Faunhofer Project Center for Production Management and Informatics PMI
Now showing 1 - 10 of 21
PublicationRobotisiertes Laser-Fernschweißen( 2016)Monostori, LászlóDas Ziel des Projekts ist die Verbesserung der Bearbeitungsgeschwindigkeit, die Erhöhung der Prozessflexibilität und der Energieeffizienz. Zudem sollen die realen und virtuellen Prozesse verknüpft werden, um eine Remote-Offline-Programmierung zu ermöglichen.
PublicationProduction trend identification and forecast for shop-floor business intelligence( 2016)
;Viharos, Z.J. ;Csanaki, J. ;Nacsa, J. ;Edelenyi, M. ;Pentek, C. ;Kis, K.B. ;Fodor, A.Csempesz, J.The paper introduces a methodology to define production trend classes and also the results to serve with trend prognosis in a given manufacturing situation. The prognosis is valid for one, selected production measure (e.g. a quality dimension of one product, like diameters, angles, surface roughness, pressure, basis position, etc.) but the applied model takes into account the past values of many other, related production data collected typically on the shop-floor, too. Consequently, it is useful in batch or (customized) mass production environments. The proposed solution is applicable to realize production control inside the tolerance limits to proactively avoid the production process going outside from the given upper and lower tolerance limits. The solution was developed and validated on real data collected on the shop-floor; the paper also summarizes the validated application results of the proposed methodology.
PublicationBilevel programming approach to optimizing a time-variant electricity tariff for demand response( 2016)Kovacs, A.This paper proposes a bilevel programming model to day-ahead electricity tariff optimization in smart grids to balance grid-level demand and supply at all times. In this Stackelberg game approach, the leader is the grid operator, who aims to set the tariff to ensure the balance of supply and demand. The followers are groups of consumers, who, in response to the observed tariff, schedule their controllable consumption and determine the charging/discharging policy of their batteries to minimize their cost. The bilevel optimization problem is reformulated into a single-level quadratically constrained quadratic program (QCQP), which is then solved by a successive linear programming (SLP) algorithm. The approach is illustrated on an example with three different consumer groups.
PublicationClosed-loop applicability of the Sign-Perturbed Sums method( 2015)
;Csaji, B.C.Weyer, E.Sign-Perturbed Sums (SPS) is a non-asymptotic system identification method that can construct confidence regions for general linear systems. It works under mild statistical assumptions, such as symmetric and independent noise terms. The SPS confidence region includes the prediction error estimate (PEM) and, for any finite sample and user-chosen confidence probability, it contains the true system parameter with exactly the given probability. Originally, SPS was introduced for open-loop systems, this paper overviews its applicability in closed-loop setups. The three main PEM approaches of closed-loop identification are addressed: direct, indirect and joint input-output, and it is discussed whether SPS can be applied to construct guaranteed finite sample confidence regions around these PEM estimates. Some parametrization issues are also highlighted and, finally, two numerical experiments are presented demonstrating SPS for closed-loop systems.
PublicationSign-Perturbed Sums (SPS) with Instrumental Variables for the Identification of ARX Systems( 2015)
;Volpe, V. ;Csaji, B.C. ;Care, A. ;Weyer, E.Campi, M.C.We propose a generalization of the recently developed system identification method called Sign-Perturbed Sums (SPS). The proposed construction is based on the instrumental variables estimate and, unlike the original SPS, it can construct non-asymptotic confidence regions for linear regression models where the regressors contain past values of the output. Hence, it is applicable to ARX systems, as well as systems with feedback. We show that this approach provides regions with exact confidence under weak assumptions, i.e., the true parameter is included in the regions with a (user-chosen) exact probability for any finite sample. The paper also proves the strong consistency of the method and proposes a computationally efficient generalization of the previously proposed ellipsoidal outer-approximation. Finally, the new method is demonstrated through numerical experiments, using both real-world and simulated data.
PublicationAdaptive aggregated predictions for renewable energy systems( 2014)
;Csáji, B.C. ;Kovács, A.Váncza, J.The paper addresses the problem of generating forecasts for energy production and consumption processes in a renewable energy system. The forecasts are made for a prototype public lighting microgrid, which includes photovoltaic panels and LED luminaries that regulate their lighting levels, as inputs for a receding horizon controller. Several stochastic models are fitted to historical times-series data and it is argued that side information, such as clear-sky predictions or the typical system behavior, can be used as exogenous inputs to increase their performance. The predictions can be further improved by combining the forecasts of several models using online learning, the framework of prediction with expert advice. The paper suggests an adaptive aggregation method which also takes side information into account, and makes a state-dependent aggregation. Numerical experiments are presented, as well, showing the efficiency of the estimated time-series models and the propos ed aggregation approach.
PublicationPrediction and robust control of energy flow in renewable energy systems( 2014)
;Csáji, B.C. ;Kovács, A.Váncza, J.The paper is motivated by making use of solar energy in public lighting services via an intermediate battery storage. The aim is to develop algorithms for controlling the energy flow in the system, in such a way that robustness against power outages is guaranteed and the total energy cost is minimized. A novel approach is proposed which predicts energy production and consumption by fitting stochastic models to historic data, and solves the resulting optimization problem on a rolling horizon. Experimental results are also presented, illustrating the behavior of the controlled energy system in typical winter and summer days.
PublicationCapacity planning and resource allocation in assembly systems consisting of dedicated and reconfigurable lines( 2014)
;Gyulai, D. ;Kádár, B.Monostori, L.Companies with diverse product portfolio often face capacity planning problems due to the diversity of the products and the fluctuation of the order stream. High volume products can be produced cost-efficiently in dedicated assembly lines, but the assembly of low-volume products in such lines involves high idle times and operation costs. Reconfigurable assembly lines offer reasonable solution for the problem; however, it is still complicated to identify the set of products which are worth to assemble in such a line instead of dedicated ones. In the paper a novel method is introduced that supports the long-term decision to relocate the assembly of a product with decreasing demand from a dedicated to a reconfigurable line, based on the calculated investment and operational costs. In order to handle the complex aspects of the planning problem a new approach is proposed that combines discrete-event simulation and machine learning techniques. The feasibility of the approach is demonstrated through the results of an industrial case study.
PublicationMethodology and data-structure for a uniform system's specification in simulation projects( 2013)
;Kardos, Csaba ;Popovics, Gergely ;Kádár, BotondMonostori, LászlóIn the last few decades the evaluation and analysis of manufacturing systems' behavior and their performance became very important. Digital enterprise technologies, as for example discrete-event simulation (DES), are effective tools both in production related decision making processes and in structure and performance analysis of manufacturing systems. However, building a discrete-event based simulation model of a manufacturing system is a difficult task and requires special competence. The majority of simulation studies are aimed at analyzing a certain problem by a specific simulation model created by experts with a relatively high financial expenditure. The paper introduces an ongoing research aimed at developing a framework to reduce the efforts spent on draft simulation studies by simplifying and accelerating the process of model building. The proposed modeling methodology uses a uniform data structure which is a production oriented implementation of the ANSI/ISA-95 standard and supports the creation of models without simulation software specific knowledge. The supporting data structure enables the development and application of proprietary simulation engines tailored for specific problems. The paper compares the traditional and the proposed methodologies and also introduces the first experiments gained on specific test -cases. In our approach the simulation models are created automatically and independently from simulation tools which will be presented through the examples of both commercial and self-developed applications.
PublicationTask sequencing for remote laser welding in the automotive industry( 2013)Kovács, A.This paper proposes a new model and algorithm for task sequencing in remote laser welding in the automotive industry. It is shown that task sequencing (in which order to weld the seams) is strongly related to path planning (how the welding robot should move), therefore the two problems must be solved together, in an integrated way. The problem is modeled as a direct product of a traveling salesman and a path planning problem, and a tabu search algorithm is proposed for solving it. Computational experiments show that the proposed method leads to a substantial reduction in the cycle time of the welding operation compared to an earlier approach.