Investigating the fatigue failure characteristics of A283 Grade C steel using magnetic flux dete

A. Arifin,W.Z.W. Jusoh,S. ABDULLAH,N. Jamaluddin,A. K. ARIFFIN 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.19 No.3

The Metal Magnetic Memory (MMM) method is a non-destructive testing method based on an analysis of the self-magnetic leakage field distribution on the surface of a component. It is used for determining the stress concentration zones or any irregularities on the surface or inside the components fabricated from ferrous-based materials. Thus, this paper presents the MMM signal behaviour due to the application of fatigue loading. A series of MMM data measurements were performed to obtain the magnetic leakage signal characteristics at the elastic, pre-crack and crack propagation regions that might be caused by residual stresses when cyclic loadings were applied onto the A283 Grade C steel specimens. It was found that the MMM method was able to detect the defects that occurred in the specimens. In addition, a justification of the Self Magnetic Flux Leakage patterns is discussed for demonstrating the effectiveness of this method in assessing the A283 Grade C steel under cyclic loadings.

DEVELOPMENT OF OCCUPANT CLASSIFICATION AND POSITION DETECTION FOR INTELLIGENT SAFETY SYSTEM

M. A. HANNAN,A. HUSSAIN,S. A. SAMAD,A. MOHAMED,D. A. WAHAB,A. K. ARIFFIN 한국자동차공학회 2006 International journal of automotive technology Vol.7 No.7

Occupant classification and position detection have been significant research areas in intelligent safety systems in the automotive field. The detection and classification of seat occupancy open up new ways to control the safety system. This paper deals with a novel algorithm development, hardware implementation and testing of a prototype intelligent safety system for occupant classification and position detection for in-vehicle environment. Borland C++ program is used to develop the novel algorithm interface between the sensor and data acquisition system. MEMS strain gauge hermatic pressure sensor containing micromachined integrated circuits is installed inside the passenger seat. The analog output of the sensor is connected with a connector to a PCI-9111 DG data acquisition card for occupancy detection, classification and position detection. The algorithm greatly improves the detection of whether an occupant is present or absent, and the classification of either adult, child or non-human object is determined from weights using the sensor. A simple computation algorithm provides the determination of the occupant’s appropriate position using centroidal calculation. A real time operation is achieved with the system. The experimental results demonstrate that the performance of the implemented prototype is robust for occupant classification and position detection. This research may be applied in intelligent airbag design for efficient deployment.

DEVELOPMENT OF OCCUPANT CLASSIFICATION AND POSITION DETECTION FOR INTELLIGENT SAFETY SYSTEM

Hannan, M.A.,Hussain, A.,Samad, S.A.,Mohamed, A.,Wahab, D.A.,Ariffin, A.K. The Korean Society of Automotive Engineers 2006 International journal of automotive technology Vol.7 No.7

Occupant classification and position detection have been significant research areas in intelligent safety systems in the automotive field. The detection and classification of seat occupancy open up new ways to control the safety system. This paper deals with a novel algorithm development, hardware implementation and testing of a prototype intelligent safety system for occupant classification and position detection for in-vehicle environment. Borland C++ program is used to develop the novel algorithm interface between the sensor and data acquisition system. MEMS strain gauge hermatic pressure sensor containing micromachined integrated circuits is installed inside the passenger seat. The analog output of the sensor is connected with a connector to a PCI-9111 DG data acquisition card for occupancy detection, classification and position detection. The algorithm greatly improves the detection of whether an occupant is present or absent, and the classification of either adult, child or non-human object is determined from weights using the sensor. A simple computation algorithm provides the determination of the occupant's appropriate position using centroidal calculation. A real time operation is achieved with the system. The experimental results demonstrate that the performance of the implemented prototype is robust for occupant classification and position detection. This research may be applied in intelligent airbag design for efficient deployment.

Probabilistic-based fatigue reliability assessment of carbon steel coil spring from random strain loading excitation

C. H. Chin,S. ABDULLAH,S.S.K. Singh,A. K. ARIFFIN,D. Schramm 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.1

This paper aims to assess the fatigue reliability of random loading signals of a suspension coil spring using probabilistic approaches. Strain time histories were acquired while the car was travelling on different road conditions (i.e., in a rural area, in an industrial area, on a university campus, on a highway and on a newly constructed road). Fatigue lives were predicted from the strain histories and fitted into probability density functions. Lognormal distribution was found to be an appropriate way to represent fatigue data. Next, the reliability function and mean-cycles-to-failure (MCTF) were determined. The results indicated that fatigue reliability rapidly deteriorated under rural road conditions, which resulted in a short MCTF of 10 4 cycles. Meanwhile, the new road signals had the longest MCTF of about 10 8 cycles. Accordingly, this is due to the rural road having the most surface irregularities, which caused more severe fatigue damage to the coil spring. This study contributed to a greater in-depth understanding of the effect of loading signals on fatigue reliability. This is essential in determining the appropriate service life of the coil spring during its production to ensure vehicle safety and reduce maintenance costs.

OFF-SET CRACK PROPAGATION ANALYSIS UNDER MIXED MODE LOADINGS

A. E. ISMAIL,A. K. ARIFFIN,S. ABDULLAH,M. J. GHAZALI 한국자동차공학회 2011 International journal of automotive technology Vol.12 No.2

An assessment was carried out herein to study the eccentricity of cracks subjected to mixed-mode loadings. Several loading locations relative to a central line were selected to induce mixed-mode loadings, which were computed using a finite element method. An adaptive meshing technique was adopted during the simulation of crack propagation to ensure the singularity of stress at the tip of the crack. The stress intensity failure criterion was used and programmed, and the node splitting technique was used when the stress intensity factor reached the fracture toughness of the material to simulate crack propagations. It was found that large variations in the stress intensity factor were observed when off-set cracks were used, and that KII decreased when loading distance increased, but increased when the off-set crack distance was increased. Both crack eccentricity and loading distance played important roles in producing mixed-mode loading, compared to the influence of central cracks. Correction factors were introduced to modify the calculation of stress intensity factors under mixed-mode loadings. Simulations of crack propagation were also conducted to study the effects of crack eccentricities and loading distances. It was found that the crack length, the loading distance relative to the central crack and the crack eccentricity dominated calculations of the integrity of cracked structures.

Study of electric vehicle battery reliability improvement

A. Ismail,W. Jung,M. F. Ariffin,S. A. Noor 한국신뢰성학회 2011 International Journal of Reliability and Applicati Vol.12 No.2

Due to restriction of vehicle emissions and high demand for fossil fuels nowadays, car manufacturers around the world are looking into alternative ways in introducing new car model that would vastly captured the market. Thus, Electric Vehicle (EV) has been further developed to take the advantage of the current global issues on price of fossil fuels and impact on the environment. Since car battery plays the crucial role on the overall performance of EV, many researchers have been working on improving the component. This paper focused on the reliability of EV battery which involves recognizing failure types, testing method and life prediction method. By focusing on these elements, the reliability feature being identified and as a result the batteries life will be prolonged.

VEHICLE CRASH ANALYSIS FOR AIRBAG DEPLOYMENT DECISION

A. HUSSAIN,M. A. HANNAN,A. MOHAMED,H. SANUSI,A. K. ARIFFIN 한국자동차공학회 2006 International journal of automotive technology Vol.7 No.2

Airbag deployment has been responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decision. This misfortune has led the authorities and the industries to pursue uniquely designed airbags incorporating crash-sensing technologies. This paper provides a thorough discussion underlying crash sensing algorithm approaches for the subject matter. Unfortunately, most algorithms used for crash sensing still have some problems. They either deploy at low severity or fail to trigger the airbag on time. In this work, the crash-sensing algorithm is studied by analyzing the data obtained from the variables such as (ⅰ) change of velocity, (ⅱ) speed of the vehicle and (ⅲ) acceleration. The change of velocity is used to detect crash while speed of the vehicle provides relevant information for deployment decision. This paper also demonstrates crash severity with respect to the changing speed of the vehicle. Crash sensing simulations were carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes. These toolboxes are also used to validate the results obtained from the simulated experiments of crash sensing, airbag deployment decision and its crash severity detection of the proposed system.

Study of electric vehicle battery reliability improvement

Ismail, A.,Jung, W.,Ariffin, M.F.,Noor, S.A. The Korean Reliability Society 2011 International Journal of Reliability and Applicati Vol.12 No.2

Due to restriction of vehicle emissions and high demand for fossil fuels nowadays, car manufacturers around the world are looking into alternative ways in introducing new car model that would vastly captured the market. Thus, Electric Vehicle (EV) has been further developed to take the advantage of the current global issues on price of fossil fuels and impact on the environment. Since car battery plays the crucial role on the overall performance of EV, many researchers have been working on improving the component. This paper focused on the reliability of EV battery which involves recognizing failure types, testing method and life prediction method. By focusing on these elements, the reliability feature being identified and as a result the batteries life will be prolonged.

VEHICLE CRASH ANALYSIS FOR AIRBAG DEPLOYMENT DECISION

Hussain, A.,Hannan, M.A.,Mohamed, A.,Sanusi, H.,Ariffin, A.K. The Korean Society of Automotive Engineers 2006 International journal of automotive technology Vol.7 No.2

Airbag deployment has been responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decision. This misfortune has led the authorities and the industries to pursue uniquely designed airbags incorporating crash-sensing technologies. This paper provides a thorough discussion underlying crash sensing algorithm approaches for the subject matter. Unfortunately, most algorithms used for crash sensing still have some problems. They either deploy at low severity or fail to trigger the airbag on time. In this work, the crash-sensing algorithm is studied by analyzing the data obtained from the variables such as (i) change of velocity, (ii) speed of the vehicle and (iii) acceleration. The change of velocity is used to detect crash while speed of the vehicle provides relevant information for deployment decision. This paper also demonstrates crash severity with respect to the changing speed of the vehicle. Crash sensing simulations were carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes. These toolboxes are also used to validate the results obtained from the simulated experiments of crash sensing, airbag deployment decision and its crash severity detection of the proposed system.

The Customer Satisfaction Index Model: An Empirical Study of the Private Healthcare Sector in Malaysia

Ahmad Azmi M. ARIFFIN,Norhayati M. ZAIN,Bama V.V. MENON3,Norzalita A. AZIZ 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.1

The main purpose of this study was to gauge the patient satisfaction index and subsequently discuss the Importance-Performance (IP) matrix analysis of the inpatient services in the context of the private hospital setting. The Malaysian Customer Satisfaction Index Model was employed as the theoretical framework for the above purposes. This study involving 242 patients in Malaysian’s private healthcare sector used a Web-based survey as the main method of data collection. Partial least square structural equation modeling (PLS-SEM) was utilized for data analysis. Using Fornell et al. (1996)’s formula, the resulting patient satisfaction index was slightly lower than the “very satisfied” category, the target level required for positioning as one of the world’s premier medical tourism players. The IP matrix showed that medical quality is the main competitive advantage of the private hospitals that can propel their growth in the global healthcare marketplace. The results also indicate that outcome quality, patient rights, and privacy, and service quality are the three quality domains that need to be prioritized for further improvement. On the other hand, the servicescape quality domain needs to be strategized as the unique selling proposition as the performance of the private hospitals in this regard is already extremely good.