Faunhofer Project Center for Production Management and Informatics PMI

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  • Publication
    Capacity management of modular assembly systems
    ( 2017)
    Gyulai, D.
    ;
    Monostori, L.
    Companies handling large product portfolio often face challenges that stem from market dynamics. Therefore, in production management, efficient planning approaches are required that are able to cope with the variability of the order stream to maintain the desired rate of production. Modular assembly systems offer a flexible approach to react to these changes, however, there is no all-encompassing methodology yet to support long and medium term capacity management of these systems. The paper introduces a novel method for the management of product variety in assembly systems, by applying a new conceptual framework that supports the periodic revision of the capacity allocation and determines the proper system configuration. The framework has a hierarchical structure to support the capacity and production planning of the modular assembly systems both on the long and medium term horizons. On the higher level, a system configuration problem is solved to assign the product families to dedicated, flexible or reconfigurable resources, considering the uncertainty of the demand volumes. The lower level in the hierarchy ensures the cost optimal production planning of the system by optimizing the lot sizes as well as the required number of resources. The efficiency of the proposed methodology is demonstrated through the results of an industrial case study from the automotive sector.
  • Publication
    Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals
    ( 2017)
    Patra, K.
    ;
    Jha, A.K.
    ;
    Szalay, T.
    ;
    Ranjan, J.
    ;
    Monostori, L.
    Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a r egression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.
  • Publication
    Design and management of reconfigurable assembly lines in the automotive industry
    ( 2016)
    Colledani, M.
    ;
    Gyulai, D.
    ;
    Monostori, L.
    ;
    Urgo, M.
    ;
    Unglert, J.
    ;
    Houten, F. van
    Automotive suppliers are facing the challenge of continuously adapting their production targets to variable demand requirements due to the frequent introduction of new model variants, materials and assembly technologies. In this context, the profitable management of the product, process and system co-evolution is of paramount importance for the company competitiveness. In this paper, a methodology for the design and reconfiguration management of modular assembly systems is proposed. It addresses the selection of the technological modules, their integration in the assembly cell, and the reconfiguration policies to handle volume and lot size variability. The results are demonstrated in a real automotive case study.
  • Publication
    Supporting multi-level and robust production planning and execution
    ( 2015)
    Stricker, N.
    ;
    Pfeiffer, A.
    ;
    Moser, E.
    ;
    Kádár, B.
    ;
    Lanza, G.
    ;
    Monostori, L.
    Operating current production systems influenced by the factors of increasing dynamics and volatility poses a need for robustness. Among different enablers for robustness the appropriate ones for specific production systems have to be identified and evaluated. In this cooperative paper multi-objective decision support models will be presented evaluating the best enablers for the levels of production network, plant and shop-floor. The suggested models for the stabilization of the production system's performance under volatile environment use analytical and simulation based approaches on the regarded levels.