Research Papers

Fatigue Design Load Identification Using Engineering Data Analytics

[+] Author and Article Information
Ha-Rok Bae

Assistant Professor
Department of Mechanical
and Materials Engineering,
Wright State University,
Dayton, OH 45435

Hiroaki Ando

Engineering Specialist
Advanced VPD, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Sangjeong Nam

Staff Analyst
Information Analytics, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Sangkyum Kim

Engineering Team Lead
Information Analytics, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Christopher Ha

Information Analytics, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 12, 2013; final manuscript received June 3, 2014; published online November 14, 2014. Assoc. Editor: Xiaoping Du.

J. Mech. Des 137(1), 011001 (Jan 01, 2015) (12 pages) Paper No: MD-13-1405; doi: 10.1115/1.4027849 History: Received September 12, 2013; Revised June 03, 2014; Online November 14, 2014

Selecting an appropriate concept design in the early stage of the product development process is crucial for successful machine development. As one of the important design requirements, a structural design needs to validate the structural fatigue life against physical tests of actual field operations. Traditionally, the fatigue design loads for the concept design evaluations are generated by hand calculations based on past experience to capture envelopes of expected system responses. But such an approach often does not capture actual loading behaviors in the field. An alternative approach of leveraging observed data from physical tests is highly desired, and a new method of this approach is introduced in this study. Our goal is to develop a new methodology of identifying fundamental fatigue load (FFL) contents from observed data by using engineering data analytics (EDA) techniques. The proposed methodology is applied to determine a new sensor layout which enables us to capture fundamental structural damage load patterns. Numerical demonstrations and case studies of the proposed method are presented with a common structural component, an I-section cantilever beam, and an industrial large-scale structure, a front linkage of a hydraulic excavator.

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Fig. 3

Worst cycle identified with time points, wc1k and wc2k, via Rainflow cycle counting

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Fig. 2

Overall process of the proposed sensor layout method

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Fig. 1

Stress amplitude and life curve

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Fig. 11

Candidate sensor locations (marked with black squares)

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Fig. 12

Sensor placement result w/fatigue life coverage 98%

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Fig. 4

I-section cantilever beam

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Fig. 5

Stress plot with bending unit load

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Fig. 6

Axial and bending load history data

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Fig. 7

SAE-1020 Steel SN plot

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Fig. 8

Cantilever beam fatigue analysis result

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Fig. 22

Normalized forces and moments of 16 FFLs

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Fig. 9

Five FFLs and their NVC values

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Fig. 10

Pattern-amplified FFLs and their NVC values

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Fig. 13

Case study 1—sensor placement result w/fatigue life coverage 90%

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Fig. 14

Case study 2—sensor placement result w/fatigue life coverage 98%

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Fig. 17

Fatigue life contour with the given load history

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Fig. 18

Initial candidate sensor locations (marked with black squares)

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Fig. 19

Optimum sensor layout for capturing all 16 FFLs

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Fig. 20

Stress contours and deformed shapes with FFL01, FFL04, and FFL08

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Fig. 21

High correlations among the FFL groups

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Fig. 15

General hydraulic excavator and its front linkage boom structure

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Fig. 16

Load history for truck loading application



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