Product geometry
Geometry determines how loads, forces, heat, flow and other physical effects move through a product.
Improving product performance starts with understanding what limits the current design. The solution may involve geometry, materials, simulation, product architecture, manufacturing or the way the product is used.
Engineering performance improvement is the systematic process of identifying performance limitations and changing the product, material, geometry, architecture or operating conditions to achieve a better result.
Performance is not a single engineering variable. Depending on the product, it may mean strength, stiffness, efficiency, speed, thermal behaviour, durability, range, accuracy, reliability or another measurable requirement.
The first step is therefore not optimization. It is understanding exactly what “better” means.
Geometry determines how loads, forces, heat, flow and other physical effects move through a product.
Material properties can determine strength, stiffness, thermal behaviour, wear, corrosion resistance and durability.
Loads, temperatures, speeds, environments and usage conditions define the conditions under which performance must be achieved.
Component arrangement and system architecture can create or constrain performance independently of individual components.
Identify what is preventing the current product from meeting the desired performance target.
Use engineering analysis and simulation to understand the behaviour of the current design.
Explore geometry, materials, architecture and operating conditions that could improve the limiting behaviour.
Balance performance against weight, cost, durability, manufacturing and other product constraints.
A structured performance improvement process connects requirements, analysis, design exploration and validation.
A technically impressive improvement is not necessarily a useful product improvement. The performance target must connect to the actual requirements of the product and its users.
Engineering AI can help identify relationships between design variables and performance using simulation, experimental or historical engineering data.
Machine learning models can potentially act as surrogate models for expensive simulations, allowing engineers to explore larger design spaces more quickly.
AI can also support automated design exploration, performance prediction and optimization. However, the resulting designs still require engineering validation.
Understand how simulation and numerical analysis can reveal the causes of performance limitations.
Explore CAE & FEA →Explore how machine learning connects with engineering simulation and product development.
Explore Engineering AI →Explore systematic approaches to finding better solutions within engineering constraints.
Explore Optimization →Understand how engineering decisions connect to the development of real products.
Explore Product Engineering →Product performance can be improved by identifying performance limitations, analysing engineering behaviour, optimizing design variables, evaluating materials and validating the resulting design.
Simulation can help engineers understand how a product behaves under relevant operating conditions and evaluate design changes before physical testing.
Yes. Material properties can strongly influence strength, stiffness, thermal behaviour, durability, weight and other performance characteristics.
AI and machine learning can support performance prediction, surrogate modelling, design exploration and optimization when appropriate engineering data and validation methods are available.
Start with the performance problem. MATAPPLY connects technology, product, market and business perspectives to identify possible paths forward.
START WITH A PROBLEM