Search

The industry has embraced digitalisation leading to a greater reliance on models derived from data. Understanding and getting insights into the models generated by machine learning algorithms is a challenge due to their non-explicit nature. Explainable artificial intelligence (XAI) is to enhance understanding of the digital models and confidence in the results they produce. The paper makes two contributions. First, the XRule algorithm proposed in the paper generates explicit rules meeting user's preferences. A user may control the nature of the rules generated by the XRule algorithm, e.g. degree of redundancy among the rules. Second, in analogy to federated learning, the concept of federated explainable artificial intelligence (fXAI) is proposed. Besides providing insights into the models built from data and explaining the predicted decisions, the fXAI provides additional value. The user-centric knowledge generated in support of fXAI may lead to discovery of previously unknown parameters and subsequently models that may benefit the non-explicit and explicit perspectives. The insights from fXAI could translate into new ways of modelling the phenomena of interest. A numerical example and three industrial applications illustrate the concepts presented in the paper. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

Globalisation of the manufacturing and service industry has increased complexity of the flow of materials and goods, magnified dependency on the underlying network, and made the industry more vulnerable to the changing market conditions. As manufacturing and service industry undergo transformation, an opportunity to rethink the design of future enterprises has emerged. Six enablers of miniversal manufacturing are discussed: digitisation, open manufacturing, service orientation, shared manufacturing, sustainability, and resilience. These enablers will form properties of universal manufacturing, with adaptability and affinity that are emerging. A universal manufacturing enterprise will be formed based on the distributed manufacturing facilities. The emerging standards for interoperability of systems needed for universal enterprises are discussed. The data and modelling standards will enable the synthesis of digital models into universal enterprises. Though there is no global standard for the representation of digital manufacturing models in a cloud, the existing process modelling methodologies and languages may offer the solutions needed. The evolution of production systems is illustrated with three snapshots, dedicated manufacturing, distributed manufacturing, and universal manufacturing. The modelling approach followed in this paper is bottom-up rather than top-down followed in the literature on modern manufacturing. [ABSTRACT FROM AUTHOR]

The transformation of the manufacturing industry over the last two decades has been largely inspired by data. Digitisation has made its mark in different areas of manufacturing, from digital materials and processes to data science in decision-making. The digital manufacturing is evolving towards universal manufacturing that is highlighted in this paper. Different manufacturing initiatives are benchmarked and their relationship to universal manufacturing is demonstrated. The representation of enterprises in the universal manufacturing cloud is discussed. Product- and process-based specifications of digital enterprises are defined. Using these specifications, the enterprise configuration algorithm proposed in the paper is applied for the selection of component models. Two different representations, a node–node and an input–output matrix, of the digital component models are considered. The extended topological sorting algorithm is applied to construct an integrated digital model. [ABSTRACT FROM AUTHOR]

Functionally graded materials (FGMs) have been used in many different kinds of applications in recent years and have attracted significant research attention. However, we do not yet have a commonly accepted way of representing the various aspects of FGMs. Lack of standardised vocabulary creates obstacles to the extraction of useful information relating to pertinent aspects of different applications. A standard resource is needed for describing various elements of FGMs, including existing applications, manufacturing techniques, and material characteristics. This motivated the creation of the FGM Ontology (FGMO) in 2016. Here, we present a revised and expanded version of the FGM Ontology, which includes enrichments along four dimensions: (1) documenting recent FGMs applications; (2) reorganising the framework to incorporate an updated representation of types of manufacturing processes; (3) enriching the axioms of the ontology; and (4) importing mid-level ontologies from the Common Core Ontologies (CCO) and Product Life Cycle (PLC) Ontologies. The work is being carried out within the framework of the Industry Ontology Foundry (IOF), and the ontology is conformant to Basic Formal Ontology (BFO). [ABSTRACT FROM AUTHOR]

With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remain a challenge. This paper presents a Digital Twin reference model for rotating machinery fault diagnosis. The requirements for constructing the Digital Twin model are discussed, and a model updating scheme based on parameter sensitivity analysis is proposed to enhance the model adaptability. Experimental data are collected from a rotor system that emulates an unbalance fault and its progression. The data are then input to a Digital Twin model of the rotor system to investigate its ability of unbalance quantification and localisation for fault diagnosis. The results show that the constructed Digital Twin rotor model enables accurate diagnosis and adaptive degradation analysis. [ABSTRACT FROM AUTHOR]

The growing complexity in product design and manufacturing processes has made virtual prototyping an important new approach that enhances products and process development. Considering the ergonomic issues evident in product lifecycles, digital human modelling (DHM) is adopted for virtual simulation and proactive evaluation. A motion generation from semantics (MGS) system is proposed in this research. The MGS system features include virtual prototyping, natural language instruction, a method time measurement (MTM) translator and motion generator. The system was implemented using product lifecycle management (PLM) software and validated in an automotive manufacturing company. The practice of intuitively generating manual operations and conducting virtual simulations enables the system planners to quickly respond to manufacturing process changes and recursively improve the tooling and process design flexibility and efficiency. [ABSTRACT FROM PUBLISHER]

Small and medium sized enterprises (SMEs) need to find ways of maintaining their competitive advantages of high customization, short lead time and high quality and overcoming high production cost because of growing competition from large global mass producers. To maintain and enhance these advantages and in the meantime to keep the production efficiency and reduce production cost requires SMEs to move to mass customization which is supported by various computer aided engineering, manufacture and management technologies or digital manufacturing technologies, such as computer aided design (CAD), process planning (CAPP), and manufacturing (CAM). However, for efficiently producing one-of-a-kind products by using one-piece flow production methods, accurate and timely information is required in large quantities which need to be processed by an information system. This paper presents the development of such an information system, which enables a medium-sized window and door manufacturer in Calgary, Canada to mass produce customised windows and doors using one-piece flow production methods. This industrial application demonstrates that an information system is a key technology for an SME to realize mass customization in one-of-a-kind production. [ABSTRACT FROM AUTHOR]

Digital manufacturing relies on geometric modelling techniques, virtual environments, and a solid common platform that can support multiple disciplines and applications. Subdivision methods offer powerful computational representations and algorithms for digital geometry processing (DGP) and can create a common model and unified representation for geometric design, numerical analysis, and numerical control (NC) machining. Subdivision-based digital geometry processing can be a fundamental building block of digital manufacturing. In this paper, models for geometric design, finite/boundary element analysis (FEA/BEA), and NC machining are created using subdivision surfaces. Automatic mesh generation for the FEA of sheet metal structures and for the BEA of general three-dimensional structures is fulfilled through subdivision-based digital geometry processing. Models with progressive refinement for progressive transmission are created. Subdivision-based digital geometry processing for NC machining is discussed. [ABSTRACT FROM AUTHOR]