Accueil
Résultat de la recherche
4 résultat(s) recherche sur le tag ''Défauts'
Ajouter le résultat dans votre panier Affiner la recherche Générer le flux rss de la recherche
Partager le résultat de cette recherche
Application of the hypothesis analysis method using Cohen's Kappa index to measure the agreement between leather sorters / Patricia Casey in JOURNAL OF THE SOCIETY OF LEATHER TECHNOLOGISTS & CHEMISTS (JSLTC), Vol. 94, N° 4 (07-08/2010)
[article]
Titre : Application of the hypothesis analysis method using Cohen's Kappa index to measure the agreement between leather sorters Type de document : texte imprimé Auteurs : Patricia Casey, Auteur ; Gustavo Altobelli, Auteur ; Pablo Pignatelli, Auteur Année de publication : 2010 Article en page(s) : p. 144-148 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Automobiles -- Matériaux
Cuirs et peaux -- DéfautsTags : 'Cuir automobile' 'Défauts des surfaces' Evaluation 'Critères de tri' 'Index Kappa Cohen' Index. décimale : 675 Technologie du cuir et de la fourrure Résumé : One of the more sensitive activities carried out in a tannery, because of the impact the results could cause on the customer and on the tannery's business is, certainly, the sorting or grading of the hides in the different stages (wet blue or wet white, crust and finished). The surface defects affect the aesthetics appearance of leather and leather goods as well as the usable area. If the grade assigned to a hide by a sorter is lower than the one it really is, according to the agreement with the customer it will cause economical damage to the tannery. On the other hand, if the grade assigned is higher than the one it really is, the customer will be disappointed and claim. The detection of defects on the surface of natural materials such as leather is a difficult task because of the variety of shapes and textures, as well as in the quality and quantity of defective areas. The most common method applied in the leather industry to sort or grade hides is visual inspection. Notwithstanding the sorting or grading criteria could be well defined and agreed between the tannery and the customer, we can always wonder: if we change from one sorter to another, which will be the concordance level existing between them? In other words, how far the sorters will coincide on their evaluation? Will the result be a real or random one? Due to the variability between two or more sorters which is traditionally recognized as an important source of mistakes, from our point of view, it would be very useful to transform an attribute analysis into a variable one. The tanneries that supply leather to the automotive companies have to certify their Quality System according to QS 9000 or ISO/TS 16949. One of the requirements in that norm and technical specification is to apply the Automotive Industry Action Group (AIAG) reference Manual "Measurement Systems Analysis" which describes the study of the measurement systems by attributes. In this paper, we describe the practical way on which the agreement between sorters is measured and the application of the hypothesis analysis method, cross tabulation, calculating Kappa coefficient initially proposed by Cohen, which is a statistical concordance rating measure for qualitative (categorical items) between two sorters. En ligne : https://drive.google.com/file/d/1qXL_GNqU5y1ahjug61SvgZKc9UK5pxYc/view?usp=drive [...] Format de la ressource électronique : Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=9856
in JOURNAL OF THE SOCIETY OF LEATHER TECHNOLOGISTS & CHEMISTS (JSLTC) > Vol. 94, N° 4 (07-08/2010) . - p. 144-148[article]Réservation
Réserver ce document
Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 012384 - Périodique Bibliothèque principale Documentaires Disponible Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation / Praveen Kumar Moganam in JOURNAL OF LEATHER SCIENCE AND ENGINEERING, Vol. 4 (Année 2022)
[article]
Titre : Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation Type de document : texte imprimé Auteurs : Praveen Kumar Moganam, Auteur ; Denis Ashok Sathia Seelan, Auteur Année de publication : 2022 Article en page(s) : 21 p. Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Apprentissage automatique L'apprentissage automatique (en anglais : machine learning, litt. "apprentissage machine"), apprentissage artificiel ou apprentissage statistique est un champ d'étude de l'intelligence artificielle qui se fonde sur des approches mathématiques et statistiques pour donner aux ordinateurs la capacité d'"apprendre" à partir de données, c'est-à-dire d'améliorer leurs performances à résoudre des tâches sans être explicitement programmés pour chacune. Plus largement, il concerne la conception, l'analyse, l'optimisation, le développement et l'implémentation de telles méthodes. (Wikipedia)
Cuirs et peaux -- Défauts
Détection de défauts (Ingénierie)
Réseaux neuronaux (informatique)Tags : 'Réseaux de neurones à convolution' 'Classificateur d'apprentissage automatique' 'Défauts du cuir' 'Classement multi-classes' 'Carte d'activation classe' Segmentation Index. décimale : 675 Technologie du cuir et de la fourrure Résumé : Modern leather industries are focused on producing high quality leather products for sustaining the market competitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature; hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is necessary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classification of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented. Note de contenu : - MACHINE VISION-BASED LEATHER INSPECTION SYSTEM : Leather Image acquisition - Leather texture defects
- DEEP LEARNING NEURAL NETWORK APPROACH FOR CLASSIFICATION AND LOCALIZATION OF LEATHER DEFECTS : Leather image Data Set preparation and preprocessing - Deep learning convolutional neural network architectures - Visualization of region of interest for defect localization
- MACHINE LEARNING BASED APPROACHES FOR MULTI CLASS DEFECT CLASSIFICATION OF LEATHER DEFECTS : Hand crafted Feature extraction from leather images - Shallow feed-forward neural network-based machine learning classifier
- PERFORMANCE METRICS OF DEEP LEARNING AND MACHINE LEARNING CLASSIFIERS
- RESULTS AND DISCUSSION : Feature maps of convolution neural networks - Feature extraction using GLCM, autocorrelation - Training and testing performance of deep learning neural networks
Training performance of shallow feed forward neural network classifier - Classification performance of deep learning neural networks - Classification performance of machine learning approaches - Class activation maps for selection of region of interest in leather imagesDOI : https://doi.org/10.1186/s42825-022-00080-9 En ligne : https://link.springer.com/content/pdf/10.1186/s42825-022-00080-9.pdf Format de la ressource électronique : Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=37575
in JOURNAL OF LEATHER SCIENCE AND ENGINEERING > Vol. 4 (Année 2022) . - 21 p.[article]Effects of injection molding holding pressure on the replication of surface microfeatures / T. R. Tofteberg in INTERNATIONAL POLYMER PROCESSING, Vol. XXV, N° 3 (07/2010)
[article]
Titre : Effects of injection molding holding pressure on the replication of surface microfeatures Type de document : texte imprimé Auteurs : T. R. Tofteberg, Auteur ; H. Amédro, Auteur ; F. Grytten, Auteur ; E. Andreassen, Auteur Année de publication : 2010 Article en page(s) : p. 236-241 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Matières plastiques -- Moulage par injection
Moulage par injection -- Défauts
PolycarbonatesTags : Polycarbonate 'Moulage par injection' 'Interférométrie de lumière blanche' (WLI) à haute résolution verticale' (HDVSI) Recuisson 'Pression possession' 'Lignes flux' 'Défauts d'injection' Index. décimale : 668.9 Polymères Résumé : The injection molding of an optical grating was studied using two different polycarbonates. The grating had period 10 μm and peak-to-valley distance ∼1 μm. Parts were molded using different holding pressures and mold temperatures. After production, the parts were annealed at 100°C. The replication was investigated using white light interferometry (WLI) before and after annealing. WLI was performed using high definition vertical-scanning interferometry (HDVSI) to resolve the details of the molded gratings with a noise level below 2 nm. It was observed that increasing the holding pressure could have either a positive or a negative effect on the replication. When the microfeatures were not fully filled, an increased holding pressure improved the definition of the features. However, for both polymers, it was observed that the replication as a function of holding pressure started to drop when the holding pressure was increased above an optimal value. This could be due to an elastic recoil occurring after releasing the holding pressure. The peak-to-valley distance of the grating was reduced after annealing. This effect was larger for parts molded using a low mold temperature. This is probably due to a higher cooling rate giving higher internal stresses, which will relax during annealing. DOI : 10.3139/217.2340 En ligne : https://drive.google.com/file/d/1Jr8pmzkZ8ZrKvyMacqDsvjly-jPSoUt-/view?usp=drive [...] Format de la ressource électronique : Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=9739
in INTERNATIONAL POLYMER PROCESSING > Vol. XXV, N° 3 (07/2010) . - p. 236-241[article]Réservation
Réserver ce document
Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 012302 - Périodique Bibliothèque principale Documentaires Disponible Technologie graphique. Vocabulaire des incidents d'impression - Norme NF Q 60-006 / Association Française de Normalisation (Paris) / Saint-Denis La Plaine : Association Française de Normalisation (AFNOR) (1977)
Titre de série : Technologie graphique Titre : Vocabulaire des incidents d'impression - Norme NF Q 60-006 Type de document : texte imprimé Auteurs : Association Française de Normalisation (Paris) , Auteur Editeur : Saint-Denis La Plaine : Association Française de Normalisation (AFNOR) Année de publication : 1977 Importance : 3 p. Format : 30 cm Langues : Français (fre) Tags : 'Incidents impression' 'Défauts Vocabulaire Normes Index. décimale : 667.9 Revêtements et enduits Résumé : La présente norme donne un vocabulaire des principaux incidents susceptibles d'apparaître au cours de l'impression des documents. Les termes et expressions qui y figurent sont définis conformément à leur emploi dans le domaine de la technologie graphique, indépendamment du fait que certains d'entre eux peuvent être utilisés dans d'autres domaines avec des acceptions parfois différentes. Note de contenu : LISTE DES INCIDENTS D'IMPRESSION : Aimantation - Arrachage - Coloration de la forme d'impression (en offset) - Défaut d'encrage - Bouchage (en typographie) - Doublage - Ecrasement (en typographie) - Encre dormante - Image dormante - Image fantôme - Filage (d'une forme d'impression offset) - Foulage (en typographie) - Glaçage (d'un blanchet d'offset ou des rouleaux d'encrage) - Gondolage (du papier) - Graissage (en offset) - Lavage de l'encre (en offset) - Levage (en typographie) - Maculage - Moutonnage - Oxydation de la forme d'impression (en offset) - Peluchage (en offset) - Petouilles - Placage de l'encre - Plissage - Poudrage du papier (en offset) - Poudrage d'une impression - Poussiérage - Refus - Repérage défectueux - Salissures - Séchage défectueux d'une impression - Séchage de l'encre sur machine - Tombé (en typographie) - Transpercement - Tuilage (du papier) - Voilage (en offset - Voltige (de l'encre) Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=16990 Réservation
Réserver ce document
Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 14479 Q 60-006 Norme Bibliothèque principale Documentaires Disponible