Computer-aided engineering

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.