches range from the direct inversion of the LVRM to the formulation of an objective function that is optimized using nonlinear programming. In this paper we present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM. The different sets of equations that become relevant (either as a term within the objective function or as a constraint) are discussed, and an example of these scenarios is also provided. Additional to the formulation of the different scenarios and their objective functions, this work proposes a new metric (the P2 statistic) to cross-validate the ability of the model to reconstruct the regressor vector (analogous to the Q2 statistic aimed at the predictability of the response). This new metric comes from the need to not only predict the response from the regressor, but to also reconstruct the regressors from the scores values. In this context, a discussion is provided on the effect of uncertainty in the reconstruction of the regressor (the actual design) as these values are normally given upstream as targets to the supplier of materials, or as set points to the process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26419245 [article] General framework for latent variable model inversion for the design and manufacturing of new products [texte imprimé] / Emanuele Tomba, Auteur ; Massimiliano Barolo, Auteur ; Salvador García-Muñoz, Auteur . - 2012 . - pp. 12886-12900. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 39 (Octobre 2012) . - pp. 12886-12900 Mots-clés : Manufacturing  Design  Modeling Résumé : Latent variable regression model (LVRM) inversion is a useful tool to support the development of new products and their manufacturing conditions. The objective of the model inversion exercise is that ot finding the best combination of regressors (e.g., raw material properties, process parameters) that are needed to obtain a desired response (e.g., product quality) from the model. Each of the published applications where model inversion has been applied utilizes a tailored approach to achieve the inversion, given the specific objectives and needs. These approaches range from the direct inversion of the LVRM to the formulation of an objective function that is optimized using nonlinear programming. In this paper we present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM. The different sets of equations that become relevant (either as a term within the objective function or as a constraint) are discussed, and an example of these scenarios is also provided. Additional to the formulation of the different scenarios and their objective functions, this work proposes a new metric (the P2 statistic) to cross-validate the ability of the model to reconstruct the regressor vector (analogous to the Q2 statistic aimed at the predictability of the response). This new metric comes from the need to not only predict the response from the regressor, but to also reconstruct the regressors from the scores values. In this context, a discussion is provided on the effect of uncertainty in the reconstruction of the regressor (the actual design) as these values are normally given upstream as targets to the supplier of materials, or as set points to the process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26419245

Transfer of process monitoring models between different plants using latent variable techniques / Pierantonio Facco in Industrial & engineering chemistry research, Vol. 51 N° 21 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 21 (Mai 2012) . - pp. 7327–7339 Titre : Transfer of process monitoring models between different plants using latent variable techniques Type de document : texte imprimé Auteurs : Pierantonio Facco, Auteur ; Emanuele Tomba, Auteur ; Fabrizio Bezzo, Auteur Année de publication : 2012 Article en page(s) : pp. 7327–7339 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Process  monitoring  models Résumé : This paper addresses the scenario where the manufacturing of a product with assigned quality specifications is transferred from a plant A to a plant B, which uses the same manufacturing process as plant A, but may differ for scale, configuration, actual operating conditions, measurement system arrangement, or simply location. The issue arises on whether the process data already available for plant A can be exploited to build a process monitoring model enabling to monitor the operation of plant B until enough data have been collected in this plant to design a monitoring model based entirely on the incoming data. This paper presents a general framework to tackle this problem (which we refer to as a model transfer problem), and three possible latent variable approaches within this framework are proposed and evaluated. One approach makes use of measurements coming from plant A only, whereas the other two integrate plant A data and plant B data into a single adaptive monitoring model. The proposed approaches are tested on an industrial spray-drying process, where plant A is a pilot unit and plant B is a production unit. It is shown that all proposed model transfer approaches guarantee very satisfactory monitoring performance in plant B, with quick fault detection, limited number of false alarms or undetected faults, and limited (or no) need of plant B data to accomplish the model transfer. We believe that these strategies can provide a valuable contribution to the practical implementation of quality-by-design methodologies and continuous quality assurance programs in product manufacturing. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202974u [article] Transfer of process monitoring models between different plants using latent variable techniques [texte imprimé] / Pierantonio Facco, Auteur ; Emanuele Tomba, Auteur ; Fabrizio Bezzo, Auteur . - 2012 . - pp. 7327–7339. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 21 (Mai 2012) . - pp. 7327–7339 Mots-clés : Process  monitoring  models Résumé : This paper addresses the scenario where the manufacturing of a product with assigned quality specifications is transferred from a plant A to a plant B, which uses the same manufacturing process as plant A, but may differ for scale, configuration, actual operating conditions, measurement system arrangement, or simply location. The issue arises on whether the process data already available for plant A can be exploited to build a process monitoring model enabling to monitor the operation of plant B until enough data have been collected in this plant to design a monitoring model based entirely on the incoming data. This paper presents a general framework to tackle this problem (which we refer to as a model transfer problem), and three possible latent variable approaches within this framework are proposed and evaluated. One approach makes use of measurements coming from plant A only, whereas the other two integrate plant A data and plant B data into a single adaptive monitoring model. The proposed approaches are tested on an industrial spray-drying process, where plant A is a pilot unit and plant B is a production unit. It is shown that all proposed model transfer approaches guarantee very satisfactory monitoring performance in plant B, with quick fault detection, limited number of false alarms or undetected faults, and limited (or no) need of plant B data to accomplish the model transfer. We believe that these strategies can provide a valuable contribution to the practical implementation of quality-by-design methodologies and continuous quality assurance programs in product manufacturing. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202974u