A Comparative Study on Interior Acoustic Comfort Level of Compact Cars using Data Mining Approach

Abstract

Vehicle acoustic comfort is one of the ergonomic measurement criteria that are essential for car occupants. furthermore, interior cabin noise of a car may affect the driver’s concentration when driving. this study is to investigate the noise comfort level of car interior on several compact cars. the objective is to measure interior cabin noise for all three cars and then to compare their acoustic comfort level using subfield data mining approach. a deduction will be made to rate the best car among the three in term of acoustic comfort. the interior cabin noise will be obtained for the cases where engine speed is varied while the cars are in stationary and moving condition. the noise will be assessed according to pre-determined subjective and objective criteria. the sound quality parameters will be assessed by regression analysis. in subjective assessment, the recorded noise is evaluated based on jury assessment. then, the data mining approach is implemented to illustrate the noise level. the collected noise data are divided into five clusters through hierarchical clustering method. to assess the accuracy of noise data clusters, the method of k-nearest neighbours is performed and the results show a high accuracy rate (> 95%). finally, the interior noise of the three cars used is compared by using the analysis of variation. the vehicle acoustic comfort index is produced for the three cars tested in this study. in addition, the acoustic quality among the three cars is presented using anova. annoyance index of the three cars is generated using data mining method. from the results, axia has the best acoustic comfort among of the three cars by objective evaluation. by subjective evaluation, axia recorded the lowest level of annoyance.

Validation of Clicking-type Noise and Vibration in Automotive HVAC System

“…In the 1980s, the sound and vibration of the vehicle became an interesting area that attracted the attention of researchers and manufacturers in the automotive industry. Every component in a vehicle has an explicit function, which also accompanied by the production of distinctive noise and vibration levels [1]. Although these factors were not seriously considered, they eventually defined the customer's comfortability and reliability [2].…”

Exploración de las condiciones del pavimento a partir de la señal acústica producida por un vehículo circulando

“…La textura del pavimento son las desviaciones que presenta la supercie real de vía al ser comparada con una supercie hipotética perfectamente plana (UNE-EN ISO 13473-1:2020). De la textura en sus distintas escalas de dimensión (microtextura, macrotextura y megatextura) dependerán parámetros como la fricción, la resistencia a la rodadura, el control ante los derrapes, el drenaje, el confort en la conducción, el nivel de ruido dentro y fuera del coche, las vibraciones, el consumo de combustible y el desgaste de la banda de rodadura del neumático (PIARC, 1987;Flintsch et al, 2003;Azuddin et al, 2020).…”

“…A continuación, se presentará en detalle las técnicas utilizadas a lo largo de esta investigación y que han sido aplicadas en la literatura de referencia en estudios de ruido de asfaltos. Mediante estas características, la información contenida en la señal sonora de rodadura ha permitido describir e identicar varias condiciones superciales de la vía (Sandberg y Descornet, 1980;Anfosso-Lédée y Do, 2002;Alonso et al, 2014;Nopiah et al, 2015;Vázquez et al, 2020;Azuddin et al, 2020).…”

“…La evaluación considera la variabilidad de los datos de nivel de ruido debido a las condiciones de conducción. Azuddin et al (2020) establecieron un método que determina cinco niveles de confort acústico en el interior del coche mediante el registro sonoro obtenido con micrófonos ins-talados en varias posiciones interiores y que cambiaban debido a la circulación del coche sobre varios tipos de supercies (adoquín, autopistas, y vías urbanas). Un resumen de la literatura previamente detallada se presenta en la Tabla 3.3 1 .…”