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Product development processes generate large quantities of experimental and analytical data. The data evaluation process is usually quite lengthy since the data needs to be extracted from a large number of individual output files and arranged in suitable formats before they can be compared. When the data quantity grows extremely large, manual extraction cannot be done in a limited timeframe. This paper describes a set of tools developed by MTS engineers to automatically extract the desired information from a large number of files and perform data post-processing. The tools greatly improved both speed and accuracy of the evaluation process during the development of a sound quality-based end-of-line inspection system for seat tracks [1]. It allowed engineers to quickly gather a comprehensive understanding of the relative importance of individual design parameters and of their correlation to the subjective perception of the sound quality of the seat track.

The role of NVH test development has changed from addressing a system-level NV concern late in the design cycle (firefighting) to having well established NV optimized test procedures in place. One way this is achieved is by leveraging the information gained during troubleshooting of current product to improve the future product development process for noise and vibration. Today, most NV groups/laboratories use optimized test procedures for creating accurate, consistent, and efficient test results. This still requires expertise to post-process data, compute targets and interpret results to guide product development. This step is often overlooked and, in recent years, due to the lack of NV expertise of “younger” labs (typically in non-automotive industries) or of more established labs affected by the economic downturn (early retirements, lay-offs, especially in the automotive industry) there has been a growing need for automated post-processing “intelligent” procedures.