LW Finder by LW Engineering

LW Finder by LW Engineering toiteallikaks on Advanced Partner Alliance

Täiustage simulatsiooni, täiustage inseneriteadust ja optimeerige disaini täiendava 82+ tootega, mis on juba osa teie pakkumisest. Avastage kolmandate osapoolte lahendused ühest ühtsest allikast, mida tunnete ja usaldate Advanced Partner Alliance (APA) kaudu.<br/>APA lahendused tarnitakse läbi Altair Unitsi litsentsimissüsteemi, mis võimaldab klientidel juurdepääsu mitmele insenerivahendile, kasutades jagatud litsentside kogumit. See vähendab kasutuselevõtu takistusi ja lihtsustab teie klientide hankeid.

Avasta Advanced Partner Alliance

Miks Strain Gauge Placement?

LW Finder helps engineers optimize strain gauge placement using simulation data to accurately measure loads on structures. It serves test and durability engineers working with vehicles, machinery, and structural components. The software predicts measurement accuracy, supports load prioritization, and reduces testing risk by identifying potential data quality issues before physical testing begins.

Eelised

Fast genetic algorithms search possible strain gauge locations and angles to find those that result in the lowest measurement error.
Prioritization of load cases lets users select which load cases should provide the best data.
Prediction of the measurement error allows users to ensure the strain gauges provide the data needed, and if not, allows users to redesign or cancel the test.

Soovitatavad Võimalused

Predict Measurement Accuracy

Estimates expected load reconstruction error before testing, helping engineers assess data quality in advance.

Gauge Location Prediction

Automatically identifies the most informative strain gauge locations and orientations using FE simulation results.

Product Interfaces

LW Finder interfaces with Simcenter Hypermesh, Simcenter Hyperview, and Simcenter Optistruct.

Reaalse Maailma Rakendused

Select...

Test Error Prediction

LW Finder predicts strain measurement errors before physical testing. Engineers can evaluate design changes virtually, improve sensor placement, and increase data quality, reducing test uncertainty and minimizing costly re-testing.