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

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.

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.

Digital twin based accuracy compensation

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.

In this paper, author present the theory and methodology of sliding mode-variable structure control of the distributed vibration systems, including the control design at the modal state space and robustness conditions, a discussion on independent modal space control (IMSC) with sliding mode control strategy, etc. The methodology presented in the paper will open a new way for the active robust control of distributed vibration systems.

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.

We present a structural digital twin system based on component-based parametric reduced order modeling using Reduced Basis Finite Element Analysis (RB-FEA) framework. This enables fast, detailed, and holistic modeling of large-scale structures, such as floating and fixed production and drilling platforms, and crucially, the digital twin's parametric nature and fast solve time enables the system to be connected seamlessly with inspection and sensor data. We demonstrate RB-FEA based structural digital twins, and capabilities for connecting them to sensor data and data science methods to increase predictive power and engineering insight. We show how the digital twin is a key enabler for a Digital Thread – a data-driven architecture that links together information generated from across the product lifecycle. Inspection data, such as thickness measurements, can be directly mapped into the structural digital twin. Sensor data, such as motion and strain can be transformed into loading data, e.g. to estimate sea-states and calculate stress and fatigue accumulation based on actual conditions, or to "tune" model parameters to match data-based measurements, such as mode shapes and frequencies. This enables continuous tracking of the asset and assessment of its current state, based on quantitative and detailed structural analysis, at any time. Ultimately the structural digital twin provides an interpretation layer for integration of monitoring and inspection data, leading to a fully automated digital thread. We present a framework, based on a cloud-based simulation platform and Python-based scripting, to automate the workflows listed above. Digital twin provides a single source of truth about asset condition and allows data driven communication and decision taking between multiple stakeholders – operators, contractors, regulators, etc. We will show examples of real-world applications of RB-FEA digital twin for FPSO and Jacket platforms and development work for digital thread with emphasis on data driven value creation along the entire life cycle of the asset. The Reduced Basis FEA based structural digital twin is a key enabler of data driven digital thread in addition to sensor frameworks and data science systems. RB-FEA provides fast, parametric, and physics-based analysis providing a transparent interpretation layer for data obtained from other systems. It provides a key element allowing knowledge management along asset lifecycle – from concept to decommissioning. High fidelity, physics based global simulations, such as RB-FEA digital twins, are a key enabler for effective digital transformation of industrial operations. They enable real-time insights into asset performance that are otherwise unattainable, providing tools for Asset Integrity Management, Risk Based Inspection, Automation and Optimization of Operations as well as enabling full digital thread enhancing Safety and CAPEX and OPEX.

An informed-decision making in managing risks due to climate-driven hazards for emergency response, designing preventive interventions, or policymaking for future requires either short-term and scenario-based assessments or long-term and uncertain assessments. Data requirements, spatial and temporal scales, observations required, and modelling techniques employed change drastically depending on the scope of the risk assessment. Digital twins (DT) in applications for natural hazards provide a great opportunity for significant improvements in disaster management. What makes DTs possible today is various technological advancements such as embedded sensors, cloud computing, edge computing, IoT. However, DTs also require a digital representation of the physical counterpart, mostly in the form of a computational or a data-driven model, to be able to predict future states. The utilisation of complex computational models in DTs is generally hindered by their relatively high computational budget and runtimes. A pathway to involve such models in (near) real-time decisions in DTs for geohazards is surrogate modelling. They are statistically valid representations of the computational model, into which physical laws and constraints can be embedded. Physics-compliant, physics-based or physics-informed surrogate models can facilitate DTs with i) instantaneous predictions, ii) the ability to conduct uncertainty quantification and sensitivity analysis to ensure reliability, iii) online updating of model parameters based on advanced calibration routines, iv) increased trust due to explainability based on physical laws. We present herein surrogate modelling as an enabler to replace computational models predicting the runout behaviour of geophysical flows. We investigate their applicability in uncertainty quantification, global sensitivity analysis, Bayesian parameter estimation, Bayesian model selection, and optimal experimental design. We demonstrate our workflow with two open-source computational models, r.avaflow 4.0 and synxflow, with synthetic and real-world case studies.

The problem of calculating the second moment characteristics of the response of a general class of nonconservative linear distributed parameter systems with stochastically varying surface roughness, excited by a moving concentrated load, is investigated. The surface roughness is modeled as spatial Gaussian, stationary colored noise. The resulting initial/boundary value problem is transformed by eigenfunction expansion into the modal state space, where the second moment characteristics of the response are determined by direct integration using a Runge-Kutta method.

While simulation has been successful in tying design and process planning into an iterative loop, machining has traditionally been a downstream terminus of the manufacturing cycle. Simulation of machining has proven difficult due to the highly dynamic and nonlinear nature of the material removal process. Consequently, actual machining must be performed to determine the suitability of designs and process plans. The nature of the programming interface to machine tools has been an obstacle to information flow in both directions. Toward the machine tool, useful design and process information is reduced to primitive tool path motion statements. From the machine tool, no automatic data paths exist, so machinists must translate results into recommendations for design or process changes manually. The advent of STEP (ISO 10303) data for numerical control, or STEP-NC, promises to rectify these problems by providing full product and process data to the machine tool controller at run time and an automatic path back to these upstream activities using technologies such as XML. This paper describes STEP-NC and early experiences that show how these benefits can be achieved.

The Digital Twin technology of the spindle unit can realize the control of accuracy, quality of processing and other indicators that affect to the efficiency of the system. Many articles focus on Digital Twin, but there are no clear and systematic analysis of the process of creating Digital Twin. To address this gap, this study conducted an article analysis of the Digital Twin of the spindle unit (up to Maу 1, 2023). In this article, we will consider the existing definitions of the concepts of digital twin and digital twin of metalcutting machines. In addition to the articles themselves, we will consider other reviews of digital twins and attempts to systematize existing research. No review articles on the topic of digital twins of spindle assemblies were found. For the search methodology, traditional methods of searching scientific sites and search engines were used, and the analysis of the obtained results is presented in graphs. The analysis included a total of 143 selected publications. After a detailed examination of which, it turned out that most of the articles, despite the presence of keywords, did not deal with the creation of digital twins, but only mentioned the possibility of creating digital twins. Some articles have ideas for creating a digital twin model for metal cutting machinery or spindle unit. As a result of the search, no domestic works in the given direction were found, most of the works are foreign, of which the lion’s share is financed by the government of the People’s Republic of China. It is expected that this will give an impetus to the in-depth study of the process of creating a digital twin of a spindle node. It can be useful in the post-war reconstruction of Ukraine, taking into account the problems of wear and tear of existing machine tools of machine-building enterprises. In addition, it is a possibility of adapting machines that were in use, which can be obtained in within the framework of international assistance to enterprises.

In the development of robots and machine tools, in addition to conventional and serial structures, parallel mechanism-based kinematic structures have been used over a longer period of time. Aside from a number of advantages, the irregular shape and relatively small dimensions of the workspace formed by parallel mechanisms rank among the major weaknesses of their application. Accordingly, this fact has to be taken into consideration in the process of designing parallel mechanism-based robots or machine tools. This paper describes the categorization of criteria for the conceptual design of parallel mechanism-based robots or machine tools, resulting from workspace analysis as well as the procedure of their defining. Furthermore, it also presents the designing methodology that was implemented into the program for the creation of a robot or machine tool space model and the optimization of the resulting solution. For verification of the criteria and the programme suite, three common (conceptually different) mechanisms with a similar mechanical structure and kinematic characteristics were used.

Adaptive reconstruction of digital twins for machining systems: A transfer learning approach