Fatigue analysis from market data

In summary, the company guarantees that the component will last for 700 days. However, the component fails by fatigue on the average 540 days. The company wants to know how long the component will last by using a basic theory simulation.
  • #1
chandran
139
1
I am working on a project for a company to analyse the fatigue life of a component. There is an existing component that the company
manufactures and the component fails by fatigue on the average 540 days. The component is a tube made of low carbon steel of yield 260N/sqmm and ultimate of 340N/sqmm. But the company gives a guarantee of 700 days to the customers. How can i simulate
by theory the fatigue life of 540 days for that component.
 
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  • #2
What exactly do you mean by "simulate by theory?" I'm a little lost at that phrase.
 
  • #3
Yes. Simulate by basic theory.
 
  • #4
minger said:
What exactly do you mean by "simulate by theory?" I'm a little lost at that phrase.

I recall there are two ways:

-Using Paris equation, which is equated with experimental coefficients.

-Using FEM.

For a rapid calculation see first case.
 
  • #5
Would also consider how you define "fatigue life" of the component, i.e. whether it consists of crack initiation and/or propagation, if only the latter then Paris law is the best way to go, if former is included methods of "classical" fatigue analysis come into play. Depending on the complexity of your component I think you can have decent enough estimates using "desktop" solutions, such as the IIW rules and there are some general lower bounds for Paris law coefficients (or Nasgro if a more general form of FCP law is required) depending on type of material and under what conditions the fatigue occurs.
 
  • #6
can anyone help with classical fatigue analysis
 
  • #7
Found these general intros to different aspects of fatigue analysis - one by D. Socie and another lecture paper:

http://www.mie.uiuc.edu/content/files/FCP%202001%20Basic%20Short%20Course/4%20Analysis.pdf
http://www.engr.ku.edu/~rhale/ae510/fatigue.pdf

... good starts in familiarizing the different approaches and concepts, I think a classical 'stress - life' / S-N approach might do it in this case (?).
 
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Related to Fatigue analysis from market data

1. What is fatigue analysis from market data?

Fatigue analysis from market data is a statistical method used to analyze trends and patterns in market data to understand the level of fatigue or exhaustion in a particular market. It involves analyzing data such as sales figures, consumer behavior, and competition to identify potential areas of weakness or opportunities for improvement.

2. Why is fatigue analysis important?

Fatigue analysis is important because it helps businesses and researchers identify market trends and patterns that may be affecting their performance. It can also help identify potential areas for growth or improvement, as well as potential risks and challenges in the market.

3. What are the steps involved in fatigue analysis from market data?

The steps involved in fatigue analysis from market data typically include data collection, data cleaning and preparation, data analysis using statistical methods and software, and interpretation of the results. It may also involve identifying and addressing any biases or limitations in the data.

4. What are the benefits of using fatigue analysis from market data?

Some of the benefits of using fatigue analysis from market data include gaining insights into market trends and patterns, identifying potential areas for improvement and growth, making data-driven decisions, and staying competitive in the market. It can also help mitigate risks and uncertainties by providing a better understanding of the market.

5. Are there any limitations to fatigue analysis from market data?

Like any statistical analysis method, fatigue analysis from market data has its limitations. It relies heavily on the quality and accuracy of the data collected and may not be able to account for all factors that may affect the market. It is important to interpret the results carefully and consider other factors before making any decisions based on the analysis.

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