Expert Network for Die Casing Defect Analysis

Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid some disadvantages of rule-based expert system. The main objective of the system is to assist die casting engineer in identifying defect, determining the probable causes of defect and proposing remedies to eliminate the defect. 14 common die casting defects could be identified quickly by expert system on the basis of their characteristics. BP neural network in combination with expert system was applied to map the complex relationship between causes and defects, and further explained the cause determination process. Cause determination gives due consideration to practical process conditions. Finally, corrective measures were recommended to eliminate the defect and implemented in the sequence of difficulty.

Investigation of point defect evolution and Voronoi cluster analysis for magnesium during nanoindentation

The present study investigates the effect of nanoindentation on single-crystal magnesium specimens using the embedded-atom method potential in molecular dynamics simulation.Analyses are done under dynamic loading where the load-bearing capacity and change in the structural configuration are studied on the basal(Z-direction)and two prismatic planes(X-and Y-directions)with varying indenter velocities.The investigation of structural evolution is done using atomic displacement analyses to measure the net magnitude of displacement,atomic strain analyses to evaluate the shear strain developed in the process,and Wigner-Seitz defect analyses to calculate the total vacancies at varied timesteps.Furthermore,Voronoi analyses are done when indented on the basal plane to identify the cluster distribution at different planar depths of the specimen.From the analyses,it has been observed that the load-bearing capacity of the specimen varies with the indentation velocity and the direction of indentation on the specimen.Additionally,it is seen that the observed shear and total atomic displacement in the Z-direction is the least in comparison to the other two axes.The partial dislocation 1/3<-12-10>is seen to be majorly present and the population of dislocation loops is more abundant for lower indenter velocities.Furthermore,clusters<0,4,4,6>and<0,6,0,8>are the major indices developed during nanoindentation on the basal plane where they exhibit symmetrical distribution as observed from the Z-direction.

Automatic recognition of defects in plasma-facing material using image processing technology

Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials.This paper presents a method for the automatic recognition of bubbles in transmission electron microscope(TEM)images of W nanofibers using image processing techniques and convolutional neural network(CNN).We employ a three-stage approach consisting of Otsu,local-threshold,and watershed segmentation to extract bubbles from noisy images.To address over-segmentation,we propose a combination of area factor and radial pixel intensity scanning.A CNN is used to recognize bubbles,outperforming traditional neural network models such as Alex Net and Google Net with an accuracy of 97.1%and recall of 98.6%.Our method is tested on both clear and blurred TEM images,and demonstrates humanlike performance in recognizing bubbles.This work contributes to the development of quantitative image analysis in the field of plasma-material interactions,offering a scalable solution for analyzing material defects.Overall,this study's findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions.This method can be employed in a variety of specialties,including plasma physics and materials science.

Photoluminescence Analysis of Injection-Enhanced Annealing of Electron Irradiation-Induced Defects in GaAs Middle Cells for Triple-Junction Solar Cells

Photolumineseenee measurements are carried out to investigate the injection-enhanced annealing behavior of electron radiation-induced defects in a GaAs middle cell for GaInP/GaAs/Ge triple-junction solar cells which are irradiated by 1.8 MeV with a fluence of i ~ 1015 cm-2. Minority-carrier injection under forward bias is observed to enhance the defect annealing in the GaAs middle cell, and the removal rate of the defect is determined with photoluminescenee radiative efficiency recovery. Furthermore, the injection-enhanced defect removal rates obey a simple Arrhenius law. Therefore, the annealing activation energy is acquired and is equal to 0.58eV. Finally, in comparison of the annealing activation energies, the E5 defect is identified as a primary non-radiative recombination center.

Parameters analysis and application of the differential excitation detection technology

A differential excitation probe based on eddy current testing technology was designed. Sheet specimens of Q 235 steel with prefabricated micro-cracks of different widths and of aluminum with prefabricated micro-cracks of different depths were detected through the designed detection system. The characteristics of micro-cracks can be clearly showed after signals processing through the short-time Fourier transform（ STFT）. By changing the parameter and its value in detecting process,the factors including the excitation frequency and amplitude,the lift-off effect and the scanning direction were discussed,respectively. The results showed that the differential excitation probe was insensitive to dimension and surface state of the tested specimen,while it had a high degree of recognition for micro-crack detection. Therefore,when the differential excitation detection technology was used for inspecting micro-crack of turbine blade in aero-engine,and smoothed pseudo Wigner-Ville distribution was used for signal processing,micro-cracks of 0. 3 mm depth and 0. 1 mm width could be identified. The experimental results might be useful for further research on engineering test of turbine blades of aero-engine.

Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology

Defect factors and their relevant rules can be analyzed in depth by processing defect records which are often expressed in the form of text data.However,considering that defect text consists of both structured and unstructured data,it is necessary to excavate structured information from unstructured data.In this paper,a text mining method based on semantic framework technology is introduced to transform unstructured defect description into structured information such as components and defect attributes.Then,a deep analyzing model of a power equipment defect is established,which provides a scheme of defect mining based on historical defect texts.Case studies prove that the proposed deep analysis method has a guiding significance for equipment upgrading,selection and maintenance.

A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Software defect prediction （SDP） is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires sufficient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction （CPDP） using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor （HISNN） method that performs a hybrid classification selectively learning local knowledge （via k-nearest neighbor） and global knowledge （via na/ve Bayes）. Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD （probability of detection） or high PF （probability of false alarm） which is impractical to overall performance as well as high PD and low PF. use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

Software defect prevention based on human error theories

Software defect prevention is an important way to reduce the defect introduction rate.As the primary cause of software defects,human error can be the key to understanding and preventing software defects.This paper proposes a defect prevention approach based on human error mechanisms：DPe HE.The approach includes both knowledge and regulation training in human error prevention.Knowledge training provides programmers with explicit knowledge on why programmers commit errors,what kinds of errors tend to be committed under different circumstances,and how these errors can be prevented.Regulation training further helps programmers to promote the awareness and ability to prevent human errors through practice.The practice is facilitated by a problem solving checklist and a root cause identification checklist.This paper provides a systematic framework that integrates knowledge across disciplines,e.g.,cognitive science,software psychology and software engineering to defend against human errors in software development.Furthermore,we applied this approach in an international company at CMM Level 5 and a software development institution at CMM Level 1 in the Chinese Aviation Industry.The application cases show that the approach is feasible and effective in promoting developers＇ ability to prevent software defects,independent of process maturity levels.