Enriching standards-based digital thread by fusing as-designed and as-inspected data using knowledge graphs

Technological advancements have led to the transition of manufacturing industries to Smart Manufacturing and Industry 4.0. Promising concepts such as Digital Twin and Digital Thread could help speed up the transition. One benefit of using digital twins is to enable continuity of lifecycle information. However, currently, most digital-twin implementations focus on modeling a particular lifecycle stage of a physical element in “silos”. This makes companies missing out on up to 65% of possible value of digital twin investments. This also results in it challenging to incorporate diverse data streams from different lifecycle stages. Digital thread has been used to represent information flow along the product lifecycle. Using information across product lifecycle stages will facilitate interoperability and reusability of digital twins. Because data from each lifecycle stage could be accessed and managed systematically, this will ensure the value and the credibility of the digital twin. A lot of confusion still remains in industry about what are digital twin and digital thread as well as their relationships. Using lifecycle data from a digital thread for digital twin implementation is complex because of the heterogeneity of standards and technologies involved. In this paper, we provide definitions of both digital twin and digital thread; we highlight the benefit of using them for interoperability and reusability of a digital twin for product lifecycle management and analysis; we propose a methodology for implementing digital twins using lifecycle data supported by a digital thread. Finally, we showcase the proposed methodology by providing an example of integrating digital twins with a digital thread.

Quality Information Framework – Integrating Metrology Processes

ABSTRACTSubstantial progress and development in additive manufacturing (AM) technologies have been realised during the last decade but have hardly been implemented on AM systems due to old numerical solutions still used by the AM digital thread, for instance STL (1987) and G-code (ISO 6963, 1982). In this field, more traditional processes like machining have challenged this issue by adopting the Standard for the Exchange of Product model data compliant Numerical Control (STEP-NC) standard that enables advanced and intelligent manufacturing by taking advantage of the full computing performance of numerical controllers in manufacturing machines. This standard integrates the whole digital chain (CAD-CAM-CAPP-CNC-CMM) in a unique file with information on design and manufacturing and enables manufacturing of high-value products directly without numerical data conversion or post-processing. This study presents a new STEP-NC data model for AM technologies developed with the ISO TC184/SC1/WG7 committee. A STEP-NC platform initially developed for machining processes has been adapted to implement and validate the AM data model. It enables AM directly from a STEP-NC file, as well as hybrid manufacturing (AM and machining), and allows integration of several optimisation and simulation modules that extend the possibility of advanced and intelligent AM, for instance in-process manufacturing optimisation.

The "Silos of Quality" in manufacturing can be described as the proliferation of customized quality languages for different stages of production. This quality "Tower of Babel" results in excessive translations causing loss of information, reduction of features or capabilities, and/or translation errors. The Quality Information Framework (QIF) is a standard to solve the "Silos of Quality" problem in the discrete manufacturing industry. Because of the history with quality frameworks that suffered from a lack of adoption, a QIF demonstration was given at International Manufacturing Technology Show (IMTS) in 2014 to promote the fact that QIF is a suitable and excellent quality framework for manufacturers. This paper discusses modern production practices, and QIF basic concepts. It also details what QIF demonstrated at IMTS 2014.

Insufficient technologies made good integration between the die components in design, process planning, and manufacturing impossible in the past few years. Nowadays, the advanced technologies based on Standard for the Exchange of Product model data are making it possible. This article discusses the three main steps for achieving the complete process planning for prismatic parts of the die components. These three steps are data extraction, feature recognition, and process planning. The proposed computer-aided process planning system works as part of an integrated system to cover the process planning of any prismatic part die component. The system is built using Visual Basic with EWDraw system for visualizing the Standard for the Exchange of Product model data file. The system works successfully and can cover any type of sheet metal die components. The case study discussed in this article is taken from a large design of progressive die.

ABSTRACT:Variation simulation approaches are frequently used to analyse the effects of geometrical variations on the final product quality. Various software tools are used during product development as they strongly differ in their specified goals, the context of use, and users. Although a few workarounds and information-sharing strategies exist, switching software usually results in the simulation model being built from scratch, leading to redundant manual effort and uncertainties. This paper examines the potential and limitations of the Quality Information Framework (QIF) information model in improving collaborative work within a heterogeneous simulation software landscape by exchanging variation simulation model-related information in a standardised Model-Based Definition sense. An application scenario shows how QIF can bridge the gap between tools used in early and late design phases.

There is tremendous need in collaborative design today for the integration of heterogeneous CAx systems. This general practice has serious shortcomings like design discontinuity, limited parallelisation, loss of information, and conversion errors. To overcome these shortcomings, macro-parametric approach (MPA), which is a history-based method of parametric CAD model exchange, has been proposed. The macro-parametric approach is a way to achieve compatible CAD model exchange amongst a variety of mutually incompatible CAD systems. CAD models can be exchanged in the form of a macro file that comprises a sequence of modeling commands. A standard macro file that contains a set of event-driven commands can transfer the designer intent such as parameters, features and constraints. Currently, STEP (STandard for the Exchange of Product model data) as an international standard (ISO-10303, Industrial automation systems and integration - Product data representation and exchange) for the computer-interpretable representation and the exchange of product model data is widely used. STEP is becoming the 'lingua franca' in the area of modelling and exchanging product data. One remaining problem of the using STEP as a neutral format is that it does not retain parametric information such as the designer intent but only the pure boundary representation (B-rep) of the CAD model. The B-rep model without parametric information presents difficulties for engineering changes and it may also lead to an unexpected distortion of shape. This research aims to investigate an exchange method that uses STEP/XML (extensible mark-up) technology to express a set of standard modeling commands extracted from CAD models created by Autodesk InventorTM, to enable the same CAD models are readable and shareable with other CAD application systems.

Over the past two decades, our understanding of product development has changed fundamentally. Disciplines that were once clearly distinct from one another, such as mechanical engineering, electrical engineering, and software development, have now become increasingly intertwined. Products are no longer isolated objects, but rather nodes in a network of systems, data, and processes. In this environment, it is no longer enough to manage individual data sets – they must flow, remain consistent, and be available to the right people at the right time. The AI based lifecycle management system of Digital Threads (Digital Thread for short) has therefore established itself as the backbone of modern system engineering. Early approaches to product data management (PDM) focused on central storage of CAD data and the management of versions and approvals. However, with the advent of complex mechatronic systems, networked vehicles, and modular plants, it became clear that this approach was not sufficient. Companies began to realize that it was not enough to simply store geometries; they needed a system that mapped the entire lifecycle of a product, from the initial requirement through development and production to operation and decommissioning. Today, Digital Threads’ AI based lifecycle management system acts as a digital nervous system that connects disciplines such as mechanics, electronics, software, and simulation. While system engineering provides methodical approaches for structuring requirements and architectures, Digital Thread ensures that the associated data are findable, traceable, and up to date. This object oriented approach creates transparency and enables end to end traceability, which is a key requirement of modern systems engineering. Another feature of modern digital thread approaches is the seamless integration of models and product data. Changes in one discipline, for example, in design, can automatically detect and forward all affected areas such as requirements, tests, and functions. This promotes the continuous flow of information and prevents unnoticed consequences of changes elsewhere in the system. The close link between AI based lifecycle management systems for digital threads and system engineering goes beyond technical necessities and promotes cultural change in companies. Organizations must not only introduce technology, but also clearly define processes and responsibilities in order to fully exploit the effectiveness of this system. Clear, transparent handling of changes, systematic management of variants and integration of real time data from operations are key success factors for the implementation of digital threads. The concept of ”‘digital twin’, which enables a continuous connection between the models of real and virtual products, can only be realized through the consistency and transparency offered by the digital threads. This, in turn, promotes more sustainable and data driven decision making processes that can be taken into account throughout the entire product lifecycle, from development to maintenance. Overall, it is clear that AI based digital thread lifecycle management systems not only provide the technical basis for system engineering, but are also an integral element for the successful development and operation of complex, networked systems. They promote innovation, improve quality, and enable companies to make their product development more flexible and efficient.

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.

Natural language processing (NLP) is an important and challenging field of computer science. With the advent of computers and related technologies, NLP has grown out of the field of linguistics and is getting more attention in the computer field to support a wide variety of intelligent applications. NLP basically refers to automatic processing of natural languages by computers or machines in order to understand human language in written or spoken form. Ontologies are semantic models that provide general description of the types and structures of objects in a particular subject area or domain. Core components of ontology include concepts, relationships, instances, and axioms. Ontology is used to depict the hierarchical organization of concepts and their relationships. Knowledge graphs represent a set of interconnected descriptions of objects or concepts, and are sometimes referred to as semantic networks. In short, an ontology can be treated as a meta-model for data and knowledge graphs as a representation of the actual data or facts. An ontology (a formal description of relationships) plays a vital role in building a knowledge graph. Ontology and knowledge graphs have proven to be powerful tools to manage enterprise or big data. Today, a number of applications rely on ontology and knowledge graphs, such as knowledge management, information discovery, text extraction, semantic search, content-based recommendation, and intelligent chatbots. This chapter focuses on the importance of ontology and knowledge graphs for Developing NLP-based applications is difficult because computers require structured data, and human speech is highly complex, extremely diverse, unstructured in nature, and often ambiguous. In general, manual creation of knowledge graphs is expensive, therefore any automation of the process is highly desirable. Here, ontologies and knowledge graphs play a crucial role by providing a well-structured knowledge base, allowing semi-automatic creation of a lexicon to be used effectively in language translations, spellcheckers, keyword extraction and similar NLP applications.

STEP meets PREMO: Product modelling and new presentation techniques

With the goal of carbon reduction and elimination in various countries, how to manage and achieve carbon footprint quantification and greenhouse gas emission accounting has become one of the key research tasks in low carbon manufacturing of electromechanical products. In order to provide users with a deeper understanding of the carbon emissions of purchased and used electromechanical products, we propose an idea of constructing a carbon footprint label (CFL) for the life cycle of electromechanical products using a hierarchical structure tree of electromechanical products. After the bibliometric analysis of the industrial-level knowledge graph research contents in the past decade, the construction method of knowledge graph in the field of electromechanical products is clarified. Based on the constructed CFL ontology model of mechanical and electrical products, a CFL representation model of electromechanical products based on knowledge graph is established. The CFL records the carbon emission data in the life cycle of mechanical and electrical products, making the CFL a new attribute of products, and providing targeted CFL representation of electromechanical products for low carbon designers.

In the manufacturing industry, the design of machining processes plays a pivotal role in determining the quality, efficiency, and cost of product manufacturing. Machining process design is progressing towards intelligence to meet the high demands for efficiency and effectiveness in smart manufacturing. Building upon traditional computer-aided process planning, intelligent technologies, such as machine learning and knowledge graphs, have emerged as key drivers in advancing intelligent process design. To address knowledge accumulation, inflexible reuse, and fragmented reasoning in machining process design, this paper organically integrates the structured representation characteristics of the knowledge graph and the perceptual reasoning capabilities of deep reinforcement learning. It introduces an intelligent generation method for machining process design based on knowledge graph and deep reinforcement learning, aiming to achieve unified representation and reasoning reuse for historical process cases and general process rules. The effectiveness of the proposed method is validated through a case study involving the generation of machining process solutions for a specific model of diesel engine components. This research contributes valuable insights to overcoming the limitations of traditional methods and enhancing the efficiency of the machining process design.

Additive Manufacturing (AM) has huge promise for manufacturing parts with improved cost and performance compared to traditional subtractive manufacturing methods. But scale-up from prototyping operations and optimization of parts across this range of processes entails understanding complex and varied interactions between part design, materials, production processes, and part performance. To this end, significant investments are being made in R&D programs to manufacture high-performance AM parts, researching new designs and new materials. These programs generate huge amounts of data on materials, process parameters, tests, and part qualification. In parallel, many large companies are responding to competitive pressures by “digitizing” their supply chain, manufacturing processes, parts, and in-service data. Data is captured throughout the product lifecycle and analyzed for opportunities to drive down tooling costs and lead times and improve efficiencies and innovation. The phrase, “Digital Thread,” is commonly used to describe this process. AM represents an ideal opportunity to apply Digital Thread (DT) technology, since 3D printing relies on new digitally - driven technologies. Success requires that data be made available to integrated discovery, data mining, and physics-based simulation tools, enabling timely evaluation of manufactured parts and ensuring a fluid response to the in-process variability that can affect part quality. This can make the difference between parts that can be reliably reproduced to the level of required fidelity (and, hence, are certifiable), as well as reducing costs of physical testing, repair, and in-service liability. Collectively, we call this AM Informatics—the science of managing AM data across its lifecycle with full maintenance of the complex relationship between the part geometry, material, and individual processes used to create the final part. This report provides an overview of the current state of AM Informatics—how data from AM is being captured and utilized to enhance supply chain and production processes, shortening development times and enhancing the reproducibility and quality of part production in support of qualification objectives.

Smart Manufacturing advances have recently emerged, focused on the integration of Digital Thread (DTh) and Digital Twin (DT). DTh-DT has been explored in additive manufacturing, aircraft production, and weapons. However, there is still an area of opportunity to explore DTh-DT in collaborative manufacturing cells, layouts, and specifically, in the creation of a system to make an intelligent robotic cell. This work provides a novel approach of a DTh-DT developed to function within a manufacturing robotic cell. A discrete event simulation (DES) was created with open-source programming software, representing the cell. Information is collected from the physical process and sent to a cloud service. The DES request the data from the cloud is executed and the results are received by a database, which works with a Server (Ignition 8.0). The Server receives data and displays results, working as the link between the DT and the Product Lifecycle. This work presents a quick look at a DTh applied to a robotic cell, an approach to a DT, and the use of different types of communications and platforms. The potential benefits of applying this type of configuration in an Industry 4.0 environment are described and critically reviewed.