Building a Cad-Native Digital Twin for Ndt and Plm: Workflow, Tools, and Case Study

Over the last few years, a concept called Digital Twin has evolved rapidly as a new key approach in the field of Product Lifecycle Management (PLM). Briefly, a Digital Twin is a digital representation of an active unique product or unique product-service-system with its selected characteristics within dedicated lifecycle phases. This concept has experienced a tremendous impact by IoT technology, which has drastically reduced the costs. It builds the foundation not only for connected products and services but also for entirely new offerings and business models. Three main characteristics of Digital Twin were identified: representation of a physical system, bidirectional data exchange, and the connection along the entire lifecycle. For a better understanding, three subtypes of Digital Twin are presented, namely: The Digital Master, the Digital Manufacturing Twin, and the Digital Instance Twin which refer to the different phases of the product lifecycle: design, production and operation. Therefore, this chapter formulates a consistent and detailed definition of Digital Twins and gives insight in dedicated research direction. Finally, based on the Digital Twin characteristics, an approach for generation of Digital Twin in manufacturing is shown.

In today’s increasingly dynamic business landscape, industries are faced with global headwinds, intensified competition, and increased volatility driven by factors such as increased demand for tailored products and solutions as well as geopolitical, economic, and climate disruptions. To address these challenges and remain competitive, the product lifecycle management (PLM) paradigm, which allows companies to be in control of their products and services across the lifecycle, has gained attention from industries. This cross-functional approach, enabled by Industry 4.0 information and communication technology (ICT) enablers such as digital twins (DT) and knowledge graphs (KGs), can elevate existing capabilities for customizability and manufacturing resilience. This thesis explores the role of DT and KG in enhancing PLM capabilities, integrating multiple lifecycle phases with emphasis on product design, manufacturing, and supply chain management (SCM). The adoption of these transformative technologies has the potential to enhance existing mass customization strategies such as the product family (PF) approach and Smart Product-Service Systems (Smart PSS), paving the way for intelligent decision support systems. Applicable to a wide range of industries, the use of DT systems with KG as a computational driver can support stakeholders in improving operational efficiencies, and responsiveness for disruption management. The main contributions of the thesis can be categorized into five research areas highlighted below: 1) Establishing an environment-based context-aware DT system to enhance PF design and optimization. To overcome the challenges of identifying optimal product configurations in the planning phase and overcoming user requirement changes in the usage phase, a context-aware DT system incorporating real-world environment aspects is proposed to aid PF reconfiguration and redesign. A novel benchmark and interacting mechanism automatically identifies feasible PF modules, reducing the need for costly and bias-prone expert recommendations, as demonstrated by a case study on tower crane planning and deployment. 2) Developing DT-as-a-Service to advance service-oriented digital manufacturing. Based on the flexibility and versatility of DT in manufacturing processes, these systems are pivotal towards customized production systems. A four-tier technology stack is proposed to construct DT systems using a modular approach and leverages advanced computational methodologies and tools such as extended reality (XR) to support a wider range of shop floor manufacturing processes. Here, DT systems are used as a fundamental to enable Smart PSS and circular economy paradigms to drive sustainability and customizability efforts. These multi-faceted capabilities are featured in two use cases, additive manufacturing, and gearbox assembly line. 3) Enabling causal inference for maintenance operations using cognitive digital twins (CDTs). The intricate nature of large production systems results in challenges pertaining to the root cause identification of product defects. As DT assets typically operate independently and lack cross-domain knowledge-sharing functionalities, a CDT can integrate multiple DT assets and different shop floor processes to generate optimal solutions. Leveraging industrial knowledge graphs (iKGs) for reasoning, feasible solutions can be generated to holistically enhance shop floor productivity. A print packaging manufacturing line is featured to demonstrate its capability. 4) Designing a hybrid supply and production (S&P) DT system for disruption management in multi-echelon networks. The reliance of many companies on multi-echelon supply networks to optimize and streamline downstream distribution operations emphasizes the pivotal role of hybrid S&P facilities. However, these bottleneck facilities are typically vulnerable to demand fluctuations, and existing DT systems usually operate in isolated domains without consideration of SC and manufacturing aspects. Through the proposed S&P DT system, demand disruptions can be mitigated through resilience evaluation, SC replanning, and shop floor rescheduling functionalities, and is demonstrated in a consumer packaged goods (CPG) industrial case study. 5) Building a graph embedding-based mitigation decision support system (GEM-DSS) for manufacturing resilience. As SC disruptions present risks to business operations and end-user satisfaction, manufacturing resilience is a core factor in Make-to-Order (MTO) strategies. Here, two challenges are identified as knowledge incompleteness and the need for automatic solution recommendation. To tackle these issues, an attention-based graph consistently-attributed graph embedding (ACAGE) model is designed to predict missing data relations via link prediction to increase database accuracy. Two computational pipelines are also developed to derive feasible mitigation strategies for alternative supplier selection and material substitution, and the model's effectiveness is rigorously tested in an automotive case study. The significance of the research lies in the development of novel DT technologies to facilitate customizability paradigms across PLM phases, and the exploratory research of integrating iKGs in DT systems for improved decision-making. The research is organized into eight chapters, supported by six industrial case studies to highlight its practicability. Through the valuable insights derived from the findings, the author hopes that the research will offer useful guidance to both industry and academia towards implementing mass customization strategies

Croyances et faits, le monde de la kinésithérapie manifeste ses émotions

Masseur-kinésithérapeute, un titre à rafraîchir ?

La technologie en mouvement

Is Human Digital Twin possible?

The new industrial revolution called Industry 4.0 embraces diverse Digital Manufacturing Tools (DMT). Trying to improve Product Life-cycle Management (PLM), some companies are trying to implement DMT to create Digital Twins (DT). New Product Introduction (NPI) demands large effort in digitalization and virtual simulation through the PLM process. Sometimes a new product could be only the development of a single component and/or an entire manufacturing process including complex instruments and controls. In this direction, it is important to accelerate Manufacturing Integrated Systems (MIS) by improving the automation not only in the NPI, but also in the PLM. This paper integrates Siemens PLM software as DMT called Tecnomatix Process Simulate (TPS), Totally Integrated Automation (TIA) and PLCSIM advanced. The scope in the NPI is showing how a Digital Twin could help the MIS. The aim of this paper is to evaluate the interconnectivity of a small physical prototype with its virtually simulated clone to support the virtual commissioning for a NPI.

Digital twins, as part of Industry 4.0, are critical for advanced smart manufacturing processes, including machining. Sensor systems in smart manufacturing allow for real-time tracking of all changes in the machining process as well as simulation of an object’s behavior in the real world. It can also intervene and correct any defects that may arise during the machining process. The current review covers basic concepts for machining processes for the first time in detail, including Big Data, the Internet of Things, product lifecycle management, continuous acquisition and lifecycle support, machine learning, digital twin prototypes, digital twin instances, digital twin aggregates, and digital twin environments. The review article examines digital twins for the most common machining processes, such as turning, milling, drilling, and grinding. This review also highlights the benefits and drawbacks, as well as the prospects for using digital twins in smart manufacturing.

The digital twin is an emergent technology that seeks to integrate digital representation with its respective physical artifact.In academia, because it is a recent technology, there is still a gap in relation to the most appropriate integration models for digital twin.Currently, there are already consolidated models for product lifecycle management.However, there is a dearth of reference model for integration between both aspects -lifecycle management and digital twin.Thus, aiming to fill this gap, this research aims to integrate the concepts of digital twin and management of the product lifecycle through a digital twin reference model in product lifecycle.Based on the generated model, the present study also seeks to map the functionalities of the digital twin.A multimethod strategy was used, composed of a systematic literature review and a bibliometric study that included three literature reviews -product lifecycle management (PLM), digital twin (DT) and the integration of product lifecycle management and digital twin.The three reviews were conducted through the Scopus and Web of Knowledge databases.The result of this research is a contribution to the product development process, generated from the integration between lifecycle management and the digital twin and the mapping of digital twin functionalities.

With the rapid development of modern information and communication technologies as well as infrastructure, the digital twin approach becomes increasingly popular and widely used throughout the industry and research. The digital twin is considered to be the key technology to realize the comprehensive digital description of components, products and systems including the information from all lifecycle phases. The Product Lifecycle Management (PLM) strategy has been present in the industry for many years and is considered as the most effective way of managing the components, products and systems of a company all the way across their lifecycles from the first idea of the product to its disposal. The digital twin is not yet clearly defined. It can be defined as a set of models, linked with each other as well as with the physical product enabling data storage and real-time processing. In contrast to a digital twin the PLM strategy provides a framework, which serves as single source of truth connecting the partial models, that describe the physical product. The models can receive the data stored in a product data management system (PDM).

In today's world, digital transformation is becoming increasingly crucial in industry. The Digital Twins and simulations become much more important with growing complexity of products, the need to accelerate product development processes while reducing costs. The increasing value of Digital Twins is prompting organizations in the aviation industry to actively utilize them in research and development efforts. However, due the idea of difficulties of using Digital Twins in the industries, product developers don’t have a clear idea where to begin. This article provides readers with a general overview of the current situation by offering an examination of the aviation sector. Initially, the concept of Digital Twin, its evolution, and relevant studies are explored, followed by an examination of the impact of creating Digital Twins for companies operating in aviation on the product development process. In this study, unlike other published works, the concept of Digital Twin is explained under three subheadings. These include the Design Digital Twin for design steps, the Production Digital Twin for manufacturing processes, and the Performance Digital Twin for post-delivery work related to the product. The final section of the study evaluates the contribution of Product Lifecycle Management (PLM) systems to the process of creating Digital Twins and the advantages they provide to companies in product development processes. The present research work does not contain any studies performed on animals/humans subjects by any of the authors.

Towards a Process Model for Digital Twin Implementation: The Implementation Canvas

In recent years, several IT systems have been applied to collect different types of data concerning the full product lifecycle. As a result of Industry 4.0 developments, the amount of product information collected over the entire lifecycle has been growing. Information and communication technology is employed to digitally mirror the lifecycle of a corresponding physical product in Digital Twin applications. These applications are middleware architectures that apply physical world information to support real-time decisions. Therefore, a Digital Twin may be used to enhance simulation, to improve traceability, and to expand the value-added services offering along the lifecycle. However, studies on Digital Twin applications are mainly focused on the beginning of life (BOL) or manufacturing optimization. In this paper, Product Lifecycle Management (PLM) theory and Internet of Things (IoT) solutions and technologies are applied to build a Digital Twin able to collect and cast middle of life (MOL) information to the other lifecycle phases. Based on the scenario implementation in a Learning Factory, the objective is to discuss the information flow that is required for a Digital Twin to be considered for closing the information gap between the product in the use phase and other lifecycle phases. The results show how a middle of life Digital Twin can impact processes and information flow. Future research work should integrate the information of multiple IT systems from the entire product lifecycle in a comprehensive Digital Twin.

Objective of the paper is to describe and present results of using a "Digital Twin" in Drilling Operations (Planning and Engineering, Training and Operational Support) in the last 10 years for Operators worldwide. The concept of Digital Twin was first introduced by Michael Grieves at the University of Michigan in 2003 through Grieves’ Executive Course on Product Lifecycle Management. Winning a Formula 1 race is no longer just about building the fastest car, hiring the bravest driver and praying for luck. These days, when a McLaren technology group races in Monaco or Singapore, it beams data from hundreds of sensors wired in the car to Woking, England. There, analysts study that data and use complex computer models to relay optimal race strategies back to the driver. The McLaren race crew and the online retailers both harness data and use algorithms to make reasonable projections about the future, Parris explains. The concept is called Digital Twin [1]. A Digital Twin contains information such as a piece of equipment or asset, including its physical description, instrumentation, data and history. A Digital Twin can be created for assets ranging from a well to a piece of equipment to an entire oilfield. For example, a subsea system could have a Digital Twin via a simulation model of a subsea system's components, including the blowout preventer, tiebacks, risers, manifolds, umbilical and moorings. Drilling and extracting simulations can determine whether virtual designs can actually be built using the machines available," GE said. "Last but not least, real-time data feeds from sensors in a physical operating asset are now used to know the exact state and condition of an operating-asset product, no matter where it is in the world"[2].

The industry is undergoing a transition into efficient technologies and it has digitalization written all over it. Digitalization not only should be about data, a fancy software, touchscreens and the internet, it is important that solutions are able to connect within existing work processes and with people for companies to truly lead to more efficient and safer drilling operations. Oil and gas industries are now moving towards using Digital Twin's during the life-cycle of well construction. The concept of Digital Twins was first introduced by Dr. Michael Grieves at the University of Michigan in 2002 through Grieves’ Executive Course on Product Lifecycle Management. Digital Twin is a digital copy of the physical systems and act as a connection between physics and digital world. The digital system gets the real-time data from the mechanical systems which include all functionality and operational status of the physical system. An example from another industry; A Formula 1 team uses data from many sensors used in the car, harnessing data and using algorithms to make projections about what's ahead, and apply complex computer models to relay optimal race strategies back to the driver. Ultimately, to drive faster and safer. By means of the digital twin of the drilling wells during the life cycle of the drilling by combining digital and real-time data together with predictive diagnostic messages there is seen a lot of advantageous in the improvement of accuracy in decision making and results. This again will help the industry to increase safety, improve efficiency and gain the best economic-value-based decision. A Digital Twin driven by real-time data helps to give operations the optimal plan with focus on safety, risk reduction and improved performance. In this paper, the concept will first be explained in creating and utilizing a Digital Twin of your well for drilling and how it will directly influence how Drilling/well engineers, managers and supervisors plan, prepare and monitor their drilling operations and then implement learnings on future wells; for faster and improved decision making with direct relation to predicting and avoiding/mitigating NPT while also optimizing operations along with it. Case examples will be shared, showing value from use of the Digital Twin from first introduced in 2008 up until now where operators around the globe have implemented it for multiple uses in the drilling lifecycle.