Mechatronics for Complex Products and Systems
Description
A project-based approach to designing mechatronic systems with new and emerging technologies
In Mechatronics for Complex Products and Systems: Project-Based Designs for Cyber-Physical Systems, Digital Twins, and Other Emerging Technologies, distinguished researcher Dr. Zhuming Bi delivers an expert discussion of real-world mechatronics skills that students will need in their engineering careers.
The book explains the characteristics and innovation principles underlying mechatronic systems, including modularization, adaptability, predictability, sustainability, and concurrent engineering. A mechatronic system is decomposed into a set of mechatronic functional modules such as power systems, actuating systems, sensing systems, systems of signal conditioning and processing, and control systems.
The author also offers:
- A thorough introduction from classic integration of mechanical, electronic and electrical systems to more complex products and systems, including cyber-physical systems, robotics, human-robot interactions, digital twins, and Internet of Things applications
- Insightful project assignments that help reinforce a practical understanding of a learning subject
- Practical discussions of real-world engineering problems
- Comprehensive guidance on how to select the right type of sensors, motors, and controllers for a variety of mechatronic functional modules
Perfect for advanced undergraduate and graduate students of mechatronics, Mechatronics for Complex Products and Systems will also benefit professional engineers working on interdisciplinary projects enabled by digital technologies, Internet of Things (IoT), and Artificial Intelligence (AI).
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Zhuming Bi, PhD, is a Professor of Mechanical Engineering in the Department of Civil and Mechanical Engineering and Harris Chair in Wireless Communication and Applied Research at Purdue University Fort Wayne. He was a 2023-2024 Fulbright-Nokia Distinguished Chair in Information Communications Technologies (ICT). He has previously held positions at the Nanjing University of Science and Technology (China), Northern Ireland Technology Center (UK), the National Research Council of Canada (Canada), National Institute of Standards and Technology (USA), and Lappeenranta University of Technology (Finland).
Content
Preface xvii
About the Companion Website xix
1 Introduction 1
1.1 Introduction 1
1.2 Growing Complexity of Engineering Designs 1
1.2.1 Products 3
1.2.2 Manufacturing Technologies 5
1.2.3 Business Environments 6
1.2.4 Engineering Design 6
1.3 Integrated Engineering Design 7
1.4 Mechatronics for Multi- or Interdisciplinary Designs 9
1.5 Mechatronic Design Examples 11
1.5.1 Development of Football Robot Team 11
1.5.2 Reusing Robots to Unload Heat Sinks Automatically 12
1.5.3 Rebuilding Rail Test Machine 14
1.5.4 Testing of Electric Hardness 16
1.5.5 Valve Needle Assembly Station 16
1.5.6 Ejecting Engine Fans from Performance Tester 18
1.5.7 Demonstrator of Automated Spacer Removals in Truck Assembly Line 19
1.6 Group Technologies (GTs) for Mechatronic Designs 21
1.7 Mechatronics and Mechatronic Functional Modules (MFMs) 22
1.8 Mechatronic Design Methodologies 24
1.9 Organization of the Book 25
1.10 Summary 26
Problems 28
References 28
2 Mechatronic Designs - Innovations, Theories, and Methods 31
2.1 Innovative Thinking 31
2.2 Theory of Inventive Problem-Solving (TRIZ) as Tactic Methodology 34
2.3 Innovations of Mechatronic Systems 39
2.3.1 Modularization 39
2.3.2 Integrability 41
2.3.3 Coupled Discipline Modeling 42
2.3.4 Concurrent Design 43
2.3.5 Decentralized Controls 45
2.3.6 Event-Driven Automation 46
2.3.7 Adaptability and Re-configurability 46
2.3.8 Predictability 48
2.3.9 System Resilience 49
2.3.10 Continuous Adaptation (CA) 50
2.4 Architecture of Mechatronic Systems 51
2.5 Design of Mechatronic Systems 54
2.6 Mechatronic Design Methodologies 57
2.6.1 System Modeling Language (SysML) 58
2.6.2 Model-Based System Engineering (MBSE) 59
2.6.3 Axiomatic Design Theory (ADT) 61
2.6.4 Concurrent Design Optimization (CDO) 63
2.6.5 Virtual Verification and Validation (VVV) 65
2.7 Project-Based Mechatronic Design (PBMD) 65
2.7.1 Existing Assistive Evacuating Technologies 66
2.7.2 Proposed Assistive Evacuation Device 69
2.7.3 Main Functional Requirements from Use Cases 69
2.7.4 Project-Based Mechatronic Designs 72
2.7.4.1 Folding and Unfolding Mechanism 72
2.7.4.2 Reaction Forces on Tracks for Structural Elements 72
2.7.4.3 Motor for Lifting Mechanism 74
2.7.4.4 Control of Evacuation Device 76
2.7.4.5 PBMD in Mechatronic Design 77
2.8 Summary 77
Problems 78
References 81
3 Power Generation, Storage, Supply and Transmission 87
3.1 Introduction 87
3.2 Energy, Work, and Power 87
3.3 Energy Source 90
3.4 Driving Components - Functional Requirements (FRs) 91
3.5 Power Transmission 93
3.5.1 Functional Requirements (FRs) 94
3.5.2 Machine Elements for Power Transmission 95
3.5.3 Types of Machine Elements 95
3.5.4 Procedure in Designing or Selecting Machine Elements 95
3.5.5 Machine Elements in Mechatronic Systems 98
3.5.6 Mechanical Power Transmission Examples 98
3.6 Power Generation 100
3.6.1 Internal Combustion (IC) Generator 102
3.6.2 Solar Power Generator 103
3.6.3 Wind Turbine Generator 105
3.6.4 Geothermal Generator 105
3.6.5 Other Generators 106
3.6.6 Selection of Power Source for Mechatronic System 107
3.7 Requirements of Power Supplies and Storages 109
3.7.1 Requirements of Power Supplies 109
3.7.2 Classification of Energy Storage Systems 111
3.7.3 Flywheel Energy Storage System (FESS) 112
3.7.4 Pumped Hydro Energy Storage (PHES) 114
3.7.5 Compressed Air Energy Storage (CAES) 115
3.7.6 Gravity Energy Storage (GES) 115
3.7.7 Electrical Energy Storage (EES) 116
3.7.8 Thermal Energy Storage (TES) 118
3.7.9 Comparison of Different Energy Storages 120
3.8 Selection of Power Supplies 122
3.9 Summary 122
Problems 122
References 123
4 Actuating Systems 127
4.1 Introduction 127
4.2 Functional Requirements (FRs) 129
4.3 Design Variables (DVs) 132
4.4 Basics of Energy Conversion 135
4.4.1 Mechanical Energy Conversion 135
4.4.2 Electromechanical Energy Conversion 140
4.4.3 Thermomechanical Energy Conversion 148
4.4.4 Electro-stimulated Materials 149
4.4.5 Magneto-rheological Fluid Energy Conversion 151
4.4.6 Nano-level Energy Conversion 152
4.5 Main Components 153
4.6 Valve and Electric Actuators 154
4.6.1 Valve Actuators 155
4.6.2 Electric Actuators and Motors 157
4.6.3 Selection of Motors 160
4.7 Summary 161
Problems 161
References 162
5 Sensing Systems 165
5.1 Introduction 165
5.2 Sensors, Actuators, and Transducers 169
5.3 Classifications 170
5.3.1 Types of Quantities to be Measured 170
5.3.2 Requirements Related to Measurement 171
5.3.3 Specifications Related to Measurement 171
5.4 Working Principles 173
5.4.1 Hooke's Law 173
5.4.2 Ohm's Law 175
5.4.3 Photoconductivity 176
5.4.4 Hall Effect 177
5.4.5 Faraday's Law of Induction 178
5.4.6 Curie-Weiss Law 179
5.4.7 Time of Flight (ToF) 181
5.5 Types of Physical Quantities 182
5.5.1 Displacement, Position, and Proximity 182
5.5.2 Velocity 184
5.5.3 Acceleration 186
5.5.4 Force 188
5.5.4.1 Direct Contact Sensors 188
5.5.4.2 Piezoelectric Sensors 189
5.5.4.3 Conventional Force Sensors 190
5.5.5 Pressure 191
5.5.6 Contacts 193
5.5.7 Temperature 195
5.5.8 Chemical Particles 197
5.6 Optical Encoders 199
5.6.1 Resolutions 199
5.6.2 Decoding 202
5.7 Sensors in MEMS 203
5.8 Summary 205
Problems 205
References 207
6 Bridging Physical and Cyber Systems 209
6.1 Introduction 209
6.2 Characteristics of Signals 209
6.2.1 Analog Signals 209
6.2.2 Digital Signals 211
6.3 Conversions of Digital and Analog Signals 212
6.4 Basic Electronic Elements for DSP 213
6.4.1 Operational Amplifiers (Op-Amps) 213
6.4.2 Comparators 216
6.5 Digitization 217
6.5.1 Sampling 217
6.5.2 Quantizing 220
6.5.3 Sampling and Quantizing in Analog-to-Digital Conversion (ADC) 221
6.6 Analog-to-Digital Conversion (ADC) 225
6.6.1 Integrating ADC 226
6.6.2 Flash Converter 227
6.6.3 Successive Approximation 228
6.7 Holding Process in Sampling 236
6.8 Digital-to-Analog Conversion (DAC) 237
6.8.1 Weighted Resistor DAC 237
6.8.2 R-2R Ladder DAC 239
6.8.3 Quantization Noise 240
6.9 Summary 241
Problems 241
References 242
7 Signal Conditioning and Processing 245
7.1 Introduction 245
7.2 Basic Concepts in Electronic Circuits 245
7.2.1 Charge, Current, Voltage, and Power 245
7.2.2 Resistor, Capacitor, and Inductor 248
7.2.3 Input Loading and Output Loading 250
7.2.4 Basic Types of Signals 251
7.2.5 Main Parameters of Periodical Signals 254
7.2.6 Amplitude and Phase Changes 254
7.2.7 Wheatstone Bridges 257
7.3 Signal Cleaning 259
7.4 Signal Isolation 260
7.4.1 Optical Isolation by Light-Emitting Diodes (LEDs) 260
7.4.2 Capacitive Isolation by Capacitor 261
7.4.3 Inductive Isolation by Inductor 261
7.5 Signal Transmission 262
7.5.1 Switches 262
7.5.2 Multiplexer 262
7.5.3 Protection from High Voltage and Current 264
7.5.4 Modulation/Demodulation 265
7.6 Signal Conditioning 266
7.6.1 Amplification 266
7.6.2 Attenuation 271
7.6.3 Filtering 271
7.6.4 Linearization 275
7.6.5 Conditioning Digital Signals 275
7.6.6 Signal Clipping 277
7.7 Signal Clamping 277
7.8 Summary 278
Problems 278
References 279
8 System Controls 281
8.1 Basics of Control Systems 281
8.1.1 Complexity of Control Problem 281
8.1.2 Types of Control Problems 283
8.1.3 Architecture of Control Systems 284
8.1.4 Design of Control Systems 285
8.2 Control Theory 286
8.2.1 Open-Loop Control Versus Closed-Loop Control 286
8.2.2 Process Control Versus Motion Control 287
8.2.3 Steady Response Versus Transient Response 288
8.2.4 Transfer Functions 288
8.2.5 Orders of Control Systems 292
8.2.6 Stability Analysis 295
8.2.7 Accuracy of Control Systems 299
8.2.8 Classification of Control Systems 302
8.2.9 Frequency Responses 303
8.3 Proportional-Integral-Derivative (PID) Controls 305
8.4 Analog and Digital Implementation of PID Controllers 307
8.5 Advanced Controls 309
8.6 Intelligent Controls 309
8.6.1 Fuzzy Logic 310
8.6.2 Artificial Neural Network (ANN) 310
8.7 Design of Control System 312
8.7.1 Microcontrollers 313
8.7.2 Digital Signal Processing (DSP) 313
8.7.3 Field Programmable Gate Arrays (FPGA) 315
8.7.4 Microcomputers 316
8.7.5 Programmable Logic Controller (PLC) 316
8.8 Programming in PLC 318
8.8.1 Data Structure and Flow 318
8.8.2 Operating Cycle 319
8.8.3 I/O Modules and Addresses 319
8.8.4 Elements of Logic Control 322
8.8.5 Ladder Logic Diagrams 325
8.8.6 Timers and Counters 327
8.8.7 Sequencers 328
8.9 Summary 330
Problems 331
References 333
9 Digital Twins (DT-I), Digital Triads (DT-II), and Internet of Digital Triads Things (IoDTT) 335
9.1 Introduction 335
9.2 Digital Twins (DT-I) 338
9.3 Enabling Technologies 339
9.3.1 Data Acquisition 339
9.3.2 Modeling and Simulation 340
9.3.3 Communication Technologies 340
9.3.4 Cloud Technologies 340
9.3.5 Big Data Analytics (BDA) 342
9.4 From Digital to Physical Twins by Manufacturing 342
9.5 DT-Is in Manufacturing 343
9.5.1 System Digitization 347
9.5.2 Interactions of Physical and Digital Worlds 348
9.5.3 Historical Development of DT-I 349
9.5.4 Communication and Integration 351
9.5.5 System Architecture 353
9.6 Limitations of DT-Is 354
9.7 Advanced Attributes of Digital Entities in Manufacturing 355
9.8 Concept of Digital Triad (DT-II) 356
9.9 The Internet of Digital Triads Things (IoDTT) 360
9.10 DT-Is and DT-IIs in Sustainable Mechatronic Systems 362
9.10.1 Monitoring and Controlling 362
9.10.2 Data-Driven Decision-Making 364
9.10.3 Fault Detections 366
9.10.4 Predication of Fatigue Life 368
9.10.5 Virtual Verification and Validation (V and V) 370
9.11 Summary 371
Problems 371
References 374
10 Cyber-Physical Systems 379
10.1 Introduction 379
10.2 Characteristics of CPSs 382
10.3 Basic Features of Cyber System of CPS 384
10.3.1 Reactive Computation 385
10.3.2 Parallel Computing 385
10.3.3 Feedback Controls 385
10.3.4 Realtime-Ness 385
10.3.5 Dependability, Reliability, and Safety Assurance 386
10.3.6 Biological Intelligence 387
10.3.7 Hybrid Systems 387
10.3.8 Embedded Computation 387
10.3.9 Standards of Cyber Systems 387
10.4 Design of CPSs 387
10.5 Mathematical Modeling 388
10.5.1 Modeling Continuous Dynamics 391
10.5.2 Discrete Event Dynamic System (DEDS) 396
10.5.3 Hybrid Modeling 398
10.5.4 State Machines 400
10.6 Development Standards 403
10.7 Model-Based System Engineering (MBSE) 404
10.7.1 Modeling in MBSE 404
10.7.2 Design Stages in MBSE 405
10.7.3 Acausality Modeling by Modelica 406
10.7.4 Programming in Modelica 409
10.7.5 Formal Semantics 412
10.7.6 Verification and Validation (V&V) 414
10.8 Summary 415
Problems 416
References 418
11 Internet of Things 421
11.1 Introduction 421
11.1.1 IoT Concepts 422
11.1.2 Smart Things 424
11.1.3 Communication Protocols 425
11.2 Characteristics of IoT-Enabled Systems 427
11.3 Importance of IoT in Mechatronics 428
11.4 Data Flows in IoT-Enabled Systems 431
11.5 IoT-Enabled Capabilities 432
11.5.1 Interactions 433
11.5.2 Big Data Analytics (BDA) 435
11.5.3 Digital Manufacturing (DM) 435
11.6 Project-Based IoT-Enabled System Development 438
11.6.1 Ubiquitous Sensing 439
11.6.2 Fusing and Integrating Data from Heterogeneous Sources 439
11.6.3 Methods of Coping with Big Data 440
11.6.4 Surveillance and Data Visualization 441
11.6.5 Workflow Composition 441
11.6.6 Standardization of Specifications 444
11.6.7 Data Acquisition, Classification, and Utilization 444
11.7 Summary and Conclusion 445
Problems 447
References 447
12 Robotics 451
12.1 Introduction 451
12.2 Classifications 454
12.3 Basic Terminologies in Robotics 456
12.3.1 Mechanical Structure 457
12.3.2 Degrees of Freedom (DOF) 458
12.3.3 Workspaces 462
12.3.4 Modeling and Simulation 464
12.3.5 Accuracy, Precision, and Calibration 464
12.3.6 Other Specifications 465
12.4 Kinematic Modeling 466
12.4.1 Positions of Points, Links, and Bodies in 2D and 3D Space 466
12.4.2 Motions of Particles, Links, and Bodies 468
12.4.3 Vector-Loop Method for Motion Analysis of Plane Mechanism 473
12.4.3.1 Kinematic Parameters and Variables 477
12.4.3.2 Inverse Kinematics 477
12.4.3.3 Forward Kinematics 478
12.4.4 Denavit-Hartenberg (D-H) Notation 479
12.4.5 Jacobian Matrix for Velocity Relations 481
12.5 Dynamic Modeling 491
12.5.1 Inertia and Moments of Inertia 491
12.5.2 Newton-Euler Formulation 493
12.5.3 Lagrangian Method 498
12.6 Kinematic and Dynamics Modeling in Virtual Design 500
12.6.1 Motion Simulation 502
12.6.2 Model Preparation 502
12.6.3 Creation of Simulation Model 504
12.6.4 Define Motion Variables 504
12.6.5 Setting Simulation Parameters 506
12.6.6 Run Simulation and Visualize Motion 506
12.6.7 Analyze Simulation Data 507
12.6.8 Structural Simulation Using Motion Loads 508
12.6.9 Summary on Kinematic and Dynamic Modeling 510
12.7 Mobile Robots 511
12.7.1 Three-Wheeled Robots 514
12.7.2 Four-Wheeled Robots 515
12.7.3 Unmanned Aerial Vehicles (UAVs) 516
12.8 Robotic Programming 519
12.9 Summary 521
Problems 521
References 524
13 End-Effectors 527
13.1 Introduction 527
13.2 Grasping Theory 528
13.2.1 Contacts on Object 528
13.2.2 Motions and Forces 530
13.2.3 Frictions 531
13.2.4 Grasping Model 533
13.2.5 Form Closure 534
13.2.6 Force Closure 536
13.2.7 Quality of Grasping 537
13.3 Mechatronic Design of End-Effectors 537
13.3.1 Mechanical and Actuating Components 538
13.3.2 Sensing Components 541
13.3.3 Control Components 542
13.4 Evaluation of Grasping Performance 544
13.5 Grasping Configurations 545
13.6 Types of End-Effectors 546
13.6.1 Types of Grippers 546
13.6.2 Types of Processing Tools 548
13.6.3 Multifunctional Tools 549
13.6.3.1 Concepts 550
13.6.3.2 Classification 550
13.6.3.3 Advantages and Disadvantages 554
13.6.3.4 Selection Principles 556
13.6.3.5 Development Trends 556
13.7 Main Factors in Designing an End-Effector 558
13.8 Computer-Aided Design Tools for End-Effectors 560
13.9 Summary 560
Problems 560
References 561
14 Metaverses for Sustainability Mechatronic Systems 565
14.1 Introduction 565
14.2 FRs of Sustainable Mechatronic Systems 566
14.2.1 Scalability, Accessibility, Security, Privacy, and Legal Issues 568
14.2.2 First-Time-Right from Virtual to Physical World 568
14.2.3 Ubiquitous Data and Computing 568
14.2.4 Diagonalizability, Predictability, and Adaptability 569
14.2.5 Human Intelligence for Uncertainty and Changes 570
14.2.6 Data-Driven Decision-Making Supports 571
14.3 Metaverse and Relevant Technologies 573
14.3.1 Architecture or Framework 574
14.3.2 Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (ER) 576
14.3.3 Digital Twins (DTs), Cyber-Physical Systems 578
14.3.4 Internet of Things (IoT) and Edge Computing 579
14.3.5 Big Data Analytics (BDA) and Cloud Computing (CC) 581
14.3.6 Blockchain Technologies (BCTs) 581
14.3.7 Artificial Intelligence (AI) 583
14.3.8 Human-Machine Interactions (HMI) 585
14.3.9 Data-Driven Decision-Making Systems 586
14.4 Metaverses for Sustainability 587
14.4.1 Metaverses to Deal with Changes and Uncertainties 588
14.4.2 Sustainable Manufacturing 590
14.4.3 Framework of Metaverse Use Cases 590
14.4.4 Metaverses for Remote Access 592
14.5 Summary and Future Work 593
Problems 593
References 594
15 Human Cyber-Physical Systems (HCPS) 603
15.1 Introduction 603
15.2 Humans' Roles in CPS 605
15.3 Enabling Technologies 608
15.4 Human-Machine Interactions (HMI) 610
15.4.1 Collaborative Robots 610
15.4.2 Types of HMIs 612
15.4.3 Collaborative Machines in Manufacturing 613
15.4.4 Critical Requirements of Cobots 613
15.4.5 Safety Assurance Mechanisms for Cobots 616
15.4.5.1 Safety-Rated Monitored Stop (SRMS) 616
15.4.5.2 Hand Guiding (HG) 617
15.4.5.3 Speed and Separation Monitoring (SSM) 618
15.4.5.4 Power and Force Limiting (PFL) 618
15.4.6 Cobotic Systems 618
15.4.7 End-Effectors of Cobots 620
15.4.7.1 Affordable Force Monitoring 620
15.4.7.2 Ergonomic Protection of Grippers 621
15.4.8 Safety Assurance in HCPSs 622
15.5 Example of Assistive Technologies 622
15.5.1 Cobots in Healthcare 622
15.5.2 Conceptual Design of Cobot 623
15.5.3 Kinematic Model 624
15.5.4 Motion for Arbitrary Explicit Trajectory 625
15.5.5 Motions of Omniwheels 626
15.5.6 Dynamic Control Model 626
15.5.6.1 Analyses of Force on Omniwheels 627
15.5.6.2 Analyses of Force on Cobot Platform 628
15.5.6.3 Constraints to Maintain Contacts to Ground 629
15.5.6.4 Strategies of Cobot Controls 630
15.5.7 Simulation 631
15.5.8 Summary of HCPS as Assistive Technologies 632
15.6 Brain-Computer Interfaces (BCI) for Supervisory Controls 634
15.6.1 Unmanned Aerial Vehicles (UAVs) 634
15.6.2 UAV Controls 635
15.6.3 BCI for Effective HMI 636
15.6.4 Development of BCIs 638
15.6.4.1 Brain Signals 639
15.6.4.2 Data Acquisition 640
15.6.4.3 Feature Classification and Detection 642
15.6.5 BCI Development Platform 645
15.7 Summary 648
Problems 649
References 649
Index 657
1
Introduction
1.1 Introduction
Mechatronics is an engineering discipline that brings multiple conventional disciplines including mechanical, electrical, electronic, and information engineering together to optimize the solutions to various engineering problems. Originally, the concept of Mechatronics was coined by a Japanese Engineer Tetsuro Mori from the words of mechanical engineering and electronics in 1969. Nowadays, the coverage of modern mechatronics has gone far beyond from the integration of conventional engineering disciplines to the extension to many new disciplines such as artificial intelligence (AI), telecommunications, and cybersecurity as long as emerging disciplines can be integrated to enhance the capabilities of mechatronic systems.
In comparison with conventional systems, a mechatronic system consists of a set of multiple mechatronic components that exhibit multidisciplinary behaviors. Therefore, the design of a mechatronic system must be performed concurrently, so that design constraints in multiple disciplines can be modeled, analyzed, and satisfied simultaneously. From this perspective, mechatronics is also viewed as a philosophy where a system design associated with multiple disciplines is performed concurrently to seek integrated solutions to complex engineering problems. Mechatronics becomes a growing discipline that has been, and it will be evolve continuously with emerging technological advancements in Materials, Science, Processes, Engineering, Integration technologies, and Information Technologies (ITs). Most of the classic books on mechatronics have lagged to reflect recent advancements, especially in ITs. In the following sections, the trends of the developments in engineering designs, integration technologies, and ITs are discussed with a focus on their impact on Mechatronics.
1.2 Growing Complexity of Engineering Designs
Engineering design is to formulate customer's requirements (CRs) into a design problem with specified constraints and objectives and develop a design solution (DS) that can satisfy CRs optimally. A complete engineering design usually includes design for manufacturing (DfM) and design for assembly (DfA) where the constraints of manufacturing or assembling processes are taken into considerations at the phases of manufacturing and assembling, respectively. By DfM and DfA, a virtual model can be used to analyze system behaviors, predict system outcomes, and verify if all design constraints can be satisfied. These reduce the needs of iterations when design defects are identified and fixed at late phases of system development.
Since the information and knowledge about a product or system are accumulated gradually when its design process proceeds, the constraints involved in later design stages cannot be verified until relevant information becomes available. This becomes an obvious reason why an engineering design process is naturally iterative. In other words, the constraints that are ignored in early design phases must be verified later. In such a way, a design space with tentative solutions should be continuously refined to satisfy more and more constraints until all of them are fully satisfied.
It is desirable that less number of iterations is needed to transform a virtual model into a physical model. This implies that design iterations all occur in the virtual world with no additional cost on physical prototyping. The finalized virtual model is converted into its physical model correctly at the first time. It is referred as First-Time Right (FTR) practice (Bi and Wang 2020). The methodologies for engineering designs are being advanced continuously to cope with the growing complexity of products or systems in their lifecycles from design to manufacturing, assembly, application, and to disposal. Figure 1.1 shows the trend of increasing complexity of engineering designs from the perspective of manufacturing (Alkan et al. 2018). The growth of the complexity of a manufacturing system can be observed in the aspects of products, enabling technologies, and business environments.
Figure 1.2 shows the dimensions of complexity in engineering designs that are dependent on those of products, technologies, and degrees of dynamics and uncertainties. The complexity of each aspect could be further decomposed when the solutions to corresponding functional requirements are not available. Accordingly, the complexity of products depends on many factors including the number of parts and assemblies, the degrees of connectivity, nonlinearity, and dynamics, the number of accessible technologies, and the levels of technological difficulties and assistive technologies.
Figure 1.1 Dimensions of growing complexity of engineering designs
Figure 1.2 The dimensions of complexity in engineering design: products, technologies, and degrees of dynamics and uncertainties
1.2.1 Products
The complexity of a product has been measured by numerous factors such as types and numbers of constitutive parts and components, types and numbers of the processes to manufacture parts and assemble parts into components, variants and volumes of products, and system performance criteria such as quality, lead-time, cost, lifespan, and after-sales services of products (Orfi et al. 2011). Researchers agree that the scale and the complexity of modern products have been increasing greatly. Figure 1.3 shows some examples of main variables that affect the complexity of products (i.e., lawn mowers, grand pianos, cars, and airplanes); some factors such as numbers, types, and complexity of constitutive components contribute to the complexity of products directly. It seems clear that a product with a high-level complexity involves a high number and types of simple or complex parts and components.
The growing complexity of modern products can be evidenced by the evolution of various product families. Adamsson (2005, 2007) used the examples of wiring harnesses to show an increase in the complexity of automotive products. An automobile in 1949 had ~60 contact points with ~40 wires. An automobile in 1990 had around 3800 contact points and used approximately 1900 wires with a total length of ~3 km. An automobile in 1999 used 110 electric motors and 60 electronic control units (ECUs). Three data bus systems were used to support information integration and data exchanges. BeyondPLM (2018) discussed the trend of the ever-increasing complexity of modern products; it was associated with the number of configuration items (NICs). A typical mechanical system, mechatronic system, and large-scale integrated system have typically less than 103, 103-105, and over 108 NICs, respectively.
Product complexity is related to numerous factors in manufacturing and production such as design, development, manufacturing, assembly, and supply chain management. As shown in Figure 1.4a, product complexity was modeled in the dimensions of designs, manufacturing processes, functionalities, and varieties. A manufacturer can be profitable only when the complexity of products and associated processes can be managed by mechatronic design at the design phase and by mass customization at the manufacturing phase appropriately. As shown in Figure 1.4b), the more products a company makes, the higher revenue the company can gain. On the other hand, making more product variants implies the increase in the complexity of the corresponding production system, thus affecting the productivity, lead time, and cost reduction. Production cost increases monolithically with the number of products and variants. Knowing customers' needs becomes a strategic resource to enterprises now. However, there is a limited business window for enterprises to make products to meet customers' needs in a profitable way. To expand a profitable business window, efforts can be made to increase the production revenue by making more products through mass customization and reduce the development cost by increasing system efficiency such as through mechatronic design.
Figure 1.3 Examples of product complexity versus numbers of parts
With the need for more versatile and advanced products, the number and types of parts and the complexity levels of parts are expected to be increased continuously. The complexity of products due to other factors, such as volume and variety in enterprise and personalization, has been thoroughly discussed by other scholars (Bi et al. 2021a). The survey of over 246 engineers by Rowe (2019) concluded that the complexity of products was continuously increasing, and design methodologies need to evolve to manage the complexity effectively. Ninety-two percent of the engineers reported that in the last five years, the products had increased the complexity in various aspects such as intricate mechanical designs, embedded electronics, and newly introduced materials and processes. It was found that the main causes of increased products' complexities were attributed by intricate mechanical design (57%), more electronics (47%), adoption of different materials (43%), reduced reductions (40%), system integration (30%), compacted...
