Post Doctoral Fellow- Surface Metrology with Applications to Forensic Topography

Summary: We seek to advance surface texture metrology by developing methods and models to characterize and reduce measurement uncertainty, and by providing best-in-class calibrations, reference artifacts, and reference software. One application area is firearm and toolmark identification, where we work with the forensics community to improve the scientific infrastructure for the objective analysis of microscopic toolmarks. We develop and characterize measurement methods, objective comparison methods, and procedures to obtain quantitative statements for the strength of the evidence.

Surface texture metrology: Surface texture affects the properties and functionality of objects, ranging from bearing surfaces in engines to structured surfaces in microelectronics. The project provides best-in- class calibrations, reference artifacts, and reference software for surface roughness, step height, and microform. The SI-traceability of most topography measurements in the U.S. relies on this service. We conduct research into parameters and measurement methods to characterize surface texture for a wide range of applications. We develop methods and standards to characterize and improve measurement uncertainty for different measurement methods, such as contact stylus-based metrology, confocal microscopy, focus variation microscopy, photometric stereo, and coherence scanning and phase shifting interferometry.

Firearm and toolmark Examination: When a firearm is fired, it leaves marks on the cartridge case and bullet. Forensic examiners compare these microscopic marks to determine whether a crime scene bullet or cartridge case was fired from a suspect’s firearm. For more than a century, this evaluation has been subjective, relying on the skill and expertise of the examiner. This approach is under scrutiny because errors in judgement can potentially lead to miscarriages of justice. NIST is working to address these concerns through improved measurement techniques, objective comparison metrics and algorithms, and statistical approaches to obtain quantitative statements for the strength of the evidence with a rigorous treatment of uncertainty.

Examples of projects:

• Artifacts, methods, and models to calibrate optical surface texture measurement instruments and characterize measurement uncertainty, including differences with contact metrology.
• Metrics and algorithms for objective forensic toolmark comparison, including machine learning
• Statistical approaches to quantify the strength of toolmark comparison evidence
• Characterization of toolmark similarity and reproducibility at various scales

Expertise of interest:

• Physics, precision engineering, computer science, and statistics
• Dimensional metrology, surface texture metrology, and measurement uncertainty analysis
• Image analysis, pattern recognition, and machine learning
• Microscopy and optical system analysis
• Pattern evidence forensics

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