With the ever-decreasing timescales and increased performance requirements afforded to OEM’s, it has become essential that NVH suppliers provide optimum palliative solutions that comply with a vehicles acoustic targets.
The acoustic effect of any palliative treatment attached to a vehicle body system depends on its ability to attenuate noise energy passing through or radiating from the system or its interaction with reflected sound from other areas. Acoustic performance uses targets relating to sound insertion loss (SIL) and / or sound absorption and these are identified to the component supplier by the OEM at the “request for quotation” (RFQ) stage. For many potential suppliers, especially those with a limited portfolio of material options, success or failure is quite straightforward. However, the problem occurs when the material and processing opportunities cover wide parameters and the available combinations and permutations are extensive. It is no longer a simple choice to get the best solution. Ultimately, competitiveness relies on the optimum choice of material types, combinations and processing along with associated cost and this requires a detailed understanding of the ‘physics’ involved. Whilst material prediction software is frequently used to spot check performance prior to actual material testing this technique cannot guarantee success. It is also very time consuming and requires considerable training. The aim of this project was to use “Big Data” to automate the selection process.
This paper describes the Authors work with “Big Data” combined with associated algorithms, so that once a system target is received a range of suitable solutions can be offered without pre-determination of parameters. It covers the creation of the “Big Data” landscapes and the integration of the procedure into a web based easily accessible application.