Machine Learning for Physical Testing

The Role of Machine Learning in Physical Testing

As physical testing capabilities have advanced, it's become possible to gather and analyze vast amounts of data. This has opened the door to leveraging machine learning and AI to optimize testing processes, enhance insights, and improve the overall efficiency and reliability of engineering systems. Despite new capabilities, running physical testing campaigns remains one of the most expensive parts of research and design. By using advanced design of experiments and uncertainty quantification analyses during test design and planning, SmartUQ helps minimize the number of tests required. SmartUQ’s accurate machine learning and optimized data sampling methods can then help maximize the information gained from the test data.

Challenges in Physical Testing

Uncertainty in Measurements

Physical tests inherently involve measurement errors and uncertainties due to various factors like sensor limitations, environmental conditions, and variations in test setups. Quantifying and managing these uncertainties is crucial for drawing meaningful conclusions from test data.

Limited Sample Sizes

Conducting extensive physical tests can be time-consuming, expensive, and sometimes even impractical due to constraints on resources or the availability of test articles. This often results in limited sample sizes, making it difficult to get sufficient coverage of the test space.

Complex Relationships

The behavior of engineering systems can be influenced by a multitude of interacting factors, leading to complex relationships between input parameters and test outcomes. Traditional analysis methods may fail to accurately capture these relationships, reducing the generalizability of results.

Data Management and Integration

Physical testing can generate large volumes of data particularly when time series records from sensors or scans are collected. Effectively managing, integrating, and extracting valuable insights from this data can be difficult.

SmartUQ Solutions for Physical Testing

SmartUQ addresses these challenges using a variety of machine learning tools:

Efficient Sequential Testing

When dealing with small sample size constraints, advanced DOEs and ML models can be used to design and execute sequential testing strategies that intelligently select the next test conditions based on the results of previous tests. These tools maximize the information gained from each test and improve the accuracy of resulting predictive models. Sequential sampling approaches can significantly reduce the number of tests required to achieve desired confidence levels, saving time and resources.

Building ML Models from Test Data

Machine learning algorithms can be trained on test data to build predictive models that capture the complex relationships between input parameters and test outcomes. Using historical tests, these models can be used to predict the performance of new designs, optimize test plans, and identify potential failure modes. With new tests, they can provide greater insight into results though analytics like sensitivity analysis. By quantifying the impact of different input parameters on the system's performance this information can guide design decisions and identify critical parameters that require tighter control during manufacturing and operation. These models can also help others make use of testing results, for example by providing empirical models of system performance or in calibration of physics-based simulations.

Subsampling of Large Data Sets

For tests with larger sample sizes or with large quantities of data from sensor recordings, optimized subsampling can be used to quickly extract representative samples or exclude redundant information. This can reduce the amount of data that needs to be moved, stored, and processed. This makes it possible to efficiently train machine learning models while maintaining accuracy and can even enable real-time predictions during testing.

Uncertainty Quantification

SmartUQ’s Uncertainty Quantification Uncertainty in testing can be handled with uncertainty quantification techniques and integrated into machine learning models to provide confidence intervals or probabilistic predictions. These predictions account for measurement errors and other uncertainties in the test data enabling more informed decision-making and risk assessment based on test results.

Summary

Machine learning and AI have the potential to transform physical testing in engineering applications by enabling more efficient test planning, improved predictive modeling, and enhanced understanding of system behavior under uncertainty. By addressing the challenges associated with physical testing, SmartUQ can reduce the number of tests necessary and increase the information gained from each test, leading to faster development cycles, reduced costs, and increased reliability.

If you are interested in how SmartUQ could apply to your testing challenges, email us at [email protected].