* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

Digital Twin in Industry: State-of-the-Art

“Industrie 4.0” and Smart Manufacturing - A Review of Research Issues and Application Examples

Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today's world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future.

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The evolution, challenges, and future of knowledge representation in product design systems

A great challenge facing industry today is managing variety throughout the entire products life cycle. Drivers of products variety, its benefits, pre-requisites and associated complexity and cost are presented. Enhancing consumers’ value through variety and approaches for achieving it efficiently including modularity, commonality and differentiation are discussed. Variant-oriented manufacturing systems paradigms, as enablers of product variety, and the effective co-development of variants and their manufacturing systems to ensure economic sustainability are reviewed. Industrial applications and guidelines to achieve economy of scope with advantages of economy of scale are discussed. Perspectives and insights on future research in this field are offered.

Product Variety Management

Competitive success of manufacturing firms is by and large determined by the success of the products they introduce to the market. This is why companies continuously try to improve the efficacy of their product realization process. Product Lifecycle Management (PLM) is a business solution which aims to streamline the flow of information about the product and related processes throughout the product’s lifecycle such that the right information in the right context at the right time can be made available. Yet, few organizations are positioned to reap the true benefits of PLM. One major reason for this is a lack of clear understanding of what PLM is, its core features and functions, and its relationship to the myriad of current software tools. This paper aims to do that and also elaborates on the role of PLM as a knowledge management system.

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Product Lifecycle Management: Closing the Knowledge Loops

In this paper, two measures are proposed for valuation of size and coupling complexities of design products as abstracted by three distinct representations. The proposed size complexity measure is based on the information theoretic definition of complexity that connects the complexity of a design to the level of entropy, or uncertainty, inherent in the design product. The proposed coupling complexity measure evaluates the decomposability of the graph-based representation of design products. To validate the proposed measures, an experiment is conducted to calculate the complexities of three consumer products based on three product representations, namely, function structure, connectivity graph, and parametric associativity graph. The findings indicate that coupling and size are independent measures of a product’s complexity. Thus, it is recommended that both measures should be used. Further, the complexity of a product is not independent of the choice of representation model used to describe the product. This suggests that the complexity of a product will vary with the selected view. Finally, it is shown that the two approaches for measuring complexity of a product are generalizable and can be applied to different representations.

Engineering design complexity: an investigation of methods and measures

In today’s volatile market, manufacturing companies are increasingly adopting agile manufacturing strategies. Virtual Enterprise (VE) is one of the key enablers of the agile philosophy. Timely deployment of VE requires efficient communication between the potential members of VE through a common language. This work introduces Manufacturing Service Description Language (MSDL) as an ontology for representation of manufacturing services. MSDL provides the primitive building blocks required for description of a wide spectrum of manufacturing services. Description Logic is used as the knowledge representation formalism of MSDL in order to make it amenable to automatic reasoning.© 2006 ASME

An Upper Ontology for Manufacturing Service Description

Manufacturing Market is a market in which manufacturing process capacity is the object of trade. In a market, units of capacity, represented as manufacturing services, can be acquired as needed and when needed, thus making supply chains more responsive to fluctuations in supply and demand. Although Manufacturing Market can be built physically as a spot market, its benefits can be better realized in a web-based framework. We refer to the web-based version of Manufacturing Market as Digital Manufacturing Market (DMM). The major challenges in deployment of a virtual market for manufacturing services include standard representation of manufacturing needs and capabilities, incorporation of intelligent supplier search and evaluation mechanism, and automation of supply chain configuration process. This paper introduces DMM through its major components including a multi-agent framework, a formal ontology for representation of manufacturing services as well as a matchmaking methodology used for connecting buyers and sellers of manufacturing services based on their semantic similarities. The ultimate goal of the proposed framework is to enable autonomous deployment of manufacturing supply chains based on the specific technological requirements defined by particular work orders.

Farhad Ameri

Papers

Product Lifecycle Management: Closing the Knowledge Loops

Engineering design complexity: an investigation of methods and measures

An Upper Ontology for Manufacturing Service Description

Digital manufacturing market: a semantic web-based framework for agile supply chain deployment

Design-to-fabrication automation for the cognitive machine shop