DESIGN OPTIMIZATION OF THE INTERIOR VEHICLE NOISE AND VIBRATION THROUGH COMPUTATIONAL APPROACHES

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DESIGN OPTIMIZATION OF THE INTERIOR VEHICLE NOISE AND VIBRATION THROUGH

COMPUTATIONAL APPROACHES

by

ADEL MOHAMMED MOHAMMED AL-DHAHEBI (1532111782)

A thesis submitted in fulfillment of the requirements for the degree of Master of Science in Engineering Mathematics

Institute of Engineering Mathematics UNIVERSITI MALAYSIA PERLIS

2017

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UNIVERSITI MALAYSIA PERLIS

NOTES : * If the thesis is CONFIDENTIAL or RESTRICTED, please attach with the letter from the organization with the period and reasons for confidentiality or restriction. Replace thesis with dissertation (MSc by Mixed Mode) or with report (coursework)

DECLARATION OF THESIS

Author’s Full Name : ADEL MOHAMMED MOHAMMED AL-DHAHEBI Date of Birth : 13 August 1987

Title : DESIGN OPTIMIZATION OF THE INTERIOR VEHICLE NOISE AND VIBRATION THROUGH

COMPUTATIONAL APPROACHES

Academic Session : 2016/2017

I hereby declare that this thesis becomes the property of Universiti Malaysia Perlis (UniMAP) and to be placed at the library of UniMAP. This thesis is classified as:

CONFIDENTIAL (Contains confidential information under the Official Secret Act 1997)*

RESTRICTED (Contains restricted information as specified by the organization where research was done)*



OPEN ACCESS I agree that my thesis to be published as online open access (Full Text)

I, the author, give permission to reproduce this thesis in whole or in part for the purpose of research or academic exchange only (except during the period of _______ years, if so requested above)

Certified by:

SIGNATURE SIGNATURE OF SUPERVISOR

04283781 DR. AHMAD KADRI JUNOH

(NEW IC NO. /PASSPORT NO.) NAME OF SUPERVISOR Date: 30 September 2017 Date: 30 September 2017

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ACKNOWLEDGMENT

First of all, I would like to express my profound and sincere gratitude to Almighty Allah for granting me health, strength and giving me the opportunity to complete my thesis.

Undertaking this Master has been a truly life changing experience for me and it would not have been possible to do without the support that I received from many people. I would like to acknowledge people who so generously contributed to the work presented in this thesis.

First and foremost, my special thanks and profound gratitude goes to my enthusiastic and energetic supervisor DR. AHMAD KADRI JUNOH for his outstanding and organized supervision throughout the journey to complete my thesis. He has taught me, both consciously and un-consciously, how good thesis, measurement of vibration and noise and analysis are done. I appreciate all his contributions of time, ideas, and kindness to make my Master experience productive and stimulating. The joy and enthusiasm he has for his research was contagious and motivational for me, even during tough times in the Master pursuit. My Msc has been an amazing and challenging experience and I thank DR. AHMAD KADRI wholeheartedly, not only for his tremendous academic support, but also for giving me so many wonderful opportunities.

I would like to take this opportunity to express my profound gratitude to Prof. AMRAN BIN AHMED for enlightening me with the necessary statistical knowledge to analyse my data in an efficient way. He has tremendous knowledge and experiences in this field.

His kindness, enthusiasm and good works are unbeatable, Terima Kasih PROF.

I would like to take this opportunity to express my thank my beloved family for their constant support and motivations throughout my entire life especially my mother HAMAMAH HAMID AL-DHAHEBI and my father MOHAMMED MOHAMMED AL-DHAHEBI. They are the backbone of my life and their presence in my life has made things easier, encouraged me to study hard and gave me the resilience and strength to carry on even when things went extremely hard.

I would like to extend my appreciation to the Institute of Engineering Mathematics department as a whole staff and lecturers for providing the perfect research environment and for their kind assistant and friendly management. It was privilege knowing them and their kindness and assistance are well acknowledged.

Last but not least, I would like to acknowledge some people whom knowing them was life changing experience FASIAL ALI, MAGED MOHAMEMD, MOATH ALUAICI EDRES HAMOOD and ABD AL-DHAHEBI.

Thank you so much!

Adel Mohammed Al-Dhahebi

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TABLE OF CONTENTS

PAGE

DECLARATION OF THESIS i

ACKNOWLEDGMENT ii

TABLE OF CONTENTS iii

LIST OF TABLES vii

LIST OF FIGURES ix

LIST OF ABBREVIATIONS xi

LIST OF SYMBOLS xiii

ABSTRAK xiv

ABSTRACT xv

CHAPTER 1 INTRODUCTION 1

1.1 Introduction 1

1.2 Research Problem Statement 6

1.3 Research Objectives 8

1.4 Research Scope 8

1.5 Research Significance 9

1.6 Research Organization 10

1.7 Summary of Chapter 1 12

CHAPTER 2 LITERATURE REVIEW 13

2.1 Background 13

2.2 Sound Quality 14

2.3 Effects of Noise and Vibration 16

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2.4 Sources of Noise and Vibration 20

2.4.1 Engine 22

2.4.2 Tire-road Interaction 24

2.4.3 Type of Road 28

2.5 Evaluation of the Acoustics Level 30

2.6 Evaluation of Vibration Level 35

2.7 Clustering and Classification Algorithms Methods 38

2.8 Summary of the Literature Review 45

CHAPTER 3 METHODOLOGY 46

3.1 Introduction 46

3.2 Methodology Flowchart 46

3.3 Experimental Design 50

3.3.1 Data Acquisition. 50

3.4 Evaluation of Noise Level 55

3.5 Evaluation of Vibration Level 58

3.6 Basic Statistical Analysis 60

3.7 Noise Isolation Index (NII) 63

3.8 Acoustical Comfort Clustering 66

3.9 Interior Comfort Optimization 68

CHAPTER 4 RESULT AND DISCUSSION 71

4.1 Introduction 71

4.2 Sound Quality Results 72

4.3 Vibration Exposure Results 76

4.4 Statistical Analysis Results 77

4.5 Noise Isolation Index (NII) 86

4.6 K-means Clustering Algorithm 89

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4.6.1 Clustering Results on Highway Road 89

4.6.2 Clustering Results on Pavement 91

4.6.3 Clustering Results on Urban 92

4.6.4 Clustering Results on Idle condition 93

4.7 Genetic Algorithm 95

CHAPTER 5 Conclusion 100

5.1 Introduction 100

5.2 Recapitulation of Research Objectives 100

5.3 Summary of Research Findings 102

5.3.1 Noise and Vibration Assessment 102

5.3.2 Noise Isolation Index 102

5.3.3 GA Optimization Model 103

5.4 Implications to Theory and Practice 103

5.4.1 Theoretical Implications 104

5.4.2 Practical Implications 104

5.5 Limitations of the Study 105

5.5.1 Experimental Limitation 105

5.5.2 Content Limitations 105

5.5.3 Geographical Limitations 106

5.6 Suggestions for Future Research 106

5.7 Conclusion 107

REFERENCES 109

APPENDIX A: SOUND QUALITY RESULTS 119

APPENDIX B: VIBRATION EXPOSURES RESULTS 128

APPENDIX C: CLUSTERING RESULTS 133

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APPENDIX D: GENETIC ALGORITHM 138

LIST OF PUBLICATIONS 150

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LIST OF TABLES

NO. PAGE

Table 2.1: Summary of noise effects on humans 17

Table 2.2: The sound quality parameters 32

Table 3.1: Locations and charactersitics of tested road surfaces 52 Table 3.2: Locations of the microphones and accelerometers 54

Table 3.3: Engine speeds (rpm) employed in the test 55

Table 3.4: Description of Sound quality parameters 56

Table 3.5: The Noise Isolation Index comfort range 65

Table 3.6: The comfort levels characteristics 68

Table 4.1: The values of the sound quality parameters form the measured sounds on the

highway road for Axia 73

Table 4.2: The values of the sound quality parameters form the measured sounds on

highway road for Myvi 74

Table 4.3: The values of the sound quality parameters form the measured sounds on the

highway road for Viva 75

Table 4.4: The vibration exposures of Axia on the highway road surface (m s/ ²). 76 Table 4.5: The vibration exposures of Myvi on the highway road surface(m s/ ²). 77 Table 4.6: The vibration exposures of Viva on the highway road surface (m s/ ²) 77 Table 4.7: Correlation coefficents of loudness (dashboard) with the vibration exposure

values of Axia on highway road surface 78

Table 4.8: Correlation coefficents of loudness, sharpness, sound pressure and engine

speeds for Axia on highway road surface 79

Table 4.9: Multiple linear regression of sharpness and loudness with the sound pressure:

Multicollineary test 81

Regression statistics. 81

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Regression coefficients 82

Residuals statistics 82

Table 4.13: Multiple linear regression of loudness and vibration exposures: Multi-

collinearity test 85

Table 4.14: Multiple linear regression of loudness and vibration exposures: Regression

Statistics 85

Table 4.15: Multiple linear regression of loudness and vibration exposures: Regression

coefficients 86

Table 4.16: Noise Isolation Index (NII) results for Axia, Myvi on highway, pavement, urban and idle (stationary) driving conditions. 87 Table 4.17: Clustering mean results for Axia on highway road surfaces 90 Table 4.18: Clustering results for Myvi on highway road surfaces 90 Table 4.19: Clustering results for Viva on highway road surfaces 91 Table 4.20: Comparison of comfort level of Axia, Myvi and Viva on highway road 91 Table 4.21: Comparison of comfort level of Axia, Myvi and Viva on pavement road

surface 92

Table 4.22: Comparison of comfort level of Axia, Myvi and Viva on urban road

surface 93

Table 4.23: Comparison of comfort level of Axia, Myvi and Viva on idel condition 93 Table 4.24: Genetic algorithm results of Axia on highway road surface 96

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LIST OF FIGURES

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Figure 2.1: A basicentic axes that can be used to measure vibration 19 Figure 2.2: A biomechanical representation model for human body 20 Figure 2.3: Various sources of noise and vibration in vehicles 21 Figure 2.4: Tires A and B with different surface patterns (Nopiah et al., 2014) 25 Figure 2.5: The forces acting on the tire (Kongrattanaprasert, 2010) 26 Figure 2.6: Radial tire construction principles (Arteaga, 2014; Kongrattanaprasert, 2010) 27 Figure 2.7: Tested road surfaces a) highway and b) pavement (Nopiah et al., 2014) 28 Figure 2.8: Types of road surfaces: a) highway, b) pavement (Nahvi et al., 2009) 29 Figure 2.9: Index booming flowchart using artificial neural network-based (S. K. Lee,

2008) 33

Figure 2.10: The experimental system procedure (Yildirim & Eski, 2008) 39 Figure 2.11: Measurement set up using Mannequin for acoustics data collection (Paulraj

et al., 2010) 40

Figure 2.12: Measurement set up using Mannequin for acoustics data collection (Xiao

et al., 2013) 42

Figure 3.1: Overall flow chart of this study 49

Figure 3.2: Road surfaces types at Perlis, Malaysia 52

Figure 3.3: Microphones (blue dots) locations and Accelerometer locations (represented

by the red dots) 53

Figure 3.4: The sound quality parameters for highway condition, recording at 1000

rpm 57

Figure 3.5: Genetic algorithm process flowchart 70

Figure 4.1: Residuals plot of the predicted sound pressure (indicates normal distributions;

p>0.05) 83

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Figure 4.2: Scatterplot of standardized predicted sound pressure vs standarized

residuals 83

Figure 4.3: Noise Isolation index comparison between Axia, Myvi and Viva on highway,

pavement, urban and idle driving conditions 88

Figure 4.4: Line plot of Axia, Myvi and Viva fitnesses on highway, pavement, urban

and idle conditions 97

Figure 4.5: The graph representing the fitness function of the genetic algorithm of Axia

on highway road surface 98

Figure 4.6: The graph representing the fitness function of the genetic algorithm of Myvi

Figure 4.7: The graph representing the fitness function of the genetic algorithm of Viva

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LIST OF ABBREVIATIONS

NII Noise Isolation Index MLR Multiple Linear Regression

GA Genetic Algorithm

RPM Revolutions Per Minute

BSS Blind Sources Separation

SVM-GA Support Vector Machine- Genetic Algorithm BPNN Back Propagation Neural Network

PLS Partial Least Square

CPX Close Proximity

SPL Sound Pressure Level

VACI Vehicle Acoustical Comfort Index

WT Wavelet Transform

DWT Discrete Wavelet Transform

SMWVD Sound Metric based on the Wigner-Ville Distribution BS-6841,1987 British Standard (Guide to measurement of vibration) IOS-2631,1997 International Organization for Standardization

RMS Root Mean Square

PSO-NN Particle Swarm Optimization Neural Network ANN Artificial Neural Network

LSSVM Least Square Support Vector Machine

GA-SVM Genetic Algorithm Optimized Support Vector Machine WPSE Wavelet Packet Sample Entropy

WPE Wavelet Packet Energy

GA-BPNN Genetic Algorithm-Back Propagation Neural network PSO Particle Swarm Optimization

PSO-BPNN PSO-Back Propagation Neural Network

DBN Deep Belief Network

CRBM Continues Restricted Boltz-Man Machine

CRBM-DBN Continues Restricted Boltz-Man Machine- Deep Belief Networks WPT Wavelet Packet Transform

EMD Empirical Mode Decomposition

KNN K-Nearest Neighbor

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SVM Support Vector Machine

GANN Genetic Algorithm based Neural Network

VDV Vibration Dose Value

JMP Johns’ Macintosh Project/Product LDA Linear Discriminant Analysis

NI National Instrument

LabVIEW Laboratory Virtual Instrument Engineering Workbench cDAQ Compact Data Acquisition

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LIST OF SYMBOLS

dB Decibel, unit measurement of sound level Hz Hertz, unit measurement of frequency

x Front to back axis

y Side to side axis

z

The longitudinal (head to toe) axis

ax Vibration magnitude x axis,

ay Vibration magnitude in the y axis

az The vibration on the longitudinal axis [ 2]

aSUM m The sum of the whole body vibration

a The acceleration value



^{m s}^/ ²



^,

( )

a t Acceleration frequency time T Time period (in seconds)

L The sound quality parameter loudness [sone],

c

Constant

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Pengoptimuman Rekabentu Hingar Dan Gegaran Dalam Kenderaan Melalui Pendekatan Berpengiraan

ABSTRAK

Kebolehupayaan hingar dan gegaran merupakan kriteria yang amat diperlukan bagi proses rekabentuk dan validasi bagi kenderaan memandangkan ianya memberi kesan ketara kepada imej pemasaran kenderaan automatif. Matlamat utama bagi kajian ini adalah untuk membuat penilaian dan mengoptimumkan tahap akustik dan gegaran di dalam kabin dalaman kenderaan. Untuk tujuan itu, isyarat hingar dan gegaran diperolehi dengan menggunakan tiga kereta kompak Perodua iaitu Axia, Myvi dan Viva dalam jenis pemanduan yang berkeadaan pegun dan tidak pegun (lebuh raya, jalan turapan dan jalan bandar). Hingar dan gegaran yang diukur dianalisa untuk memperolehi kualiti bunyi dan tahap pendedahan gegaran di dalam kabin. Selain itu, kajian mempersembahkan satu formulasi Index Pengasingan Hingar (IPH) berdasarkan kepada pengkategorian trend- trend kualiti bunyi bagi hingar di dalam dan di luar kereta. IPH dibangunkan, dibandingkan dan ditentusahkan untuk tiga jenis kereta yang diuji dan keputusan menunjukkan bahawa Axia mempunyai keselesaan yang paling tinggi dan kriteria pengasingan yang optimum terhadap hingar dari luar kabin. Selain menjalankan pengelompokan untuk mengelompok dan membandingkan keselesaan dalaman bagi Axia, Myvi dan Viva, kajian membangunkan Algoritma Genetik untuk selanjutnya mengoptimumkan hingar dan gegaran di dalam kabin kenderaan. Keputusan bagi model Algoritma Genetik mendedahkan bahawa amaun hingar di dalam kabin dipengaruhi oleh nilai gegaran yang terdedah. Secara keseluruhannya kajian ini menunjukkan bahawa tahap keselesaan adalah dengan nyatanya dipengaruhi oleh faktor-faktor termasuk jenis permukaan jalan, transmisi enjin dan ciri-ciri rekabentuk kenderaan.

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Design Optimization Of The Interior Vehicle Noise And Vibration Through Computational Approaches

ABSTRACT

Noise and vibration performance is an indispensable criterion in vehicle design and validation processes since it significantly affects the marketing image of automotive vehicles. The primary goal of this study is to assess and optimize the acoustic and vibration levels in the interior vehicle cabin. The study demonstrated experimental design to acquire sound and vibration signals using three local compact Perodua cars namely Axia, Myvi and Viva on idle (stationary) and non-stationary (highway, pavement and urban) driving conditions. The measured sounds and vibrations are analyzed to obtain the sound quality and vibration exposure levels in the interior cabin. Apart from this, the study presents a formulation of Noise Isolation Index (NII) based on categorizing the trends of sound quality into interior and exterior noises. The NII is developed, compared and validated for the three tested cars and the results showed that Axia has the highest comfort and thus optimal isolation criterion toward the exterior noises. Besides conducting clustering to compare the interior comfort of Axia, Myvi and Viva, this study developed Genetic Algorithm to further optimize the noise and vibration in the interior vehicle cabin. The results of GA model revealed that the interior noise is influenced by vibration exposure values in the interior vehicle cabin. Overall findings of this study indicate that the comfort level is factually influenced by factors including type of road surface, engine transmissions and vehicle design characteristics.

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1 CHAPTER 1

INTRODUCTION

1.1 Introduction

Vehicle interior sounds and vibrations are important factors of customer satisfaction and have a decisive influence on the automotive product and its quality impression. Indeed, as automotive products become competitive, customers sort their selection in a discriminating and differentiating way based on the perception of sound quality that the vehicles make (Hanouf, Faris, & Nor, 2015; Nopiah, Junoh, & Ariffin, 2015b; Zhang, Hou, Shen, Shi, & Zhang, 2015).

In the competitive automotive markets, there are various factors which influnce the consumer’s preferences when selecting an automtoive car such as fuel consumptions, handling, comfort, safety and cost (Duan, Wang, & Xing, 2015). Recently, it has become imperative to connect customer requirements and vehicle’s technical specifications to develop a competitive automotive vehicle with high quality and pleasant sound (Stylidis, Wickman, & Söderberg, 2015). As a consequence, various testing procedures with enhanced tools have been developed to direct and enable car manufacturers to improve the interior vehicle comfort. Undoubtedly, noise and vibration performances are essential parameters during the entire process of vehicle designs (Huang, Li, Lim, & Ding, 2016;

Panza, 2015).

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The combination of globalization and increased competition in the automobile marketplaces requires well optimization of vehicle’s noise and vibration characteristics to produce pleasant vehicles that match costumer’s expectations and to remain competitive (Yoon, Yang, Jeong, Park, & Oh, 2012). In fact, nowadays with advancements in technology and improved socioeconomic conditions, the expectations of customers for a comfortable vehicles have increased leading to stiffer competition among automotive manufacturers. Nonetheless, the future of automotive industries depends on many factors including integrating the new technologies with car’s designs, the material choices of the interior structures must be revisited and producing cost effective and sustainable cars.

Basically, noise is the unwanted sound that propagates through a medium with a particular magnitude and frequency. Meanwhile, the vibration refers to the displacement of the body at specific reference and time. Although there are various noises in the vehicle cabin, they are classified based on their transmission into two categories; airborne and structure borne noises. The airborne noises refer to the noises produced due the interaction of a moving vehicle with the air volume. Meanwhile, the structure borne noise is caused by the structures of vehicles such as system transmission, suspension, exhaust and body structural and interior acoustic resonances.

In the context of acoustics, the sound quality refers to the noise, vibration and harshness of sounds in the interior vehicle cabin. While the noise and vibration can be measured, the harshness refers to the perceived vibration that can be adequately assessed by subjective feelings of the driver and passenger. The concept of sound quality refers to

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the objectives measures used for predicting the human’s subjective preferences toward noise (Lyon, 2000). It is influenced by physical sound, psychoacoustics (human perceptions) and psychological (evaluation of the perceived sounds) (Genuit, 2004).

Apart from this, human ear perceives sounds in a complex process related to the physics of human hearing whereby the perceived sound is not exactly similar to the emitted one (Xing, Wang, Shi, Guo, & Chen, 2016). Furthermore, the perceptions of customers toward vehicle’s sound quality is dependent on their expectations and context.

The expectations refer to the cultural demographic and experiences, and the context refers to the situations or environments where the sound is perceived.

In vehicle design, there is no clear definition or an indication to which the phenomena of sound quality is addressed by noise or vibration. In fact, depending on how the phenomena are perceived by the driver or passenger they are considered either as noise or vibration. However, most of the noise vibration phenomena occurs at 100 Hz and 500 Hz which are the frequency range that signifies the airborne and structure-borne noise and vibration. While high frequency vibrations can degrade comfort and sustain local effects, low frequency vibrations have considerable impact on health such as the vibrations in the range of 4-6 Hz causes cyclic motions and resonance to the body, and vibrations up to 12 Hz impact all human organs (Hostens, Papaioannou, Spaepen, &

Ramon, 2003).

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As a matter of fact, repetitive exposure to noise and vibrations causes serious health issues including stress, muscles fatigue, mental health issues, sleep disorder, and deafness at higher annoyance levels. Indeed, the transmission of vibration to a driver or passenger has significant influences on their performance, comfort and health (Howart &

Griffin, 1990; Paddan & Griffin, 2002). In some cases, long-term exposure to a vibrating components such as seats, wheels, and flooring systems can lead to Whole Body Vibration (WBV). The severity of noise vibration depends on the magnitude, frequency, duration, direction, body size, vibration motion, body posture, composition and body tension to which the exposure occurs (Genuit, 2004).

Over the years, assessing the sound quality in the interior vehicle cabin has received significant consideration from automotive researches and engineers. As a result, various studies have been conducted to evaluate, improve and predict the sound quality in vehicles (Nor, Fouladi, Nahvi, & Ariffin, 2008; Panza, 2015; Tan, Wang, & Li, 2011;

X. Wang, 2010; Y.S. Wang, Shen, & Xing, 2014; Xiao, Wang, Shi, & Guo, 2013). The most common approach to predict sound quality is the Jury subjective test which enables ranking the noise in a qualitative scale. However, the Jury assessment is costly, time consuming and produces irreproducible results as it relies on the subjective impressions made by the human subjects which obviously varies between individuals and are difficult to be represented in quantitative scale (Faris, BenLahcene, & Hasbullah, 2012). In addition, engineering designs requires exact answers and procedures to predict noise, and hence the qualitative impressions made by subjects alone are not enough to assess and improve vehicles’ noise vibration characteristics.

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Consequently, the psychoacoustic parameters including loudness, sharpness, roughness and fluctuation strength have been developed to illustrates the dimensions of sounds in terms of subjective descriptors without the necessity to conduct the Jury test (Fastl & Zwicker, 2006). These objective measures have been utilized by various researchers in an attempt to evaluate the vehicle interior noise (Leite, Paul, & Gerges, 2009; Nopiah, Junoh, & Ariffin, 2013c; Y.S. Wang et al., 2014). Moreover, though these metrics, multiple algorithms and approaches have been developed and extended, yet the sound quality in vehicles remains a challenging task in automotive industries.

This study proposes a novel formulation of Noise Isolation Index to evaluate acoustical comfort in vehicles. Unlike the previous studies which focused on only one or two types of the interior noises, this study aims to consider both interior and exterior sounds to evaluate the overall acoustics level in the interior vehicle cabin. The proposed index is based on categorizing the sounds into interior and exterior based on the experimental design and data collection procedure. The NII index is developed and validated for three local compact cars namely, Axia, Myvi and Viva. Apart from this, this study also conducted clustering process to cluster the noise and vibration exposures in order to determine and compare the level of comfort in the test cars. Besides that, this study also developed Genetic Algorithm model to optimize the noise and vibration in the interior vehicle cabin of the three test cars. In addition, statistical models have been carried out to validate and compare the results of comfort in each car. The study also aims to determine the impact of road surfaces, engine transmission and type of vehicle on the overall comfort produced in the interior cabin.

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6 1.2 Research Problem Statement

Due to the ever increasing demands for suppressing and reducing undesirable noise and vibrations in the vehicle interior cabin, various testing procedures and tools have been developed to optimize vehicle characteristics, predict sound quality and assess the noise and vibration (Duan et al., 2015; Hanouf et al., 2015; Huang et al., 2016; Kužnar, Možina, Giordanino, & Bratko, 2012; Nor et al., 2008; Stylidis et al., 2015; Y. S. Wang, 2009; Wanger & Kallus, 2015; Xiao et al., 2013; Zhang et al., 2015).

In addition, under the conditions that the human perceptions toward experienced sounds and vibrations are complex and hard to be estimated, various methods and models have been proposed over the years such as the psychoacoustic metrics (Leite et al., 2009;

Yan Song Wang, Shen, Guo, Tang, & Hamade, 2013), statistical models mainly correlation and regression (Kim, Lee, & Na, 2010), the neural network (Yoon et al., 2012), support vector machine (Shen, Zuo, Li, & Zhang, 2010; Xiao et al., 2013), the particle swarm optimization neural networks (PSO-NNs) (Ding, Li, Su, Yu, & Jin, 2013), back propagation neural networks, BPNN (Y.S. Wang et al., 2014), and more recently the genetic algorithm optimal support vector machines (GA-SVMs) (Huang et al., 2016).

However, the fact remains that there is not a universal index which enables assessing the noise and vibration in the interior vehilce cabin. The specific limitation of the proposed neural networks is that, they are shallow and have no hidden layer which means the solutions produced are restricted and the methods proposed for assessing the noise vibration in the interior cabin should be investigated further (Huang, Li, Yang, Lim,

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& Ding, 2017). Unlike the previous studies which focused on only one or two types of noises, it is necessary to develop a powerful and holistic approach that can specify all sounds in vehicles and include all human feelings toward the perceived sounds (Huang, Li, Huang, Lim, & Ding, 2016).

Despite the success of various methods, the more effective prediction of vehicle interior sound quality remains problematic and challenging. Based on the above studies and considerations, the problem statements are:

i. The interior noise vibration is a multi-dimensional phenomena composed of various sources, different and integrated transmission paths, speed changing and ergonomics. Thus, identifying and controlling such phenomena is still a major challenge to vehicle designers and automotive engineers.

ii. The necessity to develop and validate holistic methods and sound quality prediction techniques to assess the interior and exterior sounds and to determine the acoustics comfort in vehicles’ cabin thus enabling automotive industries to remain successful and competitive.

iii. There is no specific device that could test the level of comfort in the interior vehicle cabin commercially. This is because understanding the dimensions of sound quality have not been achieved as it requires developing a universal taxonomy of the perceived quality. Thus the methods proposed for assessing the interior noise and vibration should be investigated further.

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8 1.3 Research Objectives

The specific objectives of this study are:

i. To develop acoustical comfort index called Noise Isolation Index (NII) to evaluate the acoustical comfort in the vehicle cabin by considering the psychoacoustics measures of the exterior and interior sounds.

ii. To cluster noise and vibration using K-mean clustering algorithm in order to compare the interior comfort level of Axia, Myvi and Viva on stationary (idle), non-stationary (highway, pavement and urban) driving conditions.

iii. To develop Genetic Algorithm (GA) model to obtain the comfort level in the interior vehicle cabin. GA aims to search for the promising loudness and vibration values which produce minimum noise in the interior vehicle cabin.

1.4 Research Scope

The particular scope of this study is to assess the acoustical comfort in the interior vehicle cabin of the three local compact cars namely Axia, Myvi and Viva on both stationary (idle) and non-stationary (highway, pavement and urban) conditions. However, the scope is not limited to the following sentences:

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i. To revise the previous works on sound and vibration to identify the factors which influence the discomfort in the interior vehicle cabin. Identifying the factors that influence the level of discomfort in vehicles is critical step toward developing the necessary measures to reduce and control these sources and or tuning them to be pleasant.

ii. To determine the impact of different road surfaces on influencing the acoustical comfort levels in the interior vehicle cabin. The scope of this is limited to the tested roads locations (highway, urban and pavement) which are located at (Changlun-Arau highway, Pauh Putra, near DK1 pavement road and Kampong Arau area urban). It is also limited to the tires used during measurements which are 175/65R14, 175/65R14 and 165/55R14 for Axia, Myvi and Viva respectively.

iii. The scope also limited to the points of measurements of both sounds and vibrations which are front left (interior and exterior sides), right (interior and exterior) and front dashboard for sounds, front and rear floors, front and back dashboards for vibrations.

1.5 Research Significance

The primary purpose of this study is to optimize the vehicle interior noise and vibration to improve the interior comfort level. Overall, the significances of this study are:

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