A model-based Digital Twin to support responsive manufacturing systems

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.

Urban Air Mobility (UAM) systems offer a three-dimensional transportation alternative by using low-altitude airspace, with the potential to reduce travel times and improve access to mobility in regions underserved by current transportation systems. To support efficient design and operation of UAM systems, we develop an integrated optimization framework in response to three interrelated challenges: (i) land use, aeronautical feasibility, community acceptance and other factors that restrict the number of potential locations for vertiports, (ii) bidirectional demand–supply interaction that needs to be considered, as the level of service influences demand for UAM and operators adjust the level of service in response to demand, and (iii) strong interactions between strategic decisions on the distribution of ground infrastructure, tactical decisions on eVTOL fleet size and operational decisions on dispatching and repositioning. Analyzing the decisions in isolation can lead to poor estimates of the overall system performance. The framework consists of (1) a knock-off criteria analysis model for the identification of a realistic set of candidate locations for vertiports, (2) integer programming models in which strategic, tactical and operational decision levels are modeled, and (3) pre-processing techniques to generate near-optimal solutions for real-world instances. By applying the framework in a large-scale real-world setting in the Île-de-France region, we demonstrate complex interactions between strategic, tactical, and operational decision levels and customer demand, revealing various trade-offs between operator profit and traveler generalized travel costs. • Provide realistic candidate vertiports in the Île-de-France region using knock-off criteria screening. • Integrate vertiport location selection with eVTOL fleet sizing and operations and demand interaction. • Identify non-linear trade-offs between operator profit and user generalized travel costs. • Conclusions on the economic viability of UAM operations. • Identification of UAM network structures with distinct vertiport roles.

Monitoring manufacturing systems using AI: A method based on a digital factory twin to train CNNs on synthetic data

Digital twin technology has proven to be a transformative tool in the development of smart factories by providing real-time data-driven simulations and virtual representations of physical assets and systems. This paper explores the integration of digital twins in the context of Industry 4.0 and sustainability, highlighting their potential to increase operational efficiency, optimize material usage and minimize waste. To demonstrate these benefits, case studies are presented in which a digital twin of a real manufacturing system was implemented. The digital twin enabled us to run different “what-if” scenarios to evaluate the improvements in the manufacturing system, efficiency and reduction of raw material consumption and waste by incorporating quality control operations at all assembly stations. Based on the analyzed results of both case studies, we demonstrated that additional quality control operations had a significant impact on the efficiency of the manufacturing system and its sustainability, as waste was significantly reduced. The proposed approach has proven to be highly effective for different types and sizes of manufacturing systems, especially those with high waste generation.

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context.

Maritime fuel management (MFM) controls the procurement and consumption of the fuels used on board and therefore manages one of the most important cost drivers in the shipping industry. At the operational level, a shipping company needs to manage its fuel consumption by making optimal routing and speed decisions for each voyage. But since fuel prices are highly volatile, a shipping company sometimes also tactically procures fuel in the forward market to control risk and cost volatility. From an operations research perspective, it is customary to think of tactical and operational decisions as tightly linked. However, the existing literature on MFM normally focuses on only one of these two levels, rather than taking an integrated point of view. This is in line with how shipping companies operate; tactical and operational fuel management decisions are made in isolation. We develop a stochastic programming model involving both tactical and operational decisions in MFM in order to minimise the total expected fuel costs, controlled for financial risk, within a planning period. This paper points out that after the latest regulation of the Sulphur Emission Control Areas (SECA) came into force in 2015, an integration of the tactical and operational levels in MFM has become important for shipping companies whose business deals with SECA. The results of the computational study show that isolated decision making on either tactical or operational level in MFM will lead to various problems. Nevertheless, the most severe consequence occurs when tactical decisions are made in isolation.

Small and medium enterprises (SMEs) face major challenges when implementing Digital Twin (DT) technology due to high infrastructure costs and complex integration requirements. While the development of comprehensive DT models remains an expensive, resource-intensive activity, most issues often lie in the complex integration processes required to connect existing manufacturing systems with simulation environments. This paper presents a novel low-cost data architecture for integrating data from enterprise manufacturing systems (such as MES and WMS) into external simulation-as-a-service platforms, facilitating DT integration while cutting the related costs. The proposed methodology addresses the challenge of connecting different IT systems with simulation services through a data-driven integration approach. Rather than relying on expensive enterprise integration architectures, the solution leverages a simplified framework focusing on intelligent data transformation with minimal pressure on existing infrastructure. The integration strategy centers on comprehensive data modeling that standardizes how products, resources, and materials information are represented across heterogeneous systems for DT consumption, particularly addressing the complexities of make-to-order environments where product-centric information requires flexible approaches. The work has been validated through the implementation in a medium-sized manufacturing company, and it demonstrated simplicity and cost-effectiveness compared to traditional integration approaches. The data architecture successfully transformed production plans, workforce allocation data, and warehouse materials records from native formats into standardized simulation-ready models. By isolating and addressing the integration complexity while utilizing existing simulation-as-a-service providers for the computationally intensive modeling aspects, the proposed methodology significantly reduces the overall burden of DT implementation for SME, supporting the adoption of this technology through a cost-effective integration strategy.

Digital Twin-based bottleneck prediction for improved production control

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.

In orthopedic surgery department, multiple surgeries are carried out in the same operating room every day. Each surgery may have a random duration, differs from the scheduled time, which results in room idle time and patient waiting time. One of the major factors affecting the idle and waiting times is the schedule of surgeries in the operating room. To better sequence of surgeries to reduce idle time and waiting time, an effective performance evaluation method is needed. Although discrete event simulation can be used to evaluate the performance of surgery schedules, it suffers from long simulation time, which makes difficult in scheduling optimization. In this paper, an analytical model to evaluate the performance of operating room schedules in orthopedic surgery department is introduced. Using such a model, the expected idle time and waiting time for a given surgery schedule can be calculated accurately. Such a work provides substantial easiness for optimization of operating room schedules and investigation of the impact of different schedules.

In this study, a crosspoint‐queued (CQ) switch that features a buffer size larger than one and employs the work‐conserving random scheduling algorithm under uniform Bernoulli i.i.d. traffic is investigated through both theoretical analysis and simulation. The effect of the crosspoint buffer state on switch performance is modelled and quantified. A novel method for calculating the throughput and delay of a CQ switch with a given switch size, input load, and buffer size is proposed. Compared with existing models, the proposed calculation model offers a simpler and more accurate result, and can serve as a theoretical guide in designing high‐speed switch fabrics.

In multistage parallel interference cancellation, the tentative decisions for different users at each stage are mutually correlated, and they are also correlated with Gaussian noise. By taking the correlations into consideration and employing Price's theorem, an analytical model is proposed to refine the variance evaluation of the final decision variable. It is shown that the refined variance is very close to that obtained by simulation and can improve the accuracy in bit error rate evaluation.

As a key part of digital transformation, digital twin is an important concept for achieving smart manufacturing. However, there remains many challenges about the digital twin concept and how it can be implemented in real manufacturing systems, especially among small and medium-sized enterprises. Relevant standards that provide common terminology and implementation guidelines can help address these challenges. This paper discusses the concept, definitions, applications, and recent standardization efforts of digital twins. In particular, International Organization for Standardization (ISO)/Draft International Standard (DIS) 23247, Digital Twin Framework for Manufacturing, is introduced. The scenarios of three digital-twin use cases in manufacturing are explored in depth and the procedures of implementing such use cases according to the ISO/DIS 23247 framework are presented. The designs of these use case scenarios can be implemented at the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems (SMS) Test Bed or on any manufacturing shop floor that has similar maturity level of digital transformation. In addition, the procedures of design and implementation of ISO/DIS 23247 can be used for developing additional applications of digital twins in manufacturing.

The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications.