Improving Non-Destructive Testing Accuracy Through Regression Analysis

Table of Content

• What is Non-Destructive Testing (NDT)?
• What is Regression Analysis?
• Types of Regression Analysis
• How the Regression Analysis Improves the NDT Accuracy
• Final Thoughts

Non-destructive testing is a technique that plays an important role in measuring the safety, quality, and reliability of the materials. It is used in industry sectors (like aerospace, automotive, construction, Oil & gas, and chemicals) to measure the flaws of materials or components and improve product quality.

Engineers used NDT techniques to inspect the material’s quality without causing damage. They detect flaws before they lead to catastrophic failures. To perform NDT with accuracy is challenging for engineers and researchers.

But here we provide the easiest way to perform NDT and make outcomes more effective by incorporating statistical techniques such as regression analysis. In this used the complex model relationships with different parameters and refined the analysis results.

In this article will discuss how regression analysis may improve the accuracy of NDT with basic concepts of NDT and regression.

Let us briefly introduce, before going into the actual concept, what is non-destructive testing…

What is Non-Destructive Testing (NDT)?

NDT is a group of analysis techniques used in the science and technology industry to evaluate the properties of a material, component, or system without causing damage. The terms nondestructive examination (NDE), nondestructive inspection (NDI), and Non-destructive Evaluation (NDE) are also commonly used to describe this technology. The most common NDT techniques include:

There are many methods used in NDE or NDT for the set of different types of data. Each type requires its own type of training, preparation, and tools. Some of them are given below:

Important Note: 

The choice of NDT Technique depends on the following factors:

Material being inspected

Type of flaw being sought

Accessibility of the object

After learning about NDT, we should have an understanding of regression analysis because it is important to discuss the relationship between them.

What is Regression Analysis?

Regression analysis is a statistical concept that is used to estimate the relationship between a dependent variable (the outcome) and one or more independent variables (predictors).

In the context of NDT: Regression analysis can be used to predict material properties, the likelihood of defects, or the correlation between various NDT parameters (e.g.: signal strength and defect size).

The primary goal of regression is to create an equation or model that can predict the value of the dependent variable based on the known values of the independent variables.

What are the Types of Regression Analysis?

Regression analysis has different types that are suited to different datasets and objectives. Below is an overview of the most common types of regression analysis:

1. Simple Linear Regression

Linear regression establishes a straight-line relationship between independent and dependent variables (y).

Y = a + bx + C

2. Multiple Linear Regression

This type extends linear regression to include multiple independent variables.

Y = a + b1x1 + b2x2 + …. bnxn

How to calculate the linear regression of any data?

To understand the application of regression analysis in non-destructive testing. It’s important to know the basic steps involved in calculating a simple linear regression. This method can help establish a relationship between two variables (signal strength and defect size).

Steps:

1.   Collect the data points for the independent variable (x) and dependent variable (y).

2.   Calculate the mean (average) for x and y.

3.   Determine the slop using the below formula:

4.   Find the intercept: a = Ȳ – bX̄.

5.   Substitute “a” and “b” values to construct a regression equation.

Let’s try to understand these steps with an example

Example:

Let’s consider Ultrasonic Testing, where we measure signal strength (x) to predict defect size (y). The collected data is:

We aim to find the regression equation y = a + bx.

Step-by-step solution:

1.   Calculate the means of x and y:

• X̄ = 100 + 150 + 200 + 250 / 4 = 174
• Ȳ = 4 + 5.5 + 7 + 8.5 / 4 = 6.25

2.   Determine the slop “b” using the above formula:

• b = 375 / 12;500 = 0.03

3.   Calculate the intercept (a).

• a = yˉ​ - bxˉ
• a = 6.25 – (0.03 *175) = 6.25 – 5.25 = 1

4.   From the regression equation by putting “a” and “b” values.

• a = 1 and b = 0.03
• y = 1 + 0.03x

Result:

The regression equation “y =1+0.03x” shows every unit increase in signal strength. The defect size increases by 0.03 units. For instance, if the signal strength is 200 then it predicts the defect size is “7”. Y = 1 + 0.03 (200) = 7.

Pro Tip:

In situations where inspectors are pressed for time or need to process large datasets; manual calculations of regression equations can be time-consuming and error-prone.

To quickly calculate this, an Online Simple Linear Regression Calculator can be an invaluable tool. Input your dataset it will give you slope, intercept, and regression automatically.

How the Regression Analysis Improves the NDT Accuracy 

Regression analysis can be applied to NDT in various ways to improve the accuracy and reliability of test results. Here’s how regression can be utilized:

1. Improves Data Interpretation

NDT Generates Large Amounts of Data; such as signal amplitudes; defect dimensions; or material properties. Regression analysis organizes this data by identifying trends and relationships. It also provides clear insights into the material’s integrity.

2. Reduces Noise Impact

Environmental noise or operator error can distort NDT signals. Researchers can eliminate anomalies and extract precise results from random variations by utilizing regression models.

3. Predicts Defects with Precision

Regression analysis enables predictive modeling. This helps engineers to estimate the likelihood; size; or type of defects based on measurable factors. For example; it can predict crack propagation or corrosion levels based on ultrasonic testing signals.

4. Enhances Decision-Making

By providing precise and predictive data; regression analysis supports engineers in making informed decisions about repair; replacement; or further inspection; ultimately enhancing material safety and reliability.

Final Thoughts

Regression analysis gives a way or method to improve NDT accuracy by quantifying relationships between parameters. Engineers can make better predictions by integrating statistical concepts like regression analysis with NDT. Also; it reduces the risk of undetected flaws and improves overall safety and reliability.