How can you identify common failures in aerospace materials?

3 Destructive testing

Destructive testing (DT) is a method to identify failures in aerospace materials by intentionally breaking or damaging the material. It uses various techniques to apply controlled loads, stresses, or strains to the material and observe the resulting behavior or failure. Some common DT techniques are tensile testing, compression testing, bending testing, fatigue testing, and fracture toughness testing. DT can determine the strength, stiffness, ductility, toughness, and fatigue life of the material and how it fails under different conditions. DT can also reveal the microstructure and composition of the material and how they affect the failure mode and mechanism.

4 Failure analysis

Failure analysis (FA) is a method to identify the causes and consequences of failures in aerospace materials. It involves collecting and examining the failed material and any relevant data, such as design specifications, operating conditions, maintenance records, or accident reports. It also involves performing various tests and experiments to reproduce or simulate the failure and analyze the results. FA can identify the root causes and contributing factors of the failure, such as material defects, design flaws, manufacturing errors, environmental effects, or human errors. FA can also evaluate the impact and severity of the failure and recommend corrective or preventive actions.

6 Data analytics

Data analytics (DA) is a method to identify and prevent failures in aerospace materials by using statistical and machine learning techniques to analyze large and complex data sets. It involves collecting and processing data from various sources, such as sensors, monitors, or databases, that capture the material's status, performance, or condition. It also involves applying data analytics techniques, such as descriptive, predictive, or prescriptive analytics, to extract useful information and insights from the data. DA can identify patterns, trends, anomalies, or correlations in the data that indicate or forecast the occurrence or risk of failure. DA can also suggest actions or solutions to improve or maintain the material's quality and durability.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?