Symbolic recurrence plots: A new quantitative framework for performance analysis of manufacturing networks

During the last years, the concept of recurrence plots has received considerable interest as a tool for analysing nonlinear and non-stationary time series. However, in the case of discrete-valued observables or variations on very different time scales, problems may occur in direct interpretations of the results of recurrence quantification analysis (RQA). As a potential solution, we suggest combining this approach with ideas from symbolic time series analysis, which allows an arbitrary static or dynamic coarse-graining of the dynamics that goes beyond recent recurrence plot based methods. As an illustrative application, we discuss how the resulting symbolic recurrence plots may be used for a quantitative investigation of the dynamics of discrete-valued inventory levels of cooperating firms in a manufacturing network. Based on discrete-event simulations, measures from traditional RQA are used to evaluate the performance of the individual firms under different production strategies as well as order policies. The results of our investigations are an important step towards an anticipative knowledge about the performance of manufacturing systems under different conditions, which is of major importance for the planning and control of both production and logistics.     

An anisotropic large strain plasticity model for multifilament yarns

Important industrial rubber components like tyres, driving belts and air springs are usually reinforced by twisted polymer cords. A cord itself is a twisted structure of several multifilament yarns. Each yarn is a twisted strand consisting of a large number of polymer filaments. In this work, an anisotropic large strain plasticity model is presented which captures the yarn's filament structure and frictional behaviour. The material model is implemented in Abaqus adopting its user subroutine UMAT and validated by a simulation of a cord's twisting process and the comparison with microscope images. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)