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Chapter 6: Problem 49

Cite five factors that lead to scatter in measured material properties.

Short Answer

Expert verified
Answer: The five factors that can cause scatter or variation in measured material properties are experimental errors, sample preparation, material inhomogeneity, testing conditions, and human errors.

Step by step solution

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1. Experimental Errors

Experimental errors are inevitable in any testing process and can lead to scatter in measured material properties. These errors may arise from inaccuracies in the equipment or measurement instruments used, such as force sensors, pressure gauges, or length measuring devices. To minimize experimental errors, it is essential to calibrate and maintain the testing equipment regularly.
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2. Sample Preparation

The way a sample is prepared can significantly impact the measured material properties. For example, the sample's surface condition, size, and shape can all influence the test results. Surface roughness, cracks, or other defects can lead to stress concentrations and affect the material's strength or fatigue performance. To reduce scatter, it is important to prepare samples consistently, following standard procedures or specifications.
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3. Material Inhomogeneity

Materials are rarely completely homogeneous, and variations in the material's structure, composition, or internal defects can cause scatter in the measured properties. Examples include variations in grain size, porosity, or chemical composition in a metal alloy, which can affect the strength, ductility, and other properties. Testing multiple samples can help identify the influence of material inhomogeneity on the measured properties.
04

4. Testing Conditions

The environmental and experimental conditions under which a material is tested can significantly affect its measured properties, leading to scatter. These factors include temperature, humidity, and loading rate in a mechanical test. To achieve consistent and reliable test results, it is necessary to control these testing conditions and ensure they remain consistent throughout the experiment.
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5. Human Errors

Human errors can also lead to scatter in measured material properties. These could include mistakes in sample preparation, incorrect data recording, or misinterpretation of the test results. Proper training, documentation, and quality control procedures can help minimize the impact of human errors on the test results. In summary, scatter in measured material properties may arise from experimental errors, sample preparation, material inhomogeneity, testing conditions, and human errors. By recognizing and addressing these factors, it is possible to reduce scatter and improve the reliability of test results.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Experimental Errors
In the realm of material testing, experimental errors are one of the primary contributors to the scatter in measured properties. This type of error reflects the intrinsic unpredictability when dealing with sensitive measurements.
Imagine setting up a test with a force sensor. If that sensor is not calibrated correctly, the force it measures could be off, leading to questionable results. Calibration is like the warm-up before a sprint – skipping it can mean the difference between a record-breaking time and a stumble out of the blocks.
  • Regular calibration ensures the equipment runs like a well-oiled machine.
  • Maintenance and check-ups prevent the sneaky drifts in accuracy over time.
Even the most vigilant scientist can't completely eliminate these errors, but they can surely tame them with diligence and precision.
Sample Preparation
When preparing a specimen for testing, the adage 'well begun is half done' holds true. Sample preparation plays a critical role in the reliability of material property measurements.
If a sample has a surface as rough as a gravel road, or an imperfection hidden like a fault line waiting to cause trouble, these small inconsistencies can greatly skew results. A well-prepared sample sets the stage for accurate measurement, creating conditions for the performance to follow as predicted.
  • Smooth, consistent sample conditions can prevent stress concentrations that affect the results.
  • Using standard procedures for sample preparation levels the playing field for all tests.
By giving each sample the same careful preparation, testers can ensure that the only variables in the experiment are those they actually want to test.
Material Inhomogeneity
Like the diverse ingredients in a stew, the material inhomogeneity within a sample adds complexity to any measurement.
Consider a metal alloy. Just as the flavor of a stew can change with each spoonful, depending on the mix of ingredients it scoops up, the strength or ductility of the alloy can vary depending on the size and distribution of its grains, its porosity, or its chemical makeup. In between the uniform grains could lie the renegade elements that throw off the results.
  • Testing a batch of samples can shed light on this inner inconsistency.
  • Acknowledging the natural variety within materials helps in predicting their behavior under stress.
Testing Conditions
Imagine trying to read a book during a rock concert. That's a bit like measuring material properties under inconsistent testing conditions. For accurate results, you need a steady and predictable environment.
Whether it's the temperature, which could sway a material's flexibility like mood swings, or humidity, which could impact a material like a rainy day can affect your plans, control over environmental factors is key.
  • Stable testing conditions equal less variance in results.
  • Uniformity in the experimental setup means fewer surprises in the data.
With a steadfast testing environment, materials can show their true colors, undistorted by the whims of the surroundings.
Human Errors
Lastly, we come to the most unpredictable factor of all: ourselves. Human errors are missteps in the delicate dance of material testing, like accidentally stepping on your dance partner's toes.
Whether it's the scientist who misspeaks a number, jotting down 16 instead of 61, or mixes up samples like socks in the laundry, these blunders can muddle the results. But fear not, for with the vigilant eyes of quality control and the guiding hand of proper training, these pitfalls can be mitigated.
  • Documentation is the roadmap that keeps everyone on the right path.
  • Double-checking data is like proofreading a novel for plot holes before publication.
The human element, vulnerable to error, is also innately capable of growth, learning, and improvement – turning this potential liability into a pillar of reliable testing.

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Most popular questions from this chapter

(a) A 10 -mm-diameter Brinell hardness indenter produced an indentation \(2.50 \mathrm{mm}\) in diameter in a steel alloy when a load of \(1000 \mathrm{kg}\) was used. Compute the HB of this material. (b) What will be the diameter of an indentation to yield a hardness of \(300 \mathrm{HB}\) when a \(500-\mathrm{kg}\) load is used?

For a brass alloy, the stress at which plastic deformation begins is \(345 \mathrm{MPa}(50,000 \mathrm{psi})\) and the modulus of elasticity is 103 GPa \(\left(15.0 \times 10^{6} \mathrm{psi}\right)\). (a) What is the maximum load that may be applied to a specimen with a cross- sectional area of \(130 \mathrm{mm}^{2}\left(0.2 \text { in. }^{2}\right)\) without plastic deformation? (b) If the original specimen length is \(76 \mathrm{mm}\) \((3.0 \text { in. }),\) what is the maximum length to which it may be stretched without causing plastic deformation?

A steel alloy to be used for a spring application must have a modulus of resilience of at least \(2.07 \mathrm{MPa}(300 \mathrm{psi}) .\) What must be its minimum yield strength?

A cylindrical specimen of a nickel alloy having an elastic modulus of \(207 \mathrm{GPa}\left(30 \times 10^{6}\right.\) psi) and an original diameter of \(10.2 \mathrm{mm}\) \((0.40 \text { in. })\) will experience only elastic deformation when a tensile load of \(8900 \mathrm{N}\left(2000 \mathrm{lb}_{\mathrm{f}}\right)\) is applied. Compute the maximum length of the specimen before deformation if the maximum allowable elongation is \(0.25 \mathrm{mm}\) \((0.010 \text { in. })\).

In Section 2.6 it was noted that the net bonding energy \(E_{N}\) between two isolated positive and negative ions is a function of interionic distance \(r\) as follows: $$E_{N}=-\frac{A}{r}+\frac{B}{r^{n}}\quad\quad\quad (6.25)$$ where \(A, B,\) and \(n\) are constants for the particular ion pair. Equation 6.25 is also valid for the bonding energy between adjacent ions in solid materials. The modulus of elasticity \(E\) is proportional to the slope of the interionic force-separation curve at the equilibrium interionic separation; that is, $$E \propto\left(\frac{d F}{d r}\right)_{r_{0}}$$ Derive an expression for the dependence of the modulus of elasticity on these \(A, B,\) and \(n\) parameters (for the two-ion system) using the following procedure: 1\. Establish a relationship for the force \(F\) as a function of \(r,\) realizing that $$F=\frac{d E_{N}}{d r}$$ 2\. Now take the derivative \(d F / d r\). 3\. Develop an expression for \(r_{0},\) the equilibrium separation. since \(r_{0}\) corresponds to the value of \(r\) at the minimum of the \(E_{N^{-} \text {versus- } r}\) curve (Figure \(2.8 b\) ), take the derivative \(d E_{N} / d r,\) set it equal to zero, and solve for \(r\) which corresponds to \(r_{0}\). 4\. Finally, substitute this expression for \(r_{0}\) into the relationship obtained by taking \(d F / d r\).
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