Micromechanical Studies of 4n Gold Wire for Fine Pitch Wirebonding

This study focuses towards typical micromechanical properties such as strength, yield point, Young’s Modulus, strain, shapes of fracture end and element analysis, atomic percentage of Ca of 4N gold (Au) wire using microstructures and composition observation, micro-tensile test and depth sensing indentation technique. A series of micro-tensile test were performed with different strain rate values of 10˚-10-4 min-1 on to a 25.4 μm diameter plain gold wire. The nanoindentation with 20 mN maximum load was indented on a near fracture end of a gold wire specimen, for which this test was carried out after the micro-tensile test. The stress-strain curves were used to characterize the 4N purity gold wire. The shapes of fracture end of gold wire after micro tensile test were carried out using Scanning Electron Microscopic (SEM). The finding showed that the mechanical properties of ultra-fine gold wire was in the proportional relationship with the increment of the strain rate value. It is suggested that micromechanical behaviour gave the effect for the wirebonding process in order to characterize the wire loop control and strengthen the wire loop to avoid the wire sweep

Characterisation of Stress Intensity Factor with Magnetic Flux Signal Leakage in Stable Fatigue Crack Growth Region S. R. Ahmad ...[et al.]

This paper presents the characterisation of the stress intensity factor range ΔK, with the magnetic flux gradient, dH(y)/dx signals obtained using the metal magnetic memory (MMM) method during fatigue crack growth test. The MMM method is a passive non-destructive testing technique developed for the examination of self-magnetic leakage field signals which were generated in the stress concentration zones. In this paper, the fatigue crack growth test was conducted by applying a constant amplitude loading at different stress ratios. The scanning device and crack opening displacement gauges were used for acquiring the magnetic signals and crack growth parameter, respectively. The relationship between the dH(y)/dx signals, fatigue crack growth rate, da/dN and ΔK was determined. As a result, some similarities were observed between the ΔK and dH(y)/dx signals; wherein both were seen to increase with an increase in the value of da/dN. Furthermore, the analysis of the relationship between dH(y)/dx and ΔK focused on the stable crack growth region and noted that the correlation of determination ranged between 0.9286 - 0.9788. This indicates that dH(y)/dx signals can be used to evaluate the fatigue crack growth of the material

Classification of Fatigue Damaging Segments Using Artificial Neural Network / M. F. M. Yunoh ...[et al.]

This paper focuses on the classification of the fatigue damaging segments datasets associated with the measurement of Variable Amplitude Loadings of strain signals from the coil springs of an automobile during road tests. The wavelet transform was used to extract high damaging segments of the fatigue strain signals. The parameters of the kurtosis, wavelet-based coefficients, and fatigue damage were then calculated for every segment. All the parameters were used as input for the classification analysis using artificial neural networks. Using the back-propagation trained artificial neural network, the corresponding fatigue damages were classified. It was observed that the classification method was able to give 100% accuracy on the classifications based on the damaging segments that were extracted from the training and the validation datasets. From this approach, it classified the level of fatigue damage for coils spring

Fatigue feature classification for automotive strain data

Fatigue strain signal were analysed using data segmentation and data clustering. For data segmentation, value of fatigue damage and global statistical signal analysis such as kurtosis was obtained using specific software. Data clustering were carried out using K-Mean clustering approaches. The objective function was calculated in order to determine the best numbers of groups. This method is used to calculate the average distance of each data in the group from its centroid. Finally, the fatigue failure indexes of metallic components were generated from the best number of group that has been acquired. Based on four data collect from two different roads which are D1, D2, the index value generated is not the same for all of data because due to K-Mean clustering, the best group is different for each of the data used. The maximum indexes generated are different for two types of road and namely the index 4 for D1 and index 5 for D2. Due to the road surface condition, higher distributions of the best groups give higher values of index and reflect to higher fatigue damage experienced by the system

Probability Analysis in Determining the Behaviour of Variable Amplitude Strain Signal Based on Extraction Segments

Abstract This paper focuses on analysis in determining the behaviour of variable amplitude strain signals based on extraction of segments. The constant Amplitude loading (CAL), that was used in the laboratory tests, was designed according to the variable amplitude loading (VAL) from Society of Automotive Engineers (SAE). The SAE strain signal was then edited to obtain those segments that cause fatigue damage to components. The segments were then sorted according to their amplitude and were used as a reference in the design of the CAL loading for the laboratory tests. The strain signals that were obtained from the laboratory tests were then analysed using fatigue life prediction approach and statistics, i.e. Weibull distribution analysis. Based on the plots of the Probability Density Function (PDF), Cumulative Distribution Function (CDF) and the probability of failure in the Weibull distribution analysis, it was shown that more than 70% failure occurred when the number of cycles approached 1.0 x 1011. Therefore, the Weibull distribution analysis can be used as an alternative to predict the failure probability

Fatigue Features Extraction of Road Load Time Data Using the S-Transform

This paper presents the algorithm development of a new fatigue data editing technique using S-T approach. In general, the S-transform (S-T) is a time-frequency spectral localization method which performs a multi-resolution analysis on signal. This method represents a better time-frequency resolution especially for non-stationary signal analysis. This technique was developed to produce shortened fatigue data for fatigue durability testing. The S-T method was applied to detect the damaging events contained in the fatigue signals due to high S-T spectrum location. The damaging events were extracted from an original fatigue signal to produce the shortened edited signal which has equivalent fatigue damage. Three types of road load fatigue data were used for simulation purpose, pave track, highway and country road. In this study, an algorithm was developed, to detect the damaging events in the original fatigue signal. The algorithm can be used to extract the fatigue damaging events and these events were combined in order to produce a new edited signal which neglect the low amplitude cycles. The edited signal consists of the majority of the original fatigue damage in the shortened signal with 15–25% time reduction. Thus, it has been suggested that this shortened signal can then be used in the laboratory fatigue testing for the purpose of accelerated fatigue testin