Simulation Of Manufacturing Systems Research Articles

Simulation of a Flexible Manufacturing System for Machining Some Industrial Parts

One thought about this work because we consider that the operating procedure of flexible systems could be introduced in most factories that propose and have as a priority large-scale production. The mode of operation and planning of each robot is very close to reality thanks to the 3D simulation, made in the Alice three-dimensional graphic environment. With this procedure, one can highlight the main characteristic of a flexible manufacturing system, that is, the flexibility obtained through automation and at the same time the saving of working time. Thanks to the fact that a computerized machine system is used, any piece can be executed, at any time and in any quantity, representing an advantage for any factory that uses this system. Another major advantage is the development of new programs that allow the solution of numerous problems related to the coordination of the various components of the system and the optimal operation of the machine.

Continuous-flow simulation of manufacturing systems with assembly/disassembly machines, multiple loops and general layout

Performance evaluation methods are important to design and control manufacturing systems. Approximate analytical methods are fast, but they may be limited by the restrictive assumptions on the system. On the contrary, simulation has not specific limitations in its applicability, but the time to model and analyse a manufacturing system can increase as the level of detail addressed by the model increases. The main contribution of this study is presenting a computationally efficient methodology to simulate single-part continuous-flow manufacturing systems with assembly/disassembly machines, multiple loops, general layout and general inter-event time distributions. By using graph theory, a new method is presented to identify the machines causing slowdown, blocking and starvation in a general layout and determine the time before the occurrence of a state transition for each machine and the time before the fulfilment or depletion of each buffer. By advancing the time clock to the next event-time accordingly, the number of discrete events needed to be simulated is decreased compared to a discrete-event simulation with discrete flow of parts. As a result, the proposed method is on average 15 times faster than DES methods in the analysis of discrete-flow systems, and 110 times faster on average in the analysis of continuous-flow systems. The low computational time of the proposed method allows to simulate systems under general assumptions and in a very short time.

Design of experiment base scenario analysis in discrete event simulation of multi-path manufacturing system

Design of experiment base scenario analysis in discrete event simulation of multi-path manufacturing system

Layout planning and analysis of a Flexible Manufacturing System based on 3D Simulation and Virtual Reality

The high competition in the global market where the agile product launch and variable demand of goods by customers persuaded manufacturing companies to adopt Flexible Manufacturing System (FMS) and many companies have already implemented FMS solutions. On the other hand, manufacturing digitalization such as digital twin development, Industrial Virtual Reality (IVR), virtual modeling and 3D simulation of manufacturing systems offer new possibilities for effective facility layout planning, quick and easy modification and validation of production processes, analysis, and optimize the workflow and activities conducted on a factory floor. These new technological developments in digitalization have changed the thinking of manufacturing companies and they are eager to use digitalization solutions in their factory operations. However, there is a lack of harmonized methods and procedures to implement virtual modeling, 3D simulation, and IVR for layout planning and analysis of FMS. This paper proposed an approach for performance analysis of a FMS that is based on the 3D layout creation and simulation in a virtual environment, monitoring of key performance indicators via a digital dashboard, and immersive visualization through virtual reality. The relevance and feasibility of the proposed performance analysis approach are demonstrated by a case study.

Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods

In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most challenging task. This situation may even be hampered by unreliable supplier performance. This paper presents a generic decision support workflow, which first identifies the most critical external and internal factors which have a serious impact on delivery performance. Next, it suggests a method which combines traditional manufacturing system simulation with advanced machine learning techniques to support the improved daily routine lot-sizing and production scheduling activities in a HMLV company. Argumentation is motivated and illustrated by a detailed industrial case study.

Simulation Modeling for Energy-Flexible Manufacturing: Pitfalls and How to Avoid Them

Due to the high share of industry in total electricity consumption, industrial demand-side management can make a relevant contribution to the stability of power systems. At the same time, companies get the opportunity to reduce their electricity procurement costs by taking advantage of increasingly fluctuating prices on short-term electricity markets, the provision of system services on balancing power markets, or by increasing the share of their own consumption from on-site generated renewable energy. Demand-side management requires the ability to react flexibly to the power supply situation without negatively affecting production targets. It also means that the management and operation of production must consider not only production-related parameters but also parameters of energy availability, which further increase the complexity of decision-making. Although simulation studies are a recognized tool for supporting decision-making processes in production and logistics, the simultaneous simulation of material and energy flows has so far been limited mainly to issues of energy efficiency as opposed to energy flexibility, where application-oriented experience is still limited. We assume that the consideration of energy flexibility in the simulation of manufacturing systems will amplify already known pitfalls in conducting simulation studies. Based on five representative industrial use cases, this article provides practitioners with application-oriented experiences of the coupling of energy and material flows in simulation modeling of energy-flexible manufacturing, identifies challenges in the simulation of energy-flexible production systems, and proposes approaches to face these challenges. Seven pitfalls that pose a particular challenge in simulating energy-flexible manufacturing have been identified, and possible solutions and measures for avoiding them are shown. It has been found that, among other things, consistent management of all parties involved, early clarification of energy-related, logistical, and resulting technical requirements for models and software, as well as the application of suitable methods for validation and verification are central to avoiding these pitfalls. The identification and characterization of challenges and the derivation of recommendations for coping with them can raise awareness of typical pitfalls. This paper thus helps to ensure that simulation studies of energy-flexible production systems can be carried out more efficiently in the future.

Dynamic optimal control and simulation for unreliable manufacturing systems under perishable product and shelf life variability

This paper develops an optimal control policy for unreliable manufacturing systems evolving in a stochastic and dynamic environment and producing perishable products with limited and random shelf lives. The developed optimal control policy aims to minimize the total cost, composed of backlog, inventory holding and disposal costs. To do this, we develop a stochastic dynamic programming model and the obtained optimality conditions are solved using a numerical approach to determine the parametrized structure of the resulting optimal control policy. A simulation-based optimization approach is used to optimize the parameters of the developed control policy. This policy is subsequently analyzed using multiple sensitivity analyses and compared to other policies from the literature. We also study shelf life variability and its influence on the system performance. Numerical examples show that in the case of a random shelf life, the developed control policy presents a lower total cost in comparison with other policies from the literature.

Enabling Industry 4.0 impact assessment with manufacturing system simulation: an OEE based methodology

Increasing digitalization in manufacturing, often associated with terms like Industry 4.0 (I4.0) or Smart Manufacturing, is a topic of crucial concern for manufacturing companies. Different digital technologies (DTs) can be integrated into manufacturing processes and systems aiming at increasing flexibility, product quality or productivity. The type and scope of potential DTs must be carefully selected when planning and improving a manufacturing system. The definition and configuration could be supported by simulation techniques that assess the DTs' impact on the manufacturing system and its final performance. However, parametrizing the DTs into a simulation tool is not straightforward since appropriate models might be challenging to obtain and actual impacts of DTs are uncertain. Against this background, the paper presents methods to enable a simulation-based assessment while considering the impact of not just individual but also a combination of DTs. The paper introduces a framework to define the base characteristics of selected DTs within a manufacturing system and their parameterization into a commercial simulation tool. Furthermore, the usability and expectable results are demonstrated in a case study.

An ontological approach for modelling evolutionary knowledge of prognostic method selection

The selection of appropriate predictive maintenance methods based on the current state of a manufacturing system, its machines, and its components is not an easy task due to the multitude of physical and virtual resources available. Moreover, the value of the prognostic information provided by a prognostic method, when applied to a given machine, depends on the system structure and the production and maintenance planning process. Therefore, it is necessary to consider the impact of such information on the system’s key performance indicators to assess the real benefits of each prognostic method. Based on knowledge from predictive maintenance approaches, manufacturing system simulation, and production and maintenance planning, an appropriate semantic model allows establishing a shared vocabulary and understanding among all these fields, along with a description of their relationship. Thus, this paper proposes an ontology of domain termed Ontological MetaMaintain (OntoMM), which specifies the description of concepts and their relationships to provide evolutionary knowledge on domain structure which further specializes in information flows. It is a novel semantic architecture in an ontology network with three modules that address automated prognostic method selection in an industrial environment.

Challenges and Opportunities for Integrating Augmented Reality and Computational Fluid Dynamics Modeling under the Framework of Industry 4.0

The industrial requirements change rapidly due to the technological advancements, and the need to immediately investigate prospective system alternatives to develop a more efficient production system design is more intense than ever. Manufacturing systems simulation as a key technology of Industry 4.0 is a powerful tool towards optimizing manufacturing processes. Computational Fluid Dynamics (CFD) simulation can help smart factories gain a competitive advantage due to low cost, low risk and the provided meaningful insights. However, while CFD tools can provide users with precise simulations, there is strong need for interactive design and analysis tools. Additionally, fluid flow behavior is a challenging problem for cognitive interpretation and visualization. Therefore, engineers/technicians need to visualize the behavior of the desired flow field to improve their cognitive ability in problem-solving. As a result, Augmented Reality (AR) technology is proposed as the solution for an enhanced virtual learning and training environment in smart factories. To address these challenges, this research work presents an Industrial Internet of Things (IIoT) framework for simulation and visualization of selected monitored values based on AR and CFD in a cloud-computing environment for the intuitive visualization of the data on smart devices. The applicability of the proposed framework has been validated in a real-life case study derived from an Original Equipment Manufacturer (OEM) supplier of Industrial refrigerator systems.

Model-based automatic generation of digital twin models for the simulation of reconfigurable manufacturing systems for timber construction

Reconfigurable manufacturing systems adapt to changing requirements and therefore offer a solution to the challenges associated with increasing product variety and shorter product life cycles. This is especially helpful in the prefabrication in timber construction, as components and requirements vary widely between projects. However, reconfigurations must be quick to keep downtimes short. The planning of reconfigurations is a complex problem where digital twins and simulation models of the manufacturing systems are necessary for simulation and validation. In our timber prefabrication use case, building components and requirements change from project to project, and the manufacturing system needs to reconfigure frequently. The building components and the configuration of the manufacturing system are designed simultaneously during a co-design process to ensure the integrity of the manufacturing process in a changing manufacturing environment. Hence, this may require many iterations of the planning process. Each iteration needs validation through a simulation. However, the manual generation of simulation models is time-consuming and error-prone and thus poses an obstacle for the digital planning phase. This paper addresses this issue and presents a model-based approach to generate simulation models for reconfigurable manufacturing systems. Our system and its components are modeled in automation markup language as this domain-specific language allows us to model not only kinematics and geometry but also behavior, skills, and interfaces to check the compatibility of components. We apply the method to our use case with the IntCDC wood prefabrication system. The proposed method builds a component graph for a configuration of the manufacturing system and generates the required packages for the simulation in Gazebo and ROS. To build simulation models automatically, we apply model-to-model and model-to-text transformation techniques. The proposed method is suitable for integration in the digital co-design workflow of IntCDC and allows fast iterations with continuous simulations of machine configurations throughout the planning process.

Fault tolerance makespan synthesis in multi-process systems via resource sharing and backtracking

This paper proposes an algorithms to find the time optimal fault-tolerant controllable string for multi-process systems, in which some specifications are enforced to the nonfaulty system, such that the system can recover from faults and satisfy the specifications efficiently through resource sharing. We first derive an algorithm to check the fault-tolerant property via the abstraction of the system, then a backtracking algorithm is sequentially used to find the optimal fault-tolerant string based on the abstraction of the system. Moreover, the proposed fault tolerant string synthesis algorithms are tested on a simulated manufacturing system to show their effectiveness.

The efficiency of modeling and simulation of manufacturing systems using Petri nets

The paper is a partial synthesis of the work that a team within the Faculty of Managerial and Technological Engineering has in the field of modeling and simulation of systems with Petri nets. The paper deals with three issues. The first part is Modeling and simulation of manufacturing systems with timed Petri nets. The first part presents the way in which the performances of some manufacturing systems are evaluated using timed Petri nets. The first example is a manufacturing system specific to the textile industry. The system has three production lines. In this case, the modeling with timed Petri nets allows the analysis of the operation of the flow lines and highlights the bottleneck. Another modeled manufacturing system is specific to a series production, more precisely it refers to the manufacture of gas cookers within a company from Oradea. The model with Petri nets also describes the supply sequences of the stocks of parts and subassemblies. The next modeled system is an automated manufacturing system for the packaging of detergents. The complexity of the Detergent Packing System and the high level of automation, require the use of efficient modeling and simulation methods to verify the validity of the solutions adopted. In this chapter is present the model of a robotic welding cell. The second part of the paper, the transition from timed Petri nets to hierarchical colored Petri nets, shows how Petri nets were developed for a robotic cell for metallographic analysis. The third part is dedicated to modeling with colored Petri nets timed with complex colors of the flexible manufacturing cell A007 within the Department of Industrial Engineering.

Demonstration and evaluation of a digital twin-based virtual factory.

Smart manufacturing, tailored by the 4th industrial revolution and forces like innovation, competition, and changing demands, lies behind the concurrent evolution (also known as co-evolution) of products, processes and production systems. Manufacturing companies need to adapt to ever-changing environments by simultaneously reforming and regenerating their product, process, and system models as well as goals and strategies to stay competitive. However, the ever-increasing complexity and ever-shortening lifecycles of product, process and system domains challenge manufacturing organization’s conventional approaches to analysing and formalizing models and processes as well as management, maintenance and simulation of product and system life cycles. The digital twin-based virtual factory (VF) concept, as an integrated simulation model of a factory including its subsystems, is promising for supporting manufacturing organizations in adapting to dynamic and complex environments. In this paper, we present the demonstration and evaluation of previously introduced digital twin-based VF concept to support modelling, simulation and evaluation of complex manufacturing systems while employing multi-user collaborative virtual reality (VR) learning/training scenarios. The concept is demonstrated and evaluated using two different wind turbine manufacturing cases, including a wind blade manufacturing plant and a nacelle assembly line. Thirteen industry experts who have diverse backgrounds and expertise were interviewed after their participation in a demonstration. We present the experts’ discussions and arguments to evaluate the DT-based VF concept based on four dimensions, namely, dynamic, open, cognitive, and holistic systems. The semi-structured conversational interview results show that the DT-based VF stands out by having the potential to support concurrent engineering by virtual collaboration. Moreover, DT-based VF is promising for decreasing physical builds and saving time by virtual prototyping (VP).

Idle Duration Prediction for Manufacturing System Using a Gaussian Mixture Model Integrated Neural Network for Energy Efficiency Improvement

Manufacturing activities dominate the energy consumption and greenhouse emissions of the industrial sector. With the increasing concerns of greenhouse gas (GHG) emissions and climate change in recent years, the significance of the performance in terms of sustainability of manufacturing has been gradually recognized by both academia and industry. Various researches have been implemented to analyze, model, and reduce the energy consumption of manufacturing activities toward sustainable manufacturing. In a typical manufacturing system with multiple machines and buffers, the state of a certain machine is not only determined by the machine itself, but also the states of the adjacent machines and buffers. Therefore, machines may be in idle states due to nonincoming part from the upstream section of the manufacturing system or noncapacity to hold the delivered part to the downstream section of the manufacturing system. Those idle machines consume energy without production if there is no appropriate energy control strategy. In this article, we focus on the reduction of the energy waste for those idle machines in a typical multi-machine and multi-buffer manufacturing system. A Gaussian mixture model (GMM) integrated neural network is proposed to predict the duration of the idle periods for the idle machines, during which optimal energy control action can be identified and implemented under the constraint of production throughput of the manufacturing system. A manufacturing system simulator is built to provide the training dataset including the information, such as production throughput, energy consumption, buffer content, and failure rate, to the proposed neural network. A numerical case study for a five-machine-and-four-buffer manufacturing system is conducted to validate the effectiveness of the proposed prediction model in terms of the energy waste reduction for the idle machines. Note to Practitioners —This article proposes a prediction model to forecast the idle duration of the manufacturing machines in a typical multi-machine and multi-buffer manufacturing system. With this predicted result, two concerns in energy control for the idle machine, i.e., throughput protection and energy consumption reduction, can be more accurately modeled in decision-making procedure. Optimal energy control actions under the constraints of throughput maintaining and energy saving can be identified and implemented considering different warmup energy consumption and warmup time of the machines to reduce the energy waste for those machines in idle states without any production and thus, improve the energy efficiency of the entire manufacturing system.

Building a Reusable Data Model to Support Systematic Layout Planning with Discrete Event Simulation of Flexible Manufacturing Systems

Manufacturing has undergone a greater change in recent years than it has over almost the entire past century. Due to the variety of products, shortening delivery times and changing customer habits, the COVID-19 pandemic has demanded greater flexibility from manufacturing companies than ever before. One of the fundamental parts of manufacturing flexibility is the design of manufacturing layouts, which has so far not followed automation and methodological developments with regard to technological areas of manufacturing. In this article, a new method for supporting traditional factory design methods with digital manufacturing tools is introduced.

Design of experiment base scenario analysis in discrete event simulation of multi-path manufacturing system

Design of experiment base scenario analysis in discrete event simulation of multi-path manufacturing system

Design and simulation of a flexible manufacturing system for manufacturing operations of railcar subassemblies

The application of the elements of the Fourth Industrial Revolution (4IR) through the development of a Flexible Manufacturing System (FMS) for manufacturing operations in the railcar industry will promote flexibility, intelligent coordination of the manufacturing operations, efficient handling and quality control. Some production systems in the railcar industry are non-responsive to changes in real time thereby leading to reduction in productivity, hence, the need for a FMS that will cater for the dynamics of manufacturing operation. This work proposes a FMS, which encompasses the assembly line, lean production, logistics and quality assurance. The system comprises of the Radio Frequency Identification Technology (RFID) for components identification and process control, arrays of sensors and cameras, automated material storage and supply, standard interfaces such as the interface for the internet of things (IoT), the robotic welding system as well as a robust intelligent control system. A framework for the implementation of the FMS was developed while the simulation of the designed system was performed using the Anylogic 8.2.3. software. Based on the results obtained, there was an inverse relationship between the operating cycle time of the conveyor and the conveyor speed per cycle when the conveyor’s performance was simulated at a speed of 3 m/s and 7 m/s. The results showed that the system can suitably perform the sequence of assembly and quality assurance operations during the manufacturing of railcar subassemblies with minimal interruptions and human intervention. This will promote production of component parts with high structural and dimensional integrity with significant reduction in the manufacturing cycle time and cost.

Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor

ABSTRACT In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods.

Modeling and Simulation of a Flexible Manufacturing System—A Basic Component of Industry 4.0

The field of Flexible Manufacturing Systems (FMS) has seen in recent years a dynamic development trend and can now be considered an integral part of intelligent manufacturing systems and a basis for digital manufacturing. Developing the factory of the future in an increasingly competitive industrial environment involves the study and analysis of some FMS key elements and managerial, technical, and innovative efforts. Using a new approach, thus paper presents a material flow design methodology for flexible manufacturing systems in order to establish the optimal architecture of the analyzed system. The research offers a solution for modeling and optimizing material flows in advanced manufacturing systems. By using a dedicated analysis and simulation software, the structure of the system can be established and specific technical and economic parameters can be determined for each processing and transport capacity. Different processing scenarios will be evaluated through virtual modeling and simulations in order to increase the performance and efficiency of the system. Thus, an interactive tool useful in the design and management of flexible manufacturing lines will be developed for companies operating in the industrial sector. The application of this paper is mainly in the field of development of intelligent manufacturing systems, where the control system will make and use simulations in order to analyze current parameters and to predict the future.