Computer-aided engineering
Technology aiding engineering analysis tasks.
Computer-aided engineering (CAE) refers to using technology to support engineering analysis tasks. It covers methods like finite element analysis (FEA), computational fluid dynamics (CFD), multibody dynamics (MBD), durability studies, and optimization. CAE is part of a broader group called computer-aided technologies (CAx), which also includes computer-aided design (CAD) and computer-aided manufacturing (CAM).
The term CAE was originally coined in the late 1970s by Jason Lemon, founder of Structural Dynamics Research Corporation (SDRC), to describe computer use in engineering more broadly than just analysis. Today, that broader meaning is more commonly known as CAx or product lifecycle management (PLM). In a CAE system, each tool acts as a node on an information network, able to interact with other nodes.
CAE work typically covers stress analysis via FEA, thermal and fluid flow analysis via CFD, multibody dynamics and kinematics, process simulation for operations like casting or molding, and product or process optimization. Any CAE task generally follows three phases: pre-processing (building the model and defining environmental factors), analysis solving (often on high-powered computers), and post-processing (visualizing results). This cycle may be repeated manually or with commercial optimization software.
In the automotive industry, CAE tools have cut development costs and time while improving safety, comfort, and durability. Their predictive power now allows much design verification through computer simulations rather than physical prototypes. However, CAE reliability depends on correct assumptions and identifying critical inputs. Despite advances, physical testing remains necessary for verification, model updating, defining loads and boundary conditions, and final prototype approval.
CAE is well established for verification and troubleshooting, but results often arrive too late in the design cycle to truly guide it. This becomes more problematic as products grow more complex, incorporating smart systems requiring multi-physics analysis (including controls) and new lightweight materials that engineers may not fully understand. To address this, CAE software companies and manufacturers pursue improvements in both tools and processes. On the software side, they develop more powerful solvers, use computer resources better, and embed engineering knowledge into pre- and post-processing. Recent advances include integrating artificial intelligence and machine learning for real-time simulations and predictive modeling. On the process side, they aim for better alignment between 3D CAE, 1D system simulation, and physical testing to improve modeling realism and speed. They also work to integrate CAE more fully into product lifecycle management, connecting product design with product use—a need for smart products. This enhanced approach is called predictive engineering analytics.
- field
- Engineering analysis
- known_for
- Finite element analysis, computational fluid dynamics, multibody dynamics, optimization
- coined_by
- Jason Lemon
- organization
- Structural Dynamics Research Corporation (SDRC)
- related_terms
- CAx, product lifecycle management (PLM)
Lore & Background
The term CAE was coined by Jason Lemon, founder of Structural Dynamics Research Corporation (SDRC), in the late 1970s to describe the use of computer technology within engineering in a broader sense than just engineering analysis. However, this broader definition is better known today by the terms CAx and product lifecycle management (PLM). CAE systems are individually considered a single node on a total information network, and each node may interact with other nodes on the network.
CAE areas covered include stress analysis on components and assemblies using finite element analysis (FEA); thermal and fluid flow analysis using computational fluid dynamics (CFD); multibody dynamics (MBD) and kinematics; analysis tools for process simulation for operations such as casting, molding, and die press forming; and optimization of the product or process. In general, there are three phases in any computer-aided engineering task: pre-processing, analysis solver, and post-processing of results. This cycle is iterated either manually or with the use of commercial optimization software.
CAE tools are widely used in the automotive industry, enabling automakers to reduce product development costs and time while improving safety, comfort, and durability. The predictive capability of CAE tools has progressed to the point where much of design verification is done using computer simulations rather than physical prototype testing. However, physical testing is still a must for verification, model updating, and final prototype sign-off.
Reader's Guide
Computer-aided engineering (CAE) has become a cornerstone of modern engineering analysis, particularly in industries such as automotive manufacturing. Its significance lies in its ability to simulate and analyze product performance under various conditions, reducing the need for costly and time-consuming physical prototypes. The three-phase process—pre-processing, analysis solver, and post-processing—enables iterative design optimization. Despite advances, physical testing remains essential for verification and model updating. The future of CAE involves integration with artificial intelligence and machine learning for real-time simulations, better alignment between 3D CAE, 1D system simulation, and physical testing, and deeper integration into product lifecycle management to connect product design with product use. This enhanced process is referred to as predictive engineering analytics. CAE's legacy includes its role in enabling safer, more durable products and its ongoing evolution to handle complex multi-physics and smart systems.
Did You Know?
- The term CAE was coined by Jason Lemon, founder of Structural Dynamics Research Corporation (SDRC), in the late 1970s.
- CAE includes finite element analysis (FEA), computational fluid dynamics (CFD), multibody dynamics (MBD), durability, and optimization.
- CAE tools are widely used in the automotive industry to reduce product development costs and time while improving safety, comfort, and durability.
- Recent developments have seen the integration of artificial intelligence and machine learning into CAE tools, enabling real-time simulations and predictive modeling.
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