Ingénierie de la chaîne numérique d'industrialisation : proposition d'un modèle d'interopérabilité pour la conception-fabrication intégrées

This work focuses on the knowledge and the data management extracted from the manufacturing to ensure the interoperability in the digital chain. According to the extended enterprise and the factory of the future context, the aeronautics manufacturers tend to a design and manufacturing integrated platform in order to get a right part the first time.This work focus on manufacturing process control and capitalization of know-how from the manufacturing aiming at answering the following issue : How to enable interoperability for the digital production process in order to ensure an integrated and agile design and manufacturing ? This issue is addressed with two proposals : OntoSTEP-NC which focuses on how to model and structure the manufacturing knowledge from the CNC machine and Closed-Loop Manufacturing which focuses on how to re-use and integrate the information feedback from manufacturing to process engineering. Both combined those two proposals address the main issue of this work defining an interoperability framework for the factory of the future and address trends like the definition of guidelines for manufacturing in extended enterprise context. This work has been validated through a demonstrator and an industrial case study with various scenarios.Ce travail s'intéresse à la gestion d'information techniques et connaissances métiers issues de la production pour assurer l'interopérabilité et la continuité de la chaîne numérique. Dans un contexte d'entreprise étendue et de développement des technologies de l’information pour l'usine du futur, l'industrie aéronautique s'oriente vers une intégration flexible et agile des phases de conception et fabrication pour l'obtention de pièces bonnes du premier coup. C'est pour assurer la maîtrise des processus et la capitalisation des savoir-faire métier issus de la fabrication que ces travaux adressent la problématique suivante : comment élaborer un modèle d'interopérabilité de la chaîne numérique d'industrialisation, pour assurer une intégration agile de la conception et de la fabrication ? Pour ce faire, nous définissons deux propositions : OntoSTEP-NC pour permettre l'extraction et la structuration des données issues de la fabrication et Closed-Loop Manufacturing pour permettre l'intégration et la réutilisation des connaissances métiers capitalisées au niveau de l'industrialisation. Les apports de ces propositions se retrouvent dans la définition d'un cadre d'interopérabilité pour l'usine du futur mais également dans les enjeux tels que la définition de bonnes pratiques pour l'entreprise étendue en vue d'une harmonisation des processus de fabrication. Ces travaux ont été validés au travers d'un démonstrateur sur un cas d'étude industriel comportant plusieurs scénarii

NORECOIN method in social collective restauration. A new study during three years

Après une première étude (1985 - 1986 - 1987), la méthode d’analyse NORECOIN (normalisée, répétitive, codée et intégrée) a été appliquée à 96 restaurants de restauration collective sociale (restaurants scolaires) au cours d’une nouvelle période : 1988-1990. Ont été à nouveau obtenus < 5 % de plats cuisinés non conformes et < 10 % de plats autres (entrées ou desserts) non conformes ou non satisfaisants. La note NORECOIN est, dans ce cas, comprise entre 2,5 et 3.After a first study (1985 - 1986 - 1987) the analysis method NORECOIN (normalized, repeatitive, coded and integrated) was applied for 96 restaurants of social collective restauration (school restaurants) during a new period : 1988-1990. Were yet obtained : < 5 % of not conform cooked dishes and <10% of not conform or not satisfactory other dishes (entrées or desserts). In this case, the NORECOIN note ranged 2.5 to 3

Future trends in organic flour milling: the role of AI

The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance

Systematic comparison of digital maturity assessment models

ABSTRACT: Assessing the digital maturity of companies is essential to prepare for digital transformation in the context of Industry 4.0. Several digital maturity assessment models have emerged in the past few years to support this evaluation. One obstacle for companies is the impossibility of easily comparing themselves to one another quantitatively or qualitatively. This paper introduces a new way to compare digital maturity models through a quantitative framework that is compatible with a wide variety of models. Comparisons are performed in the space of the keywords used to characterize key performance indicators (KPIs) that are reverse engineered from the models. The matches are encoded in a keyword matrix that is used to automatically compute the match level of KPI pairs. The framework has been validated on 13 state-of-the-art maturity models whose analysis resulted in the identification of 451 KPIs characterized using 263 keywords structured according to 12 dimensions and 58 subdimensions

STEP-NC based optimization and smart industrialization of NC machining in the context of the factory of the future

L'article propose un nouveau concept pour assurer le retour d'information depuis les Machines-Outils à Commandes Numériques vers les systèmes FAO et la simulation d'usinage. Le but principal de la proposition est de gérer les connaissances d'usinage extraites de la machine pour les réutiliser au niveau de la simulation d'usinage et ainsi fournir une aide à la décision pour la réalisation de futur programme d'usinage. Pour assurer la capitalisation des connaissances d'usinage, la proposition s'appuie sur une structure basée sur les entités d'usinage. Bien que la structure du standard STEP-NC permette l'exploitation d'une telle structure, son utilisation est impossible du fait de sa faible implémentation. C'est pourquoi la proposition se base sur la chaîne numérique actuelle et propose une reconnaissance des entités d'usinage directement depuis les fichiers STL extrait de la simulation d'usinage et propose une conversion des programmes de Code-G en STEP-NC. Cela permet alors d'interagir avec l'implémentation d'OntoSTEP-NC, une ontologie basée sur STEP-NC, comme base de connaissance

Digital maturity models: comparing manual and semi-automatic similarity assessment frameworks

The fourth industrial revolution is forcing companies to define their digital strategy, making it imperative that they assess their digital maturity as a basis for improvements. As a result, a variety of maturity models have emerged. However, it can be difficult to identify which one is most appropriate. This paper introduces a new methodology to compare a manual and a semi automatic framework for assessing the similarity of digital maturity models. It allows identifying the most adequate framework for comparing maturity models. Both frameworks have been designed to identify correspondences between KPIs. The analysis of the matches and the obtained results are then used to tune the semi-automatic framework. The proposed comparison methodology has been validated using two digital maturity models and shows that the semi-automatic framework provides good results in a very efficient manner. Several insights have been derived and will help to develop a new maturity model

Identifying PLM themes and clusters from a decade of research literature

Product lifecycle management (PLM) encompasses a wide array of expertise, from designing green products to knowledge representation techniques. This paper characterises PLM as a research domain through the themes and clusters of a decade of scientific literature. Authors’ keywords from 1,390 research papers published from 2005 to 2015 are analysed. The co-occurrence of these 2,947 normalised authors’ keywords, connected in pairs via 11,289 edges, indicates how PLM research themes relate to each other to form communities – or clusters. These communities are revealed by filtering the network according to the weights of the network’s edges. The PLM core cluster, the PLM global cluster and the PLM overall cluster are distinguished based on the level of filtering, thus unveiling increasing levels of detail. The four major communities composing the PLM global cluster are ‘interoperability’, ‘ontology’, ‘product data management’ and ‘lifecycle assessment’. The PLM overall cluster also reveals the ‘intelligent product’ community, which relates to the Industry 4.0 phenomenon. The BIM community is revealed as well, but remains isolated from the PLM overall cluster

Construction 4.0: a survey of research trends

ABSTRACT: The fourth industrial revolution, called Industry 4.0, is transforming decision-making through the increasing use of information and digitization technologies. While Industry 4.0 is expanding rapidly in manufacturing industries, its induced transformations are gradually affecting other sectors, including the construction industry. In recent years, the use of 4.0 technologies in the construction industry, termed as ‘Construction 4.0’, has increased, mostly due to the immense potential of Industry 4.0 for improving the performance of construction projects and structuring their underlying management processes. This paper proposes a classification of existing literature on applications of Construction 4.0 technologies to allow for a better analysis of trends and gaps in the research. A total of nearly 200 research papers between 2009 and 2020 were reviewed and analyzed. Overall, the analysis shows that research on Construction 4.0 is closely aligned with the construction phase. Also, the most researched topics seem to be related to the management processes of quality, risk, and health and safety

Transformer le secteur de la construction par le numérique : un chantier ambitieux et nécessaire

L’industrie de la construction joue un rôle prépondérant dans l’économie. Malgré son importance, elle fut longtemps décrite comme moins productive et innovante que d’autres secteurs. Depuis quelques années, cette situation semble changer et plusieurs acteurs reconnaissent la nécessité d’innover pour assurer la compétitivité des entreprises. De nouvelles formes de travail émergent grâce à l’introduction de technologies telles que la modélisation des données du bâtiment (BIM), la réalité virtuelle et augmentée, la préfabrication, les objets intelligents, la fabrication additive, les technologies portables, l’automatisation, la robotisation, etc. Au Québec, plusieurs grandes entreprises ont déjà adopté certaines de ces technologies, mais leur mise en œuvre reste encore embryonnaire, en particulier chez les petites entreprises. Selon nombre d’experts, l’utilisation des technologies doit être encouragée afin de créer une véritable transformation numérique de la construction au Québec. Ce chapitre propose une courte analyse de cette transformation et des pistes d’intervention pour le contexte québécois, à partir des tendances observées au niveau international

Genetic risk of Parkinson disease and progression:: An analysis of 13 longitudinal cohorts.

OBJECTIVE: To determine if any association between previously identified alleles that confer risk for Parkinson disease and variables measuring disease progression. METHODS: We evaluated the association between 31 risk variants and variables measuring disease progression. A total of 23,423 visits by 4,307 patients of European ancestry from 13 longitudinal cohorts in Europe, North America, and Australia were analyzed. RESULTS: We confirmed the importance of GBA on phenotypes. GBA variants were associated with the development of daytime sleepiness (p.N370S: hazard ratio [HR] 3.28 [1.69-6.34]) and possible REM sleep behavior (p.T408M: odds ratio 6.48 [2.04-20.60]). We also replicated previously reported associations of GBA variants with motor/cognitive declines. The other genotype-phenotype associations include an intergenic variant near LRRK2 and the faster development of motor symptom (Hoehn and Yahr scale 3.0 HR 1.33 [1.16-1.52] for the C allele of rs76904798) and an intronic variant in PMVK and the development of wearing-off effects (HR 1.66 [1.19-2.31] for the C allele of rs114138760). Age at onset was associated with TMEM175 variant p.M393T (-0.72 [-1.21 to -0.23] in years), the C allele of rs199347 (intronic region of GPNMB, 0.70 [0.27-1.14]), and G allele of rs1106180 (intronic region of CCDC62, 0.62 [0.21-1.03]). CONCLUSIONS: This study provides evidence that alleles associated with Parkinson disease risk, in particular GBA variants, also contribute to the heterogeneity of multiple motor and nonmotor aspects. Accounting for genetic variability will be a useful factor in understanding disease course and in minimizing heterogeneity in clinical trials.The Intramural Research Program the National Institute on Aging (NIA, Z01-AG000949-02), Biogen Idec, and the Michael J Fox Foundation for Parkinson’s Researc