Digital Twin Driven Smart Design

Fei Tao, Ang Liu, Tianliang Hu, A.Y.C. Nee

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128189184
• eBook ISBN: 9780128189191
• Topic: Technology & Engineering
• Subtopic: Mechanical Engineering
• Index: Technology & Engineering

Digital Twin Driven Smart Design draws on the latest industry practice and research to establish a basis for the implementation of digital twin technology in product design. Coverage of relevant design theory and methodology is followed by detailed discussions of key enabling technologies that are supported by cutting-edge case studies of implementation. This groundbreaking book explores how digital twin technology can bring improvements to different kinds of product design process, including functional, lean and green. Drawing on the work of researchers at the forefront of this technology, this book is the ideal guide for anyone interested in digital manufacturing or computer-aided design. - Provides detailed case studies that explore key applications of digital twin technology in design practice - Introduces the concept of using digital twins to create the virtual commissioning of design projects - Presents a framework to help engineers incorporate digital twins into their product design process

Part 1

Theory and methodology

Outline

• Chapter 1 Digital twin driven smart product design framework
• Chapter 2 Digital twin driven conceptual design
• Chapter 3 Conceptual design driven digital twin configuration
• Chapter 4 Digital twin driven virtual verification
• Chapter 5 Digital twin driven design evaluation
• Chapter 6 Digital twin driven energy-aware green design
• Chapter 7 Digital twin enhanced Theory of Inventive Problem Solving innovative design

Chapter 1

Digital twin driven smart product design framework

Meng Zhang¹, Fangyuan Sui¹, Ang Liu², Fei Tao¹ and A.Y.C. Nee³, ¹School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, ²School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia, ³Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

Abstract

With the advent of new generation information technologies (new IT) in industry and product design, the big data–driven product design era has arrived. However, current research mainly places emphasis on the analysis of physical data rather than the virtual models. In other words, the convergence between product’s physical and virtual spaces is usually absent. Fortunately, digital twin (DT), an emerging and fast-growing technology, provides a promising way to connect and integrate the physical and virtual spaces seamlessly, thus to solve the previous problem efficiently. Therefore this chapter introduces DT into product design and presents a new design method based on the DT approach. First, related theories and technologies of product design are reviewed and the concept of DT is explored. Then a five-dimension DT and its abilities enabled by new IT are proposed. Based on this, a DT-driven smart product design (DTPD) framework is discussed, in terms of key processes and related technologies. Finally, DTPD applications in bicycle and landing gear are presented as two case studies to illustrate the proposed method.

Keywords

Digital twin; smart product design; framework; design theory; big data; product life cycle

1.1 Introduction

The era of data-driven product design is coming with the advancement of new IT. The design process is becoming more digitalized than ever before, supported by the computer-aided tools for product design, e.g., computer aided design (CAD), finite element analysis (FEA), computer aided engineering (CAE), computer aided manufacturing (CAM). In the virtual world, products are created in computers to visualize product structure, simulate product behavior, and validate product performance. In the physical world, in virtue of technologies such as Internet of Things (IoT), cloud, and artificial intelligent, products’ performance, behavior, and interaction with the users can be captured directly and analyzed in real time. However, generally, the virtual and physical products are built, analyzed, and upgraded, separated from each other, which could lead to such problems as incomplete information support, cyber-physical inconsistency, and imperfect digital mapping. Therefore the data-driven product design calls for a new framework that can effectively integrate, synchronize, and converge the increasingly “bigger” data that are related to the virtual product, the physical product, and their back-and-forth interactions [1].

Different from CAD that exclusively focuses on the digital world and IoT that heavily concentrates on the physical world, digital twin (DT) is characterized by the interaction and convergence of the digital and physical worlds, which could possibly bring many benefits [1]. On one hand, the physical product can be made more intelligent to actively adjust its behavior in real time according to the simulation by the virtual product; and on the other hand, the virtual product can be made more realistic to accurately reflect the real states of the physical product [1].

In the past, DT is mostly used for production [2], prognosis and diagnosis [3], predictive maintenance [4], etc. In this chapter, combining with the authors’ previous work [1], a systematic DT-driven smart product design (DTPD) framework coupled with related key technologies is explored from a holistic perspective, in order to guide designers to use the design theories, DT technology, and product life cycle data to facilitate the product-design process. The rest of this chapter is organized as follows. Section 1.2 reviews the development of product design and points out the prospect. Section 1.3 introduces the DT with respect to its concept, background, and applications in product design. In Section 1.4 a five-dimension DT for a product is constructed and its abilities enabled by new IT are introduced. Based on this, Section 1.5 formally presents the DTPD framework by describing the systematic processes and technologies of the design based on DT approach, which aims at helping the readers gain a preliminary understanding of the design process driven by DT. Each process or technology will be discussed in detail in the following Chapters 2–12. Section 1.6 presents case studies of design for the bicycle and landing gear to showcase the possible applications of DTPD in practice. Section 1.7 draws conclusions and outlines future works.

1.2 Development of product design and prospect forecast

1.2.1 Traditional design methods and technologies

Design methods have been developed for years. They play a significant role in different stages of product design, including task clarification, conceptual design, embodiment design, detail design, and virtual verification, and they can also guide the designs for factory, process planning, control logistics, etc. Some traditional design methods and technologies are introduced briefly in this section.

Theory of inventive problem solving (TRIZ) is one of the most well-known problem-solving methods for the conceptual design, which is the most significant and creative phase [5–8]. The design thinking behind TRIZ is highly analogous to that behind the big data analytics, as they intend to uncover the hidden patterns, general principles, and unknown correlations, under the surface of an enormous amount of data [1]. The TRIZ matrix that involves recognized pairs of contradictions was manually created by Altshuller based on his personal interpretation and abstraction of hundreds of thousands of patents [1,8]. The matrix can be used to remove conflicts between parameters (e.g., volume, weight, speed, and energy) in a system.

Virtual prototyping refers to the process of constructing and testing a realistic and interactive virtual prototype, that is, a 3D digital representation that can perform various types of simulations iteratively about product performances [9–11]. An initial version of a product can be provided to designers and users via the virtual prototype to find out potential flaws, and then it would be revised and verified iteratively in the digital space until finally reaching t...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Preface
• Part 1: Theory and methodology
• Part 2: Application and case study
• Index