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Measurement Systems Analysis for Curve Data

Astrid Ruck, Senior Specialist in Statistics, Autoliv B .V . & Co .KG
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
Laura Lancaster, JMP Principal Research Statistician Developer, SAS


Autoliv is the world’s leading manufacturer of automotive safety components such as seatbelts, airbags and active safety systems. Seatbelts significantly contribute to preventing fatalities, so it's important to ensure optimal functionality and comfort. Extraction and retraction forces of seatbelts are important factors that affect both safety and comfort.

Measurement systems analysis (MSA) is a measurement process consisting not only of the measurement system, equipment and parts, but also the operators, methods and techniques involved in the entire procedure of conducting the measurements. Automotive industry guidelines investigate a one-dimensional output per test, but they do not describe how to deal with data curves as output.

In this paper, we take a first step by showing how to perform a gauge repeatability and reproducibility (GR&R) study using force vs. distance output curves.  The Functional Data Explorer in JMP Pro is designed to analyze data that are functions such as measurement curves, as those which were used to perform this GR&R study. 

FDE and Mixed Models platforms are effective and important methods for generalizing MSA studies to curve data. Most of our processes and tests have curves as output. Until now it has been impossible to standardize an MSA procedure using complete curve data. We had to restrict ourselves to using the maximum value of a curve or the area between curves as the measurement output for each test, and we lost a considerable amount of information.   

JMP Pro does an impressive job quickly and simply solving MSA with curve data, a complex and relevant problem. There doesn't appear to be any other publication that discusses this type of MSA generalization for curve data with commercial software.

Please complete the form to the right for immediate access to this white paper (PDF).

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