What is Digital Engineering?
Digital engineering is an engineering approach that uses digital models, data, software, simulation and computational tools to support the design, development, manufacture, operation and maintenance of engineered systems.
It brings together traditional engineering disciplines with information technology, data science, systems engineering, artificial intelligence and advanced manufacturing.
The Digital Engineering Concept
Requirements → Digital Models → Simulation → Manufacture → Operation → Data → Improved Design
The site www.engineering.me.uk provides a general resource on Engineering.
The Digital Engineering Lifecycle
Digital engineering attempts to maintain consistency and traceability of engineering information throughout the lifecycle rather than treating design, analysis, manufacturing and operation as isolated activities.
Computer-Aided Design
CAD provides the digital foundation for much modern engineering design. Engineers can create three-dimensional models, assemblies, drawings and detailed product definitions.
The LinkedIn Group CAD CAM CAE has been created to connect the people interested in the CAD CAM CAE field. You can also link to the LinkedIn profile CAD CAM CAE.
3D Modelling
Creation of detailed digital representations of components and assemblies.
Parametric Design
Dimensions and relationships can be controlled through design parameters.
Assemblies
Multiple components can be combined to investigate fit, movement and interfaces.
Design Documentation
Digital models can support drawings, manufacturing information and technical documentation.
The site www.cad-cam-cae.com provides a general resource on CAD/CAM/CAE.
Engineering Simulation
Computer simulation allows engineers to investigate the behaviour of designs before physical prototypes are built or tested.
Finite Element Analysis
Structural, thermal and other physical problems can be approximated using finite-element models.
The site https://www.cad-cam-cae.com/FEA.ht provides a general resource on Finite Element Analysis (FEA).
Computational Fluid Dynamics
CFD models fluid flow, heat transfer and related phenomena.
The LinkedIn Group Computational Mechanics Research has been created to connect the people interested in the computational mechanics field. You can also link to the LinkedIn profile Numerical Analyst.
Multibody Dynamics
Mechanical systems containing moving bodies can be analysed computationally.
Acoustic Simulation
Digital models can predict sound, vibration and acoustic radiation.
The LinkedIn Group Computational Acoustics has been created to connect the people interested in the computational acoustics field.
Electromagnetic Simulation
Electromagnetic fields and interactions can be studied computationally.
Process Simulation
Manufacturing and production processes can be evaluated before implementation.
Model-Based Systems Engineering
Model-Based Systems Engineering (MBSE) uses structured digital models to represent requirements, functions, architecture, interfaces and relationships within complex systems.
Digital Twins
A digital twin is a digital representation of a physical asset, system or process that can be connected to information about its real-world counterpart.
Design Twin
Represents the intended design and supports development and verification.
Production Twin
Connects manufacturing information with the product definition.
Operational Twin
Uses operational data to understand the condition and behaviour of an asset.
Predictive Twin
Uses models and data to anticipate future behaviour, faults or maintenance requirements.
Engineering Data
Digital engineering produces and consumes large quantities of engineering data. Managing this information reliably is essential for traceability and decision-making.
| Data type | Engineering use |
|---|---|
| CAD geometry | Product definition and design |
| Simulation results | Performance prediction and verification |
| Sensor data | Monitoring physical systems |
| Manufacturing data | Production control and quality assurance |
| Maintenance records | Asset management and reliability |
| Requirements data | Systems engineering and traceability |
Artificial Intelligence and Digital Engineering
Artificial intelligence and machine learning can complement conventional engineering analysis by identifying patterns in large datasets and assisting prediction, optimisation and decision-making.
Design Optimisation
Algorithms can explore large design spaces and identify promising configurations.
Predictive Maintenance
Machine-learning models can identify patterns associated with equipment degradation.
Surrogate Models
Fast approximations can reduce the computational cost of repeated simulations.
Computer Vision
Image analysis can support inspection and automated quality control.
Digital Manufacturing
Additive Manufacturing
Digital models can be translated directly into additive manufacturing processes.
CNC Manufacturing
Computer-controlled machines use digital manufacturing information to produce components.
Robotics
Industrial robots integrate digital instructions, sensors and automated processes.
Inspection
Digital metrology and automated inspection compare manufactured products with digital definitions.
Applications of Digital Engineering
Aerospace
Aircraft, spacecraft, propulsion systems, structures, avionics and manufacturing.
Automotive
Vehicle design, electrification, autonomous systems, crash simulation and manufacturing.
Energy
Power generation, wind turbines, nuclear systems, grids and energy infrastructure.
Construction
Building Information Modelling, structural analysis and digitally managed infrastructure.
Marine Engineering
Ship design, hydrodynamics, propulsion and offshore systems.
Biomedical Engineering
Medical devices, biomechanics, implants and patient-specific computational models.
Digital Engineering Infrastructure
Cloud Computing
Provides scalable computational resources and collaborative engineering environments.
High-Performance Computing
Enables large-scale simulation, optimisation and scientific computation.
Industrial Internet of Things
Connects sensors, machines and engineering systems to digital networks.
Data Platforms
Store, integrate and manage engineering information across organisations.
Cybersecurity
Protects engineering data, intellectual property, operational technology and connected systems.
Visualisation
Transforms complex engineering datasets into interpretable visual representations.
Digital Engineering Skills
| Skill | Role |
|---|---|
| CAD | Digital product and component design |
| CAE | Computer-aided engineering analysis |
| Programming | Automation, modelling and data processing |
| Data Science | Analysis and interpretation of engineering datasets |
| Systems Engineering | Integration of complex engineering systems |
| Artificial Intelligence | Prediction, optimisation and intelligent automation |
| Manufacturing Engineering | Digital production and process control |
| Cybersecurity | Protection of digital engineering infrastructure |
Benefits of Digital Engineering
Faster Development
Digital workflows can reduce the time required to explore and evaluate design concepts.
Better Decisions
Simulation and data provide quantitative evidence for engineering decisions.
Reduced Prototyping
Virtual testing can reduce the number of physical iterations required for some designs.
Improved Traceability
Connected digital information can link requirements, design decisions, verification and operation.
Predictive Maintenance
Operational data can support earlier identification of degradation and faults.
Lifecycle Optimisation
Digital engineering supports decisions throughout the complete engineering lifecycle.
Digital Engineering and Sustainability
Digital engineering can support sustainable engineering by enabling virtual experimentation, design optimisation, efficient manufacturing and better management of assets throughout their lifecycles.
Model → Simulate → Optimise → Manufacture → Monitor
Digital information can provide a continuous engineering thread from concept to operation.
The Future of Digital Engineering
AI-Assisted Engineering
Engineering workflows will increasingly combine physical models with machine-learning methods.
Autonomous Design
Optimisation systems can explore design alternatives against multiple engineering constraints.
Connected Digital Twins
Digital twins will increasingly integrate real-time operational information with engineering models.
Immersive Engineering
Virtual and augmented reality can provide new ways of interacting with complex engineering models.
Generative Design
Computational methods can generate candidate designs from requirements and constraints.
Digital Thread
Connected information architectures can link requirements, design, manufacturing, operation and disposal.
Development of Digital Engineering
Computer-aided numerical analysis and early computer-aided design began transforming engineering practice.
CAD, finite-element analysis, CFD and computer-controlled manufacturing became increasingly established.
Product lifecycle management, integrated simulation and networked engineering workflows expanded.
Cloud computing, additive manufacturing, industrial IoT and digital-twin concepts became increasingly prominent.
AI, data-driven engineering, model-based systems engineering and connected digital engineering ecosystems continue to develop rapidly.
Summary
Digital engineering combines engineering knowledge with digital technologies to create more connected, data-driven and computationally enabled engineering processes.
- Computer-aided design and engineering
- Engineering simulation
- Model-Based Systems Engineering
- Digital twins
- Engineering data and the digital thread
- Artificial intelligence and machine learning
- Digital manufacturing and robotics
- Additive manufacturing
- Cloud and high-performance computing
- Cybersecurity
- Predictive maintenance
- Sustainable engineering
Digital Engineering
Design · Simulate · Connect · Manufacture · Operate · Improve