Where Did All These Reliability Life Models Come From?

Among students beginning their examination of reliability engineering, one question pops up repeatedly: Where did all these reliability life models come from? On one hand, reliability engineering is deeply entrenched in statistical models … Weibull, exponential, etc. But these models alone, do not fully explain the product life models. There is still a missing piece: the Physics of Failure (PoF).

PoF and reliability models are closely connected concepts, as they both relate to the ability of products, processes, and systems to perform their intended function consistently over time.

PoF refers to the scientific principles that govern the behavior of materials and systems under different conditions. It involves the study of how materials and systems fail, and the identification of the root causes of failure. The goal of PoF is to understand how and why materials and systems fail, and to develop strategies to prevent or mitigate these failures.

Reliability models, on the other hand, are mathematical models that are used to predict the reliability of products, processes, and systems over time. These models are based on statistical data and engineering principles, and are used to predict the likelihood of a failure occurring and the time it will take for a failure to occur. Reliability models help users identify and address potential reliability issues, and to develop strategies to improve the overall reliability of their products, processes, and systems.

The development of reliability models was made possible by PoF, as it provided the underlying scientific principles and data that were used to develop and validate these models. For example, data on the failure rates of different materials and components, which was obtained through the study of PoF, was used to develop and validate reliability models that predicted the likelihood of a failure occurring.

PoF also helped to inform the development of reliability models by providing insights into the root causes of failure. By understanding the underlying causes of failure, engineers were able to develop reliability models that accurately predicted the likelihood of a failure occurring, and to identify strategies to prevent or mitigate these failures.

One example of how PoF lead to the development of reliability models is the study of fatigue failure in metals. Fatigue failure is a common mode of failure in metals that occurs when a material is subjected to repeated cyclic loading, resulting in the development of cracks or fractures. PoF includes the study of the factors that contribute to fatigue failure, such as the stress range, the number of cycles, and the material properties.

This fatigue-related failure data was used to develop a general reliability model that could predict the likelihood of fatigue failure occurring in metals under differing conditions. For example, the Wohler curve, more commonly called the S-N curve, is a reliability model that is used to predict the number of cycles to failure for a given stress range in metals. 

The relationship between the stress and the corresponding cycles to failure are depicted mathematically as:

NS^M = A

Where N is the cycles to failure, S is the applied stress, m is the slope of the curve on log-log paper, and A is a constant. Once an S-N curve has been established using actual fatigue test data, a design curve can then be constructed that is useful for predicting product life (in terms of cycles to failure) based on the stress level that the components are expected to encounter in the field.

Effectively, the development of reliability models was a response to the findings of PoF. Reliability models are used to predict the likelihood of failure of a material or structure based on various factors such as its design, the conditions it will be subjected to, and the type of load it will bear. These models then allow engineers and designers to assess the reliability of a material or structure, and to optimize a design of a material or structure for a specific application. By understanding the factors that contribute to the failure of a material or structure, engineers can design structures that are less likely to fail and are more durable over time. This can lead to significant cost savings and improved safety for products and structures that rely on these materials.

Author’s Biography:

Ray Harkins is the Quality and Technical Manager for Ohio Star Forge in Warren, Ohio. He earned his Master of Science from Rochester Institute of Technology and his Master of Business Administration from Youngstown State University. He also teaches manufacturing and business-related skills through the online learning platform, Udemy.

Click on the following coupon codes to receive substantial discounts on his courses, or reach out to him via LinkedIn at linkedin.com/in/ray-harkins or by email at the.mfg.acad@gmail.com.