correlation study of a commercial truck biaxial test...• correlation of strain data shows both...

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2019 Prenscia User Group Meeting | April 30th – May 1st | Novi,MI (USA)

Correlation Study of a Commercial Truck Biaxial Test

Mike McLeod

Wheel Engineering North America, Accuride Corporation

mmcleod@accuridecorp.com

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2Intro to Accuride Corporation

• Leading Supplier to North American and European truck/trailer industry• 57% Commercial Truck & Trailer• Balanced 48% America, 40% Europe

• Three Business Units • Accuride Wheels Europe & Asia• Accuride Wheels North America• Accuride Wheel End Solutions

• 4,800 Associates in Europe, North America & Asia• Operational Footprint

• 14 manufacturing facilities & 7 distribution centers• 13 global third-party contract manufacturing locations

• 2018 Estimated Revenue: >$1.2 billion

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• Primary Challenge : • Use DesignLife and GlyphWorks to predict pass/fail of truck wheels tested on

on an LBF biaxial fatigue test machine and identify the correct failure locations with reasonable confidence.

• Sub Challenges• Accurate knowledge of loading• Accurate prediction of strain history• Appropriate prediction of fatigue damage• Knowledge of local material properties• Knowledge of local manufacturing effects.• Multiple fatigue modes. i.e. weld, others• Field data to accelerated test profile• Ability to quantify the uncertainties

The Challenge

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• Prenscia UGM 2018• Introduction to the Commercial Truck Wheel Industry• Biaxial Load Testing of Truck Wheels• Required Fatigue Strength (RFS) Calculation Using DesignLife

• Prenscia UGM 2019• Accurate knowledge of loading• Accurate prediction of strains• Prediction of fatigue damage

• Prenscia UGM Future• Knowledge of local material properties• Knowledge of local manufacturing effects.• Field data to accelerated test profile• Ability to quantify the uncertainties

Today’s Scope

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5LBF Biaxial Truck Wheel Test Machine

100 Sequence StepsRepeated Until Rolling Distance Achieved5-6 Million Cycles

22.5 x 9.0 Aluminum9100 lbf @ 141 psi

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• Test machine input: Fv, Fh, Velocity, Time• Input Versus Wheel Reaction Force

• Kinematics• Nonlinear tire response• Tire on curb• Test versus wheel coordinate system

Tire Loading Evaluation: Input Loading

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• Wheel Force Transducer

• Measure time series data through entire load profile• 16 channels of data; 300 Hz: 21.2 minutes, 700k data points

Tire Loading Evaluation: Wheel Reaction Forces

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8Tire Loading Evaluation: Input Versus Reaction

Vertical Input Versus Reaction Horizontal Input Versus Reaction

Red=Input Blue=Reaction Black=Difference

Input Versus Reaction Correlation

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9Strain Correlation

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10GlyphWorks Template

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11Strain Prediction and Correlation

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12Fatigue Damage: Direct From Strain Data

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13Fatigue Damage: From FEA

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• Applied scale factor because insufficient damage was being generated• Significant differences in damage predictions• Multiple stress values possible for damage predictions. • Potential cumulative error effects. • Wheel operates in the high cycle range making results very sensitive to input. • Need to repeat with RFS values

Damage Correlation Results

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• GlyphWorks and DesignLife are the right tools for this work.• Applying a correction factor to the test input loading improves FEA loading• 300 Hz data brought out a lot of detail in the actual loading of the wheel• Overlaying FEA data with TS data required playing with frequency data. • Correlation of strain data shows both shape and magnitude. • Correlation of strain data showed potential improvements in the FEA tire loading• Angular frequency of the tire data can influence strain correlation.• FEA modeling practices can influence strain predictions which effect damage

• Ultimate goal is to predict test fatigue damage and show correlation with damage from measure strains as well as actual observed failures.

Lessons Learned

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Challenge Solution

Summary

Results

The current sub challenges addressed include 1) validating actual wheel reaction loads relative to test input load spectrum, 2) correlation of predicted with actual strain values, and 3) correlation of fatigue damage calculated from measured and predicted strain histories.

GlyphWorks was used to evaluate time series data gathered from running a sample wheel loading sequence. The data evaluated included wheel force transducer and strain gage output. Tables of load input and predicted strains were also input for correlation with time series data.

A DesignLife template was used to calculate fatigue damage and RFS for the test loading sequence

• Load correlation identified differences between input loading and actual wheel reaction forces. Analytical correction has been applied to predictive models.

• Strain evaluation showed some accurate predictions as well as areas for improvement for the load steps evaluated.

• First attempt at fatigue damage correlation. Damage provides overall test performance metric for a complex loading evaluation. More work is needed to improved correlation and accuracy between damage calculations from measured and predicted strain histories.

• Progress has been made towards performance prediction capability for the LBF biaxial test of commercial truck wheels. Every test series eliminated potentially saves 4-6 months of time to market.

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Correlation Study