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Smart Components ™

Engineers can embed equations and empirical data into Smart Components, enabling the components to compute their own functional properties. Bearings can compute their B-10 life; springs can compute their force-deflection curves; shafts can compute their deflection under load; motors can compute their RPM-Torque curves.

Functional interdependencies between Smart Components are easily created and managed: one component’s functional outputs can be connected to another’s functional inputs. For example, the force generated by a spring produces a load on a shaft, which in turn transmits the load to a bearing.

These component-level knowledge modules drastically reduce the time mechanical engineers spend looking for information and enable collaborative system-level modeling, thereby accelerating system performance analysis and optimization.

Smart Assemblies ™

Smart Assemblies can also roll up costs, giving team members a running total on manufacturing cost estimates. Integrated Excel spreadsheets make it a snap for costing engineers to embed information, such as costing formulas, directly into component definitions.

Smart Component Example

The following example of a spring model highlights some of the main advantages of Enventive's Smart Components.

Complete, integrated model information

Enventive enables engineers to embed formulas computing functional properties in component models, as shown in the spring model in Figure 1. Engineers can examine and modify graphical and mathematical views of their model simultaneously. Changes are automatically reflected throughout the model, in the sketch, equations, and parameters, so you never have to worry about keeping data in sync.

Figure 1. A model of a spring.

Integrated equations compute properties

The equations for computing the spring properties (Figure 2) are an integral part of the spring.

Figure 2. Equations are an integral part of the model.

Tolerance analysis identifies contributors

When the spring component is used in an assembly, tolerance analyses involving the spring will identify contributors from the spring equations, such as wire diameter (wire_dia), as shown in the Contributor Info section of the tolerance analysis report in Figure 3.

Figure 3. Tolerance analysis identifies contributors.

Optimizing critical parameters reduces variation

Using the Excel Solver add-in, optimization may be run directly from the tolerance analysis report. In Figure 4, the Solver has been set up to minimize variation (minimize sigma) by changing wire diameter, free-length, and number of turns. Boundaries (constraints) have been specified for these parameters, and a target value has also been specified for the analyzed parameter.

Figure 4. The Excel Solver may be used to optimize parameter values.

As shown in Figure 5, the optimization finds nominal values that meet the target performance goals while minimizing variation. Optimization has been used to reduce variation by 28%, without tightening tolerances.

Figure 5. Optimization results.