Failure analysis is used to detect chemical and mechanical defects and deficiencies in polymer products, which have contributed to or directly resulted in failure. It is a critical analytical field that can improve the efficiency and profitability of polymeric products, supporting research and development applications of polymer materials. In severe instances, polymer failure analysis may be required to determine liability in cases of hazardous device failure.
This important analytical discipline typically begins with a forensic evaluation of the point of failure to determine the root cause of flaws and inform the best analytical method for assessing the level of failure, informing corrective actions, and determining whether the failed product was manufactured to required certifications.
Countless extraneous factors could cause polymeric products to degrade, but failure analysis is concerned with establishing the underlying cause of failure within the product. For example, external thermodynamic or chemical stress can cause polymers to wear and eventually break. Failure in response to distinct strains is expected and may be considered acceptable from a liability perspective. However, where a polymer has failed to resist strains within the pre-defined parameters of its application requirements, failure analysis looks to determine the root cause.
Typical root causes of polymer failure include design flaws, application of unsuitable raw materials, the presence of contaminants within the polymeric product. This blog post will explore some of the common analytical methods for failure analysis in further detail.
Failure Analysis with Gel Permeation Chromatography
Gel permeation chromatography (GPC) is most commonly used to determine the polymer molecular weight (MW) of a sample material by standardizing its physical properties against industrial benchmarks. It is also used to assess material degradation for failure analysis of polymeric products by dissolving a small sample of the failed component in a solvent and assessing the molar mass of the analyte through chromatographic measurement. This generally requires an understanding of the molecular weight distribution of the successful end-product for comparison.
Failure Analysis with Stereo Microscopy
Stereo microscopy is a non-destructive method of failure analysis that allows analysts to directly assess cracks in the surface of solid polymer products. It provides low magnification of an analyte material, with a magnifying range of 35 – 90x and two varying viewing angles for each eye. The image can subsequently be viewed three-dimensionally to highlight mechanical defects in greater detail.
Failure Analysis with FTIR-Microscopy
FTIR-microscopy enables analysts to assess the heterogeneity of sample materials to screen for compositional differences that could have contributed towards mechanical failure. Small phase inconsistencies can be detected through microscopic analysis of surface molecules excited by infrared light. The absorption of light is recorded to create chemical map of the surface composition and identify variations in the sample bulk.
Failure Analysis with Mass Spectrometry
Mass spectroscopy refers to a collection of techniques that measure the mass of individual chemical species. It is one of the most important methods of chemical analysis because the mass of a molecule is one of its most defining characteristics. Mass spectroscopy can be used to identify unknown chemicals and to determine their amount. This makes mass spectroscopy a very useful tool for forensic analysis as it enables the determination of the ingredients in a sample and allows comparisons of the amounts. Typical examples of mass spectroscopy techniques include liquid chromatography mass spectroscopy (LCMS), gas chromatography mass spectroscopy (GMCS) and pyrolysis mass spectroscopy (PYMS).
Failure Analysis with Jordi Labs
Jordi Labs is uniquely prepared to provide expert failure analysis to support corrective actions or to inform liability cases. With over 30 years’ experience of product failure analysis, we can accurately represent robust scientific data in a manner that is easy to understand.