A Virtual factory data model as a support tool for the simulation of manufacturing systems

The design of a manufacturing systems is a complex and critical activity entailing decisions with an impact on a long time horizon and a major commitment of financial resources. Indeed, the modelling, simulation and evaluation of manufacturing systems are relevant activities both in the design and the operational phases of a factory. This paper grounds on the results of the Virtual Factory Framework (VFF) Project and addresses the use of an ontology based model of a production system to support the construction of a performance evaluation model

A mathematical foundation to support bidirectional mappings between digital models: an application of multi-scale modelling in manufacturing

With manufacturing going through the Industry 4.0 revolution, a vast amount of data and information exchange leads to an increase in complexity of digitized manufacturing systems. To tackle such complexity, one solution is to design and operate a digital twin model under different levels of abstraction, with different levels of detail, according to the available information and scope of the model. To support efficient, coherent and stable information flows between models with different levels of detail, a mathematical structure, called a delta lens, has been explored and developed to support rigorous bidirectional transitions between the models. To support different types of abstractions in manufacturing, a hybrid delta lens has been proposed and its formal representation is developed to support the generalization of its structure and properties. Benefits of the proposed hybrid delta lenses are demonstrated through an application to an industrial case to support the modelling of an automatic, high-throughput assembly line

A novel algorithm for optimal buffer allocation in automated asynchronous unreliable lines

The Buffer Allocation Problem is a well-known optimization problem aiming at determining the optimal buffer sizes in a manufacturing system composed by various machines decoupled by buffers. This problem still has scientific relevance because of problem complexity and trade-off between conflicting goals. Moreover, it assumes industrial relevance in reconfigurable manufacturing lines, where buffer sizes can be easily adapted to the production scenario. This work proposes a novel algorithm integrating performance evaluation and optimization by means of throughput cuts based on a linear approximation. Numerical results show the validity of the proposed approach with respect to the traditional gradient-based method. Moreover, an industrial case study integrating the proposed approach into a decision-support system for the buffer allocation and reallocation is analyzed

Different Perspectives of a Factory of the Future: An Overview

Digitalfactory,andCloudManufacturingaretwoapproaches that aim at addressing the Factory of the Future, i.e., to provide digital support to manufacturing factories. They find their roots in two different geographical areas, respectively Europe and China, and therefore presents some differences as well as the same goal of building the factory of the future. In this paper, we present both the digital factory and the cloud manufacturing approaches and discuss their differences

Robust optimization of manufacturing systems flexibility

Mass customization requires frequent product changeovers thus leading to the need of manufacturing systems endowed with flexibility and reconfiguration capabilities, in order to be robust to changes in the production scenario. Therefore, manufacturing companies face a relevant risk when taking strategic decisions about which system resources should be acquired. This risk can be mitigated by exploiting performance evaluation models, such as analytical models and Discrete Event Simulation, that are effectively adopted to estimate the performance of possible system configurations. However, current decision-support tools for optimizing system configurations can be only loosely coupled with performance evaluation models, hence undermining the actual optimization of the system itself, even more if production requirements may evolve in the future. This work presents an analytical methodology to support the optimization of manufacturing systems configuration and reconfiguration subject to evolving production requirements. The methodology integrates a stochastic analytical model for performance evaluation of manufacturing lines into a mixed integer programming problem, by means of performance linearization. The advantage of using the proposed methodology is shown on a line configuration problem, where buffer capacities and machine capabilities have to be jointly optimized, in order to minimize costs and satisfy the target performance

Formal modelling of release control policies as a plug-in for performance evaluation of manufacturing systems

Control policies significantly affect the performance of manufacturing systems, driving the need to assess their impact during both the design and operational phases. Performance evaluation tools can provide a relevant support, but their full exploitation is hindered by the difficulty of considering the huge variety of control decisions that are interwoven with manufacturing system configurations. Herein, a formal modelling approach is presented to jointly describe a manufacturing system and its release control policies, thus enabling the definition of performance evaluation models in terms of different policies. An application case is provided for the automatic generation of discrete event simulation models to assess the viability of the approach for assembly lines