Data-Driven Engineering Design

✍ Scribed by Ang Liu, Yuchen Wang, Xingzhi Wang

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 203
• Category: Library

✦ Synopsis

This book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design.

Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation.

Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.

✦ Table of Contents

Contents
1 Data-Driven Engineering Design
1 Introduction to Data-Driven Design
2 Design Theory and Methodology (DTM)
2.1 Historical Reflection of DTM
2.2 Design Operations
2.3 Data in Design Theory and Methodology
3 Emergence of Data Science
3.1 Background of Data Science
3.2 Scientific Dimension of Data Science
3.3 Data Dimension of Data Science
4 New Paradigm of Data-Driven Engineering Design
4.1 Definition of Data-Driven Engineering Design
4.2 Human-Machine Collaboration in Data-Driven Design
4.3 Suitability of Data-Driven Design
5 Conclusion and How to Read This Book
References
2 User-Generated Content Analysis for Customer Needs Elicitation
1 Introduction
2 User-Generated Content (UGC)
2.1 Introduction to UGC
2.2 UGC Sources for Designers
3 Enabling Technologies for UGC Analysis
3.1 Natural Language Processing (NLP)
3.2 Computer Vision (CV)
3.3 Social Network Analysis (SNA)
4 UGC Analysis Framework
4.1 Data Collection and Preparation Module
4.2 Feature and Context Extraction Module
4.3 Satisfaction Analysis Module
4.4 Context Detection Module
4.5 Importance Rating Module
4.6 Information Fusion Module
4.7 UGC Analysis Application
5 Case Study
6 Conclusion and Future Work
References
3 Data-Driven Conceptual Design
1 Introduction
2 Overview of Conceptual Design
2.1 Factors of Conceptual Design
2.2 Process of Conceptual Design
2.3 Challenges of Conventional Conceptual Design
3 Data-Driven Conceptual Design
3.1 Services of Data in Conceptual Design
3.2 Important Data for Conceptual Design and Their Collections
3.3 Data-Driven Functional Design
3.4 Data-Driven Concept Generation
3.5 Data-Driven Concept Evaluation
3.6 Data-Driven Affordance-Based Design
4 Case Study
4.1 Data for the Conceptual Design of Robot Vacuum Cleaner
4.2 Data-Driven Functional Design of a Robot Vacuum Cleaner
4.3 Data-Driven Concept Generation of Robot Vacuum Cleaner
4.4 Data-Driven Concept Evaluation of Robot Vacuum Cleaner
4.5 Data-Driven Affordance-Based Design of Robot Vacuum Cleaner
5 Conclusion
References
4 Management of Constraints, Complexities, and Contradictions in the Data Era
1 Introduction
2 Theory Backgrounds of Constraints, Complexities, and Contradictions
2.1 Introduction to Constraints
2.2 Introduction to Complexities
2.3 Introduction to Contradictions
3 Importance of Data for Managing Constraints, Complexities, and Contradictions
3.1 Challenges of Concept Improvements
3.2 Roles of Data in Concept Improvements
4 Data-Driven Concept Improvements
4.1 Data-Driven Constraint Management
4.2 Data-Driven Complexity Management
4.3 Data-Driven Contradiction Management
5 Case Study
5.1 Data-Driven Constraint Management of Robot Vacuum Cleaner
5.2 Data-Driven Complexity Management of Robot Vacuum Cleaner
5.3 Data-Driven Contradiction Management of Robot Vacuum Cleaner
6 Conclusion
References
5 Blockchain-Based Data-Driven Smart Customization
1 Introduction
2 Overview of Customization
3 Overview of Blockchain Technology
3.1 Blockchain and Key Capabilities
3.2 Blockchain Suitability Evaluation Framework for Customization
4 Blockchain-Based Smart Customization Framework
4.1 Customization Module
4.2 Data Driver Module
4.3 Blockchain Module
4.4 Systematic Process of Framework
5 Applications of Blockchain in Smart Customization
6 Case Study: Customization of Smart Vehicle's Merging Module
7 Conclusion
References
6 Data-Driven Design of Smart Product
1 Introduction to Smart Product Design
2 Characteristics of Smart Product
2.1 Context-Smartness of Smart Product
2.2 Network-Smartness of Smart Product
2.3 Service-Smartness of Smart Product
3 Data-Driven Design of Smart Product
3.1 Theoretical Foundation of FBS Ontology
3.2 Process of Data-Driven Smart Product Design
4 Conclusion
References
7 Data-Driven Smart Product Service System
1 Introduction
2 Related Theories
2.1 Product-Service Systems (PSS)
2.2 Smart Products (SP)
3 Data Sources for SPSS Design
3.1 User-Generated Content (UGC)
3.2 Product-Sensed Data
3.3 Internet Information Data
4 Enabling Technologies for SPSS Design
4.1 Big Data Analytics
4.2 Recommender Systems
4.3 Autonomous Systems
5 Data-Driven SPSS Framework
5.1 Data-Driver Module
5.2 Context Detection Module
5.3 Recommendation Module
5.4 Adaptation Module
5.5 Evaluation Module
6 Case Study: Data-Driven SPSS for Robot Vacuum Cleaners (RVC)
7 Discussion and Conclusion
References
8 Digital Twin for Data-Driven Engineering Design
1 Introduction
2 Introduction to Digital Twin
2.1 Compositions of Digital Twin
2.2 Enabling Technologies of Digital Twin
3 Challenges of Data-Driven Engineering Design and the Importance of Digital Twin Technology
3.1 Challenges of Data-Driven Engineering Design
3.2 The Case for Digital Twin
4 Digital Twin for Data-Driven Engineering Design
4.1 Real-Time Monitoring and Data Collection in the Physical World
4.2 Identifications, Diagnoses, and Predictions of Product Statuses
4.3 Enhancement of Human-Machine Interactions via the Virtual Entity
5 Case Study
5.1 The Development of Digital Twin for the Robot Vacuum Cleaner
5.2 Real-Time Monitoring and Data Collection for the Robot Vacuum Cleaner
5.3 Identification, Diagnosis, and Prediction of a Robot Vacuum Cleaner’s Performance
5.4 Enhancement of Human-Machine Interactions via the Virtual Entity of Robot Vacuum Cleaner
6 Conclusion
References
9 Enabling Technologies of Data-Driven Engineering Design
1 Introduction
2 Enabling Technologies of Data Collection and Transmission
2.1 Sensing Technologies
2.2 Internet and Mobile Internet
2.3 Internet of Things (IoT) and Industrial Internet of Things (IIoT)
3 Enabling Technologies of Data Storage and Computation
3.1 Cloud Computing
3.2 Edge Computing
3.3 Blockchain
4 How to Analyse and Understand Data
4.1 Machine Learning
4.2 Artificial Intelligence
4.3 Big Data Analytics
5 Integration of Cyber-Physical Data
5.1 Virtual Reality
5.2 Digital Twin
5.3 Wearable Devices
6 Integration of Enabling Technologies
7 Illustrative Examples of Practical Applications
8 Conclusion
References