Digital twin based accuracy compensation

Biopharma Is Going Digital … Bit by Bit

The CNC machine tool is the passive executor of machining code. It cannot predict the machining accuracy during machining. If the error is found to be out of tolerance after processing, it will not only scrap the parts, but also greatly affect the processing efficiency. This phenomenon is very prominent when machining sculptured surface parts with five-axis machine tools. Therefore, this paper proposes a Digital Twin (DT) modeling method of five-axis machine tools for predicting Continuous Trajectory Contour Error (CTCE) caused by tracking errors and geometric errors. The DT consists of three parts: the Setpoints Trajectory (ST) model, the Actual Trajectory (AT) model considering tracking errors and geometric errors and the CTCE model. For a specific machine tool, according to the basic geometric information of the machine tool (tool length, kinematic chain information, etc.) and 41 geometric errors, the DT can be established. Inputting the Setpoints Positions (SPs) and the Linear Encoder Detection Positions (LEDPs), the DT can be used to predict the Tool-Tip Position Trajectory (TTPT) contour error and the Tool Orientation Trajectory (TOT) contour error. In order to verify the proposed method experimentally, the KMC400S U five-axis machine tool is selected to establish its DT by which the contour error of the S-shaped trajectory are predicted offline. Then, the DMU50 five-axis machine tool is selected to establish its DT to predict the contour error of the circular trajectory in real time. Combined with the deep motion mechanism, this paper proposes a DT modeling method for the vertical application scene of parts machining accuracy prediction, which is of great significance to developing the DT application theory and ensuring the machining accuracy of parts.

Machine tools play a key role in the manufacturing industry for shaping metals. These machine tools have evolved significantly through the adaptation of Industry 4.0/Industry 5.0 norms. This transformation has revolutionized the machine tools and manufacturing industries. These ground-breaking changes and the reforms in the modern machine tool processes are highlighted in this chapter, especially focusing on the virtual replicas of processes and products thus leading to digital twins (DTs) of complex mechanical systems for better understanding. This chapter discusses the associated challenges of traditional machine operations, justifying the need for integrating digital technologies in mass production industries. DT is explained detailing its significance in machine tool perspective and the emerging role of DT in modern industries. The steps involved in creating the DT of machine tools are discussed, for example the role of sensor integration, data acquisition system, 3D modeling, and simulation. To make the concepts more relatable, the chapter includes real-world case studies that demonstrate how industries are leveraging DT to address the complexities of machine tool operations. The chapter concludes by summarizing the pivotal role these technologies play in advancing machine tools, ensuring they remain integral to the digital age. Combining technical depth with seamless accessibility, this chapter serves as a guide for researchers, professionals, and enthusiasts navigating the modern era of manufacturing.

Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effective DT is to act as a fast, accurate, and maintainable surrogate of its physical counterpart, the physical twin (PT). To this end, machine learning (ML) is frequently employed to (i) construct real-time DT prototypes using efficient reducedorder models (ROMs) derived from high-fidelity simulations of the PT’s nominal behavior, and (ii) specialize these prototypes into DT instances by leveraging historical sensor data from the target PT. Despite the broad applicability of ML, its use in DT engineering remains largely ad hoc. Indeed, while conventional ML pipelines often train a single model for a specific task, DTs typically require multiple, task- and domain-dependent models. Thus, a more structured approach is required to design DTs. In this paper, we introduce DesCartes Builder, an opensource tool to enable the systematic engineering of ML-based pipelines for real-time DT prototypes and DT instances. The tool leverages an open and flexible visual data flow paradigm to facilitate the specification, composition, and reuse of ML models. It also integrates a library of parameterizable core operations and ML algorithms tailored for DT design. We demonstrate the effectiveness and usability of DesCartes Builder through a civil engineering use case involving the design of a real-time DT prototype to predict the plastic strain of a structure.

Digital twin technology has been increasingly applied in healthcare and patient well-being in recent years. This paper provides an overview of the current methods and applications of digital twins in the healthcare field. One such application is digital twins in precision healthcare, where digital twins are used to create patient-specific models to assist in diagnosis and treatment planning. Digital twins are also used in hospital/clinic management, where they help to optimize resource allocation and workflow processes. In response to the COVID-19 pandemic, digital twins have been utilized to detect outbreaks and predict disease spread. In addition, digital twins have been applied in bio-manufacturing and pharmaceutical industry to improve manufacturing processes. Another application area is machine learning and modeling, where digital twins are used in machine learning, data generation, and system modeling for applications in healthcare and disease prediction. Security and ethical issues related to digital twins are also discussed in this paper, as privacy concerns and data protection remain important considerations in the application of digital twin technology in healthcare. Finally, the paper concludes by discussing the future challenges and directions of future work in this field. These include the need to develop more accurate and sophisticated digital twin models, addressing interoperability and integration issues, and further exploring the potential of digital twin technology in emerging areas such as telemedicine and personalized medicine.

In modern medicine, novel technologies play a key role in improving the diagnosis, treatment, and management of patient health. One of the most promising innovations is Digital Twin technology, which enables the creation of virtual models of real-world objects, including human organs. This technology opens new possibilities for personalised medicine, disease prediction, and the optimisation of medical processes. Digital Twins are already actively used in various industries, such as manufacturing, aviation, and urban planning, but their potential in medicine is only beginning to unfold. They allow for creating detailed models of an individual patient’s body, contributing to more accurate diagnostics, simulation of treatment responses, and enhanced efficiency of medical interventions. This article explores the key aspects of Digital Twin medical applications, their advantages, challenges, and development prospects. Special attention is given to the technical foundations of this technology, its use in personalised medicine, health monitoring, disease prediction, and the optimisation of healthcare facilities. For the practical part, the study shows how to create a Digital Twin of a heart based on real patient data. Additionally, the study investigates a model of the heart’s Digital Twin, demonstrates its use, and illustrates how to replicate heart functionality using the Digital Twin. The study describes how a part of a Digital Twin was created using the suggested architecture and outlines how to use and extend this functionality. More than that, the study shows how to integrate artificial intelligence into the Digital Twin architecture and how artificial intelligence algorithms help create Digital Twins. The research shows how to build the Digital Twin using machine learning and artificial intelligence technologies, and how to choose the mathematical model for the Digital Twin. To conclude, the study defines further work, problems, and difficulties in creating the Digital Twin of the heart. The study shows the perspectives on using Digital Twins, their practicality, and real-life cases where Digital Twins can save lives. Also, the study defines the next steps for the Digital Twin creation, testing, implementation, and usage in the real-life healthcare industry.

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

Noise monitoring is important in the context of manufacturing because it can help maintain a safe and healthy workspace for employees. Current approaches for noise monitoring in manufacturing are based on acoustic sensors, whose measured sound pressure levels (SPL) are shown as bar/curve charts and acoustic heat maps. In such a way, the noise emission and propagation process is not fully addressed. This paper proposes a digital twin (DT) for noise monitoring in manufacturing using augmented reality (AR) and the phonon tracing method (PTM). In the proposed PTM/AR-based DT, the noise is represented by 3D particles (called phonons) emitting and traversing in a spatial domain. Using a mobile AR device (HoloLens 2), users are able to visualize and interact with the noise emitted by machine tools. To validate the feasibility of the proposed PTM/AR-based DT, two use cases are carried out. The first use case is an offline test, where the noise data from a machine tool are first acquired and used for the implementation of PTM/AR-based DT with different parameter sets. The result of the first use case is the understanding between the AR performance of HoloLens 2 (frame rate) and the setting of the initial number of phonons and sampling frequency. The second use case is an online test to demonstrate the in-situ noise monitoring capability of the proposed PTM/AR-based DT. The result shows that our PTM/AR-based DT is a powerful tool for visualizing and assessing the real-time noise in manufacturing systems.

Noise monitoring is important in the context of manufacturing because it can help maintain a safe and healthy workspace for employees. Current approaches for noise monitoring in manufacturing are based on acoustic sensors, whose measured sound pressure levels (SPL) are shown as bar/curve charts and acoustic heat maps. In such a way, the noise emission and propagation process is not fully addressed. This paper proposes a digital twin (DT) for noise monitoring in manufacturing using augmented reality (AR) and the phonon tracing method (PTM). In the proposed PTM/AR-based DT, the noise is represented by 3D particles (called phonons) emitting and traversing in a spatial domain. Using a mobile AR device (HoloLens 2), users are able to visualize and interact with the noise emitted by machine tools. To validate the feasibility of the proposed PTM/AR-based DT, two use cases are carried out. The first use case is an offline test, where the noise data from a machine tool are first acquired and used for the implementation of PTM/AR-based DT with different parameter sets. The result of the first use case is the understanding between the AR performance of HoloLens 2 (frame rate) and the setting of the initial number of phonons and sampling frequency. The second use case is an online test to demonstrate the in-situ noise monitoring capability of the proposed PTM/AR-based DT. The result shows that our PTM/AR-based DT is a powerful tool for visualizing and assessing the real-time noise in manufacturing systems.

The cellular network standard 5G meets the networking requirements for different industrial use cases due to the advantages of low latency, high bandwidth, and high device density while providing a very good quality of service. These capabilities enable the realization of wireless digital twins (DTs), a key element of future cyber-physical production systems. DTs for prediction, monitoring, and control of machine tools need physical modeling as well as the bidirectional exchange of information between the digital and the physical world. 5G is a wireless communication technology with the potential to disruptively change industrial communication. 5G enables wireless, highly scalable, and flexible realization of even safety- and latency-critical connections. In this paper, a 5G enabled DT of a machine tool for process control, monitoring and simulation is developed and implemented. A bidirectional communication between the physical machine tool and the DT is realized via 5G. Moreover, process prediction is enabled based on physics simulation. Next to the physical modeling of the machine behavior, a 5G-capable interface between the input and output signals of the machine control system and the developed DT is implemented. Moreover, the DT is migrated in a wireless form to an edge server. Furthermore, the capabilities of the DT are demonstrated. Therefore, the architecture and implementation of the DT as well as its benefits and challenges are outlined.

Digital twins (DTs) are rapidly changing how manufacturing companies leverage the large volumes of data they generate daily to gain a competitive advantage and optimize their supply chains. When coupled with recent developments in machine learning (ML), DTs have the potential to generate invaluable insights for process manufacturing companies to help them optimize their manufacturing processes. However, this potential has yet to be fully exploited due to the challenges that process manufacturing companies face in developing and implementing DTs in their organizations. Although DTs are receiving increasing attention in both industry and academia, there is limited literature on how to apply them in the process industry. To address this gap, this paper presents a framework for developing ML-based DTs to predict critical process parameters in real time. The proposed framework is tested through a case study at an international process manufacturing company in which it was used to collect and process plant data, build accurate predictive models for two critical process parameters, and develop a DT application to visualize the models’ predictions. The case study demonstrated the usefulness of the proposed DT–ML framework in the sense that it provided the company with more accurate predictions than the models it previously applied. The study provides insights into the value of applying ML-based DT in the process industry and sheds light on some of the challenges associated with the application of this technology.

The article demonstrates the applications, essence and possibilities of digital twin technology and explains the advantages of digital twin technology and potential challenges. A digital twin is a digital copy that can be used to simulate the status of a physical object or system. The integration of digital twin technologies with the Internet of things, Big Data and Artificial intelligence offers innovative solutions for quicker detection of arisen problems (or problems to arise) related to changes in a real physical object and for making relevant decisions. The development of such solutions in the healthcare is crucial for preserving human health and providing people with higher-quality medical care. It is possible to get an early diagnosis of the disease and the selection of a more effective treatment method on the produced digital twin by transferring the patient’s physical characteristics and changes in his/her body to the digital environment. The article analyzes medical digital twins, categorizing their benefits into patient health, cost reduction, self-management, and other benefits. In order to ensure adaptability and effectiveness in disease therapy and healthcare management decision-making, the patientoriented ontology of healthcare is examined, and a four-level ontological model is suggested to create its digital twin. The creation of a patient-oriented digital twin of healthcare requires the creation of a digital twin of existing physical objects at each level of its ontological model. The creation of digital twin in healthcare opens wide opportunities for making decisions on provision of safe and high-quality medical care to patients.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening.

Fast flexible multibody dynamic analysis of machine tools using modal state space models