Automatic Generation Of Simulation Models Research Articles

Synchronous integration method of mechatronic system design, geometric design, and simulation based on SysML

The benefits of integrated design using the Model Based System Engineering (MBSE) approach in the design process of mechatronic systems have gradually become apparent. The automatic generation of simulation models and geometric models based on System Modeling Languages (SysML) models enables system engineers to swiftly analyze and simulate system performance, visually depict design outcomes, and expedite the product development process. Due to the current system modeling's lack of model integration and geometric design functions, this paper proposes an integrated design and simulation method for mechatronic systems that can carry out complete model synchronization and verification, rapid geometric solution generation, and visual representation. Furthermore, a corresponding model synchronization integration framework is established. This framework primarily encompasses system de-sign, system simulation, and geometric design, with its model integration method being model transformation and model synchronization. The paper concludes with an example of the design process of a quadruped robot to validate the framework and its supported methods, providing a reference for other system design and integration endeavors.

Automatic model generation for material flow simulations of Third-Party Logistics

AbstractThe use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system’s functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems.

Support the Decision Processes in Management Through the Automatic Generation of Simulation Models of Warehouses

Support the Decision Processes in Management Through the Automatic Generation of Simulation Models of Warehouses

EIPPM—The Executable Integrative Product-Production Model

In this paper, a combination of graph-based design and simulation-based engineering (SBE) into a new concept called Executable Integrative Product-Production Model (EIPPM) is elaborated. Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of SBE for the development of production systems (PS). Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain “PSs” together with the knowledge of the corresponding “product” using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle of product and PS, results of an automatic translation step in a compiler. Since the execution of the GBDL can be repeated as often as desired with modified boundary conditions (e.g., through feedback), a design of experiment is made possible, whereby unconventional solutions are also considered. The novel concept aims at the following advantages: Consistent linking of all mechatronic disciplines through a data model (graph) from the project start, automatic design cycles exploring multiple variants for optimized product-PS combinations, automatic generation of simulation models starting with the planning phase and feedback from simulation-based optimization back into the data model.

Towards an Automated Product-Production System Design - Combining Simulation-based Engineering and Graph-based Design Languages

In this paper, the authors elaborate a combination of graph-based design and simulation-based engineering into a new concept called Executable Integrative Product-Production Model (EIPPM). Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. Therefore, Digital Twins (DT) are modeled and run in a simulation. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of DTs in a simulation-based engineering for the development of production systems. Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain "production systems technology" together with the knowledge of the corresponding "product" using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle, results of an automatic translation step in a compiler. Since the execution of the GBDL can be repeated as often as desired with modified boundary conditions (e.g. through feedback), a design of experiment is made possible, whereby also unconventional solutions are considered. The novel concept aims at the following advantages: Consistent linking of all mechatronic domains through a data model (graph) from the project start, automatic design cycles exploring multiple variants for optimized product-production system combinations, automatic generation of simulation models starting with the planning phase, feedback from simulation-based optimization back into the data model.

A Survey on Automatic Model Generation for Material Flow Simulation in Discrete Manufacturing

Adaptivity of production systems is acknowledged as a key competition factor in the manufacturing industry. In order to decrease reaction time and increase cost efficiency, changes to a production system are often planned and validated using material flow simulation. However, repeated manual synchronization of the simulation model with the real manufacturing environment is time-consuming and error-prone. Automatic simulation model generation (ASMG) could overcome these issues. In contrast to recent surveys on ASMG, we focus on information retrieval. Accordingly, we constitute the state of the art of this problem domain and outline future research directions.

Emulation of control strategies through machine learning in manufacturing simulations

Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics. To reduce time and effort spent on creating simulation models, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction of dynamic behavior of the modeled system is a common challenge. One example is the representation of dispatching and scheduling strategies of production jobs. When generating a model automatically, the underlying rules for these strategies are typically unknown but yet have to be adequately emulated. In this paper, we summarize our work investigating the suitability of various data mining and supervised machine learning methods for emulating job scheduling decisions based on data obtained from production data acquisition. We report on the performance of the algorithms and give recommendations for their application, including suggestions for their integration in simulation systems.

Automatic Generation of Pipelines Into a 3D Industrial Process Model

Simulation has become an established technique to support the design of complex, mechatronic or cyber-physical systems. Ideally, simulations should already be performed at an early design phase before high-cost design commitments are made, and the recent advances in the digitalization of design information open possibilities for automatic generation of simulation models. However, high-fidelity simulation model building depends on accurate data. In particular, first-principles models are desirable source information for simulations, but such models generally are not available at an early design stage. This paper investigates the automatic generation of first-principles 3-D models for piping intensive systems based on design information that is available at an early design stage, namely piping & instrumentation diagrams (P&ID). An algorithm is presented for the generation of such 3-D models based on machine-readable P&ID information. The main focus of the algorithm is the automatic generation of feasible pipelines into the 3-D models, so that the model has sufficient information, which can be exploited in further work to automatically generate high fidelity first-principles thermo-hydraulic simulations.

Generic data structure and validation methodology for simulation of manufacturing systems

In recent years, the analysis and evaluation of manufacturing systems’ behaviour and their performance became essential. Discrete event simulation (DES) as a digital enterprise technology is an effective tool both in production related decision support processes and in structure or performance analysis of manufacturing systems. Building a DES model of a manufacturing system is a difficult task and requires special competence. Reducing the efforts spent on draft simulation is the aim of an ongoing research which is presented in the paper. The developed framework simplifies and accelerates the process of model building. A production oriented implementation of the ANSI/ISA-95 standard was used by the proposed modelling methodology to define a generic data structure supporting the creation of models without specific knowledge related to the simulation software applied. The data structure enables the development and application of proprietary simulation engines tailored for specific problems. On the other hand, numerous objective tests containing simulation model validation methods are also announced through a well-described example problem. By this method, several validating key performance indicators (KPIs) were tested, and it was indirectly determined whether the simulation model adequately represents the behaviour of the real system. The automatic simulation model generation in our approach enables using different simulation tools and presented through the examples of both commercial and self-developed software.

On the use of artificial neural networks in simulation-based manufacturing control

The automatic generation of simulation models has been a recurring research topic for several years. In manufacturing industries, it is currently also becoming a topic of high practical relevance. A well-known challenge in most model generation approaches is the correct reproduction of the dynamic behaviour of model elements, for example, buffering or control strategies. This problem is especially relevant in simulation-based manufacturing control. In such scenarios, simulation models need to reflect the current state and behaviour of the real system in a highly accurate way, otherwise its suggested control decisions may be inaccurate or even dangerous towards production goals. This paper introduces a novel methodology for approximating dynamic behaviour using artificial neural networks, rather than trying to determine exact representations. We suggest using neural networks in conjunction with traditional material flow simulation systems whenever a certain decision cannot be made ex ante in the model generation process due to insufficient knowledge about the behaviour of the real system. In such cases the decision is delegated to the neural network, which is connected to the simulation system at runtime. Training of the neural network is performed by observation of the real systems decision and based on the evaluation of data that can be gained through production data acquisition. Our approach has certain advantages compared to other approaches and is especially well suited in the context of on-line simulation and simulation-based operational decision support. We demonstrate the applicability of our methodology using a case study and report on performance results.

Layout and process optimisation: using computer-aided design (CAD) and simulation through an integrated systems design tool

The design of production and logistic systems is a process of managing both technical and organisational variants in order to identify the best solution for a given system. This paper discusses design issues of production systems that are applied to an internal logistic system in the automotive industry. As far as the production systems design (PSD) is concerned, three basic classes of software tools usually pertain: computer-aided design (CAD), process simulation, and information systems. However, these software tools have been used with low levels of integration. Vik et al. (2010b, 2010c) proposed integrating these software resources in production systems and developed an advanced tool called integrated design of systems (IDS). The proposed IDS tool involves a wide set of functions for the most common tasks of PSD, from conceptualisation to implementation, including systems analysis (P-Q, cluster, and material flow analysis), automatic generation of simulation models, generation of alternatives for the layout of facilities and factories, material flows display, transportation system design, and iterative buffer size specification. The IDS approach takes advantage of simulation, CAD systems, and their integration. This paper will demonstrate the concept and functionalities of the proposed tool in a real industrial case study.

A preliminary study of thermal hydraulic models for virtual hazard and operability analysis and model-based design of rotating machine packages

Hazard and operability analysis is a decisive factor to evaluate safety and reliability of plants considering the propagated effects due to consecutive failures of known critical components. A good design and analysis practice increases the robustness of the considered system in different operating conditions; however, in the plant design process, the use of a model-based design approach is fundamental to increase speed, efficiency and reliability. In this work, a tool for the 1-D simulation of thermal hydraulic plants is presented and applied to the analysis of critical system for compressor and gas turbine units, e.g. the lubrication circuits. Different from known commercial products, the proposed tool is implemented to work in cooperation with PidXP™ (the GE Nuovo Pignone P&ID definition tool), and it is optimized for fixed step solvers in order to easily include the rotating machine control logics within the simulation environment (essential prerequisite for hazard and operability evaluations). All the above makes it suitable for the fast prototyping of real time code. Moreover, the proposed tool represents a general tool in the model-based approach, and it can be used as a Simulink™ library of components that have been optimized and specifically designed in order to make easier the automatic generation of simulation models from P&Id schemes and technical documentation, reducing errors associated to data transcription and operator misbehaviour. In this work, proposed approach and test bench on experimental data for validation purposes are shown.

Automatic simulation model generation supported by data stored in low level controllers

One of the most widespread techniques to evaluate various aspects of a manufacturing system is discrete-event simulation (DES). However, building a simulation model of a manufacturing system is a difficult task and needs great resource expenditures. Automated data collection and model buildup can drastically reduce the time of the design phase as well as support model reusability. Since most of the manufacturing systems are controlled by low level controllers (e.g., PLCs, CNCs) they store structure and control logic of the system to be modeled by a DES system. The paper introduces an ongoing research of PLC program processing method for automatic simulation model generation of a conveyor system of a leading automotive factory. Results of the validation process and simulation experiments are also described through a case study.

Automatic Simulation Model Generation Based on PLC Codes and MES Stored Data

One of the most widespread techniques to evaluate various aspects of an existing manufacturing system is discrete-event simulation (DES). However, building a simulation model of a manufacturing system needs great resource expenditures. Automated data col-lection and model buildup can drastically reduce the time of the design phase as well as support model reusability. Since most of the manufacturing systems are controlled by low level controllers they store structure and control logic of the system to be modeled by a DES system. The paper introduces an ongoing research of PLC code processing method for automatic simulation model gen-eration of a conveyor system of a leading automotive factory.

CODIS – A FRAMEWORK FOR CONTINUOUS/DISCRETE SYSTEMS CO-SIMULATION

This paper presents CODIS, a co-simulation framework for continuous/discrete systems. Based on a well defined synchronization model and a generic architecture for continuous/discrete simulation models, this framework enables easy specification and automatic generation of simulation models. The supported simulators are Simulink for continuous components and SystemC for discrete components.

Simulation-based shop floor control: formal model, model generation and control interface

In this paper, a structure and architecture for the rapid realization of a simulation-based real-time shop floor control system for a discrete part manufacturing system is presented. The research focuses on automatic simulation model and execution system generation from a production resource model. An Automatic Execution Model Generator (AEMG) has been designed and implemented for generating a Message-based Part State Graph (MPSG)-based shop level execution model. An Automatic Simulation Model Generator (ASMG) has been designed and implemented for generating an Arena simulation model based on a resource model (MS Access 97) and an MPSG-based shop level execution model. A commercial finite capacity scheduler, Tempo, has been used to provide schedule information for the Arena simulation model. This research has been implemented and tested for six manufacturing systems, including The Pennsylvania State University CIM laboratory.

Automatic simulation model generation for simulation-based, real-time shop floor control

This paper presents a structure and architecture for automatic simulation model generation (for very detailed simulation models intended to be used for real-time simulation-based shop floor control). The simulation model code is generated from a shop floor resource model and a shop floor control model. The shop floor resource model provides much of the static information for the simulation model; while a shop level control model provides much of the dynamic information required by the simulation model. The simulation code generated can be used for traditional system analysis, but more importantly, it can also be used to control the manufacturing system by sending and receiving messages using an Ethernet communication link to a high-level task executing system. Six manufacturing systems are used to illustrate and test the validity of the simulation model generation methodology. Finally, factory level planning and scheduling activities using the generated simulation model are described.

Improving The Dutch Railway Services ByNetwork-wide Timetable Simulation

The DONS system, Designer Of Network Schedules, supports the development of timetables by generating regular interval timetables for the Dutch railway network; it is used to plan improved or new train services and to locate bottlenecks in the railway infrastructure. A new simulation module enables the planners to study the networkwide dynamic properties of DONS timetables. The direct link between DONS and this simulation module, enhanced by the re-use of previously defined DONS concepts and the automatic generation of simulation models from existing data, facilitates an integrated approach towards infrastructure and railway services development, focused on quality improvement.

Knowledge based automatic simulation model generation system

The main purpose of the research was to discover an easy fast and reliable way to generate simulation models and to create a model library. To solve this problem, the automatic element routine generation system (AERO) has been developed as an efficient way for automatic model development using domain specific automatic programming techniques. Behavioural and structural models are generated from Boolean equations, truth tables, HDL descriptions, schematic diagrams or incomplete specifications. By utilising this approach, simulation automation can be achieved by providing the same environment for model generation and simulation. Results show that the system could greatly reduce the cost of simulation model generation for CAD systems and consequently reduce the design cycle considerably.