A proposition of a manufactronic network approach for intelligent and flexible manufacturing systems

The XPRESS project introduces a completely new scalable concept of a manufactronic networked factory, which is composed by a co-ordinated team of specialized autonomous objects (Manufactrons), each knowing how to do a certain process optimally. This knowledge based concept integrated the complete chain: production configuration (decrease of ramp-up time of at least 50%), multi-variant production line (varying types and volumes on a single line) and 100% quality monitoring. The manufactronic networked architecture allows continuous process improvement, and will be able to anticipate and to respond to rapidly changing consumer needs, producing high-quality products in adequate quantities while reducing costs. This concept is demonstrated in the automotive, aeronautics and electrical industry but can be transferred to nearly all production processes

Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction

Remanufacturing is an activity of the circular economy model whose purpose is to keep the high value of products and materials. As opposed to the currently employed linear economic model, remanufacturing targets the extension of products and reduces the unnecessary and wasteful use of resources. Remanufacturing, along with health status monitoring, constitutes a key element for lifetime extension and reuse of large industrial equipment. The major challenge is to determine if a machine is worth remanufacturing and when is the optimal time to perform remanufacturing. The present work proposes a new predictive maintenance framework for the remanufacturing process based on a combination of remaining useful life prediction and condition monitoring methods. A hybrid-driven approach was used to combine the advantages of the knowledge model and historical data. The proposed method has been verified on the realistic run-to-failure rolling bearing degradation dataset. The experimental results combined with visualization analysis have proven the effectiveness of the proposed method

RECLAIM: Toward a New Era of Refurbishment and Remanufacturing of Industrial Equipment

Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system

Towards sustainable manufacturing by enabling optimum selection of life extension strategy for industrial equipment based on cost modelling

Sustainable manufacturing is of great importance in today’s world. In manufacturing, keep industrial equipment well-functioning is important because failure of equipment leads to significant financial and production losses. In addition, disposal of such failed equipment is both costly and environmentally unfriendly and does not recover any residual value. This raises the need to adopt methods and means that help extending the life of equipment and reduce waste of material. This paper presents a digital toolkit of cost model to estimate and understand the costs to be incurred when applying life extension strategy for industrial equipment. It is meant to be integrated with other tools and methodologies to enable end-users to perform optimal decision-making regarding which life extension strategy (e.g., remanufacturing, refurbishment, repair) to implement for large industrial equipment that is towards its end-of-life or needs maintenance, taking into account criteria such as cost, machine performance, and energy consumption. The cost model developed integrates a combination of parametric costing and activity-based costing methods to per form cost estimation. It has been implemented in an Excel-based Macro platform. A case study with application scenarios has been conducted to demonstrate the application of the cost model to optimize life extension strategies for industrial equipment. Finally, conclusions on the developed cost model have been reported

Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing

Industrial equipment/machinery is an important element of manufacturing. They are used for producing objects that people need for everyday use. Therefore, there is a challenge to adopt effective maintenance strategies to keep them well-functioning and well-maintained in production lines. This will save energy and materials and contribute genuinely to the circular economy and creating value. Remanufacturing or refurbishment is one of the strategies to extend life of such industrial equipment. The paper presents an initial framework of cost estimation model based on combination of activity-based costing (ABC) and human expertise to assist the decision-making on best life extension strategy (e.g. remanufacturing, refurbishment, repair) for industrial equipment. Firstly, ABC cost model is developed to calculate cost of life extension strategy to be used as a benchmark strategy. Next, expert opinions are employed to modify data of benchmark strategy, which is then used to estimate costs of other life extension strategies. The developed cost model has been implemented in VBA-based Excel® platform. A case study with application examples has been used to demonstrate the results of the initial cost model developed and its applicability in estimating and analysing cost of applying life extension strategy for industrial equipment. Finally, conclusions on the developed cost model have been reported

Differential binding of autoantibodies to MOG isoforms in inflammatory demyelinating diseases

Objective: To analyze serum immunoglobulin G (IgG) antibodies to major isoforms of myelin oligodendrocyte glycoprotein (MOG-alpha 1-3 and beta 1-3) in patients with inflammatory demyelinating diseases. Methods: Retrospective case-control study using 378 serum samples from patients with multiple sclerosis (MS), patients with non-MS demyelinating disease, and healthy controls with MOG alpha-1-IgG positive (n = 202) or negative serostatus (n = 176). Samples were analyzed for their reactivity to human, mouse, and rat MOG isoforms with and without mutations in the extracellular MOG Ig domain (MOG-ecIgD), soluble MOG-ecIgD, and myelin from multiple species using live cell-based, tissue immunofluorescence assays and ELISA. Results: The strongest IgG reactivities were directed against the longest MOG isoforms alpha-1 (the currently used standard test for MOG-IgG) and beta-1, whereas the other isoforms were less frequently recognized. Using principal component analysis, we identified 3 different binding patterns associated with non-MS disease: (1) isolated reactivity to MOG-alpha-1/beta-1 (n = 73), (2) binding to MOG-alpha-1/beta-1 and at least one other alpha, but no beta isoform (n = 64), and (3) reactivity to all 6 MOG isoforms (n = 65). The remaining samples were negative (n = 176) for MOG-IgG. These MOG isoform binding patterns were associated with a non-MS demyelinating disease, but there were no differences in clinical phenotypes or disease course. The 3 MOG isoform patterns had distinct immunologic characteristics such as differential binding to soluble MOG-ecIgD, sensitivity to MOG mutations, and binding to human MOG in ELISA. Conclusions: The novel finding of differential MOG isoform binding patterns could inform future studies on the refinement of MOG-IgG assays and the pathophysiologic role of MOG-IgG

An architecture for flexible manufacturing systems based on task-driven agents

Abstract During the last decades significant changes in the buying behavior of customers can be observed. While in former days price sensitivity lead to more uniformed products, in present days manifold high-quality products and customization with reasonable prices and rapid delivery are demanded. As a consequence, the industry asks for manufacturing systems which allow for fast ramp-up, multi-variant production and rapid adaptability. In this environment, several scientific approaches such as agent-based and holonic manufacturing systems have been investigated within the last years. In order to cover all aspects of the foreseen future demands, the architectures for such systems are very complex and the system’s entities are characterized by very flexible behavior. Hence, the efforts for their implementation are rather high and the systems tend to exhibit non-deterministic behavior. Furthermore, the top down approach of most systems leads to a complete re-organization of the factory management. As a consequence the acceptance for such systems in real industrial environment at present day is very limited. Therefore, the objective of this thesis is to develop an architecture for flexible manufacturing systems which allows for easy take-up in the industry. It is based on a bottom-up approach with a new kind of flexible, intelligent shop-floor components called Manufactrons. The architecture covers all layers of traditional factory organization with special emphasis on the shop floor organization. The approach and results are based on the research activities of the European Research Project XPRESS in which representatives of three major industry branches collaborated in order to find a solution for their future demands on flexible manufacturing systems. The architecture has been implemented in the context of XPRESS in aerospace, automotive and electrical industry. The tests show the feasibility of the approach. The capability for a smooth integration of the new approach into existing manufacturing environment has successfully been demonstrated.Tiivistelmä Viime vuosikymmeninä asiakkaiden ostokäyttäytyminen on muuttunut merkittävästi. Ennen asiakkaiden hintatietoisuus johti yhtenäisiin tuotteisiin, kun taas nykyään vaaditaan moninaisempia tuotteita ja muokattavuutta kohtuulliseen hintaan. Samaan aikaan odotetaan korkealaatuisia tuotteita ja nopeaa toimitusta. Nämä seikat ovat aiheuttaneet tuotantoteollisuudelle uusia haasteita. Reagoidakseen nopeasti asiakkaiden vaatimuksiin tuotannonsuunnittelussa on alettu keskittymään korkealaatuisten tuotemuunnelmien määrän kasvattamiseen. Tämän vuoksi tarvitaan tuotantojärjestelmiä, jotka mahdollistavat nopean Ramp Up -prosessin, moneenmuuntuvan tuotannon ja nopean mukautuvuuden. Tätä aihetta on viime vuosina lähestytty esimerkiksi agentteihin perustuvien ja holonisten tuotantojärjestelmien kautta. Kuitenkin näihin tulevaisuuden haasteisiin pystytään vastaamaan vain kompleksisilla arkkitehtuureilla ja järjestelmän entiteeteille ominaisia ovat hyvin mukautuvat käyttäytymismallit. Näiden toteuttamiseen tarvitaan paljon työtä ja järjestelmillä on tapana käyttäytyä epä-deterministisesti. Lisäksi ylhäältä alas lähestymistapa johtaa usein tehtaan täydelliseen uudelleenorganisointiin, minkä vuoksi lähestymistapaa ei suosita oikeissa teollisuusympäristöissä. Tämän väitöstyön tarkoituksena on kehittää mukautuville tuotantojärjestelmille arkkitehtuuri, joka mahdollistaa järjestelmien helpon käyttöönoton teollisuudessa. Arkkitehtuuri perustuu alhaalta ylös -lähestymistapaan ja sisältää uudenlaisen joustavan ja älykkään tuotantotilakomponentin, manufactronin. Arkkitehtuuri kattaa kaikki perinteisen tehdasorganisaation kerrokset keskittyen kuitenkin erityisesti tuotantotilojen organisointiin. Lähestymistapa ja tulokset perustuvat Euroopan Unionin XPRESS-tutkimusprojektiin. Projektissa tehtiin yhteistyötä kolmen suuren teollisuushaaran kanssa tarkoituksena löytää joustava tuotantojärjestelmäratkaisu tulevaisuutta varten. Arkkitehtuuria sovellettiin XPRESS-projektissa lentokone-, auto- ja elektoniikkateollisuuteen ja testit osoittivat lähestymistavan soveltuvuuden. Myös lähestymistavan sujuva integrointi olemassa oleviin teollisuusjärjestelmiin osoitettiin onnistuneesti