Typical Failure Types Research Articles

Experimental investigations on the failure characteristics of the slit-contained circular opening under biaxial compression: Insights into the rockburst prevention

The slit-cut method for the rockburst prevention and control is believed effective with its easiness in operation, adjustment and compatibility. However, there is limited advance knowledge of the physics of the slit-cut method, which is vital for the engineering designs. In this study, the biaxial compression tests with a synchronous AE (acoustic emission)-DIC (digital image correlation) monitoring are creatively carried out on the slit-contained circular opening specimens with different slit configurations to demonstrate the academic thoughts and mechanisms of the slit-cut method. The experiments document the two typical failure types namely the internal crack propagation and the dynamic rockburst, which occupy different AE hit rate characteristics and different entropy properties. With the occurrence of rockburst, the AE hit rate presents a bouncing ascend-descend trend, and a higher disorder and chaos is faithfully exhibited. Depending on the slit parameters, the slit-cut method can efficiently mitigate rockburst in terms of the occurrence frequency and magnitude. The underlying mechanism lies in the enhancement of the shear mechanism and the development of the internal cracks through which the stored energy can be greatly dissipated. However, due to the unrestricted shear failure in the slit-contained opening specimens, the opening-scale inward instability can be triggered by the internal crack coalescence, thus posing a threat to the safety of the opening. Implementing the slit-cut method with a consideration of the in-situ stress conditions is evidently essential for the safe excavation.

재생 태양광모듈의 발전량 예측에 관한 연구

Photovoltaic(PV) systems have been installed for 20 years in Korea to reduce green gas. An accumulated installation is amount of 9.3GW in 2019. Solar module is one of the most important factors of solar power system. If these solar modules do not reused, severe environmental pollution can be caused because the amount of disposal PV modules will be same as the amount of new installations. Many countries make an effort to find this regeneration solution and their life extension. In this paper, we propose the method to simulate typical failure types for disposal modules and predict the amount of power generation by finding regenerated correction factor for these reused modules.

Recognition of track defects through measured acceleration - part 1

For an optimized maintenance strategy, the early detection of track defects is necessary. Mounted sensors (e.g. acceleration sensors) on in-service trains are very suitable for track monitoring. With the continuous measurement of axle-box acceleration, short wavelength defects can be identified. For example, these defects can be rail breaks or cracks (i.e. rail defects), or local instabilities. Local instabilities can reduce the track quality in a short period of time. For an efficient data analysis of the acceleration signal and classification of different track defects, the development of appropriate methods is necessary. Therefore, a track-vehicle scale model was built to generate acceleration data used to detect typical types of failures. With the generated acceleration data, developed algorithms for pattern recognition can be easily tested. In the first part of this research, the vertical acceleration signals generated by the rail defects and local instabilities are collected, analysed, classified and prepared for being used in a model that can automatically identify these failures. The data is collected in a track-vehicle scale model, and after data analysis, the characteristics of the waveforms associated with each failure are examined using cross correlation. Every failure is classified both manually as well as automatically, and statistical features of the waveforms are extracted to create a database that is used to train a model using supervised learning. This model is described in the second part of the research.

Recognition of track defects through measured acceleration - part 2

For an optimized maintenance strategy, the early detection of track defects is necessary. Mounted sensors (e.g. acceleration sensors) on in-service trains are very suitable for track monitoring. With the continuous measurement of axle-box acceleration, short wavelength defects can be identified. For example, these defects can be rail breaks or cracks (i.e. rail defects), or local instabilities. Local instabilities can reduce the track quality in a short period of time. For an efficient data analysis of the acceleration signal and classification of different track defects, the development of appropriate methods is necessary. Therefore, a track-vehicle scale model was built to generate acceleration data for typical types of failures. With the generated dataset, developed algorithms for pattern recognition can be easily tested. In the second part of this research, three models created by the supervised learning method are trained and tested for the detection of the local instability in the vertical acceleration signal. The model A is trained with 78 laps and uses a manual classification. The chosen classifier for the model is a bagged tree algorithm implemented in the software MATLAB. The developed models distinguished between no failure, rail defect and local instability. For the training process of the model, the measured acceleration is treated statistically (e.g. Root-mean-square, Standard deviation, Spectral peaks and power). Subsequently test data for different scenarios is generated and used in the prediction model. With this model the track defects in the track-vehicle scale model are detected and classified very reliably. In contrast to existing methods, a machine learning approach is used for the non-destructive detection of the local instability. The results of the model are also improved by the model B and C by using 139 laps as training data, an automatic classification and an optimization of the statistics. The knowledge gained, can be used for acceleration data from in­service trains in regular operation, by adapting the developed model.

Formation of layered fracture and outburst in stratums

Layered fracture and outburst are two typical types of failures in a stratum that occur in many cases, such as liquefaction of sand, mining of coal, and dissociation of gas hydrate. The formation of both the failures is analyzed using the mathematical model based on the three-phase media. It is found that both failures occur once the pore pressure is high enough to cause the crashing and breakage of the skeleton. In other words, the formation conditions are related to the tensile strength of the stratum and the overburden. The fracture expands and shrinks along with the penetration and drainage of pore water and gas; outburst stops gradually as the energy dissipates in the stratum.

Numerical and experimental confirmation of the calibration stage's effect in multi-operation sheet hydroforming using poor-formability materials

Based on the proposed multi-operation hydroforming with the non-uniform blank holding pressure unit, using the poor formability materials including aluminum alloy and high-strength steel alloy, the effects of key process parameters including the shape of pre-forming punch, pre-forming depth, blank shape and cavity pressure, punch movement on the finally formed parts were discussed. Typical failure types including fracture and wrinkling were studied. By using the numerical simulation method, and process parameters were optimized. It was proved that the results from the experiment and the simulation were in a reasonable agreement.

Investigation of the separation of 3D-structures with undercuts

The separation of masters with 3D undercut structures from thin spin-coated layers of PDMS (Sylgard 184) on Si was investigated. In order to identify typical failure types and to figure out potential limitations imposed by the material, masters with different undercut widths were prepared. Imprint was chosen as the replication technique, where only thin base layers remain on the substrate below the 3D undercut structures. Two typical failure types were observed during separation, neck failure and base layer failure. It was found that surface quality of the master is a major impact factor. Small defects in the surface of the PDMS induced by the master result in large area tear-off of the undercut replica during separation. Defect-free masters could be successfully imprinted and separated with base layer thicknesses down to about 1 μm for undercut widths of up to 8 μm.

Study on hydromechanical deep drawing with uniform pressure onto the blank

Sheet hydroforming has many virtues and has gained increasing interest in industries recently. In this paper, hydromechanical deep drawing (HDD) with uniform pressure onto the blank is proposed and investigated in detail both primarily in experiments and simulation. Final cups with drawing ratios of 2.46 and 3.11 were, respectively, obtained using Al–Mg–Si alloy (Al6016-T4) and soft aluminum (Al1050-H0) that have the strong anisotropic properties. The influence of process parameters on the sheet deformation was studied and the typical failure types including fracture and wrinkling were discussed. Finally, FEM was used to analyze the forming process, and the results from the FEM simulations were in good agreement with those from the experiments.

Real-time furnace modeling and diagnostics

Precise control of process temperature has become increasingly important in today's semiconductor industry. Multizone batch furnaces are used widely in current manufacturing lines, and high reliability of furnace systems is a crucial factor in achieving high product yield. However, uncertainty caused by sensor noise and failure may degrade reliability. In this work, the authors develop a methodology based on thermal modeling and sensor fusion techniques to detect temperature sensor failures, power supply failures, and system faults for the multizone furnace systems. The typical types of failures have been defined. The impact of single failures and different combinations of failures on the system behavior has been studied. The furnace system has been modeled based on both physical considerations and experimental data extraction. The fault detection methodology has been tested in simulations. Principal component analysis is utilized for choosing data types for different fault detection purposes. Sensor fusion is used to enhance reliability. Simulation results show that all different types of failures can be detected when data are rich enough. Experimental results show that all single failures and some of the failure combinations can be estimated when only steady-state and cooling-down data are utilized.