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Effect of inclusion on high cycle fatigue response of a powder metallurgy tool steel

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Journal of Central South University 2012年第7期19卷 1773-1779页

作者：姚君曲选辉 rafi-ud-din 何新波章林 State Key Laboratory for Advanced Metals and Materials(University of Science and Technology Beijing) School of Materials Science and Engineering University of Science and Technology Beijing

The high cycle fatigue response of a high V-alloyed powder metallurgy tool steel （AISI 11） with different inclusion sizes was studied. Two materials of this grade at a similar hardness of about HRC 60 were subjected to axial loading fatigue tests, tensile tests and fracture toughness measurements to investigate their mechanical properties. Large inclusion above 70 ~rn is indicated to be responsible for the tensile fracture which happens before yielding. The fatigue strength obtained up to 107 cycles is found to decrease from approximately 1 538 MPa to 1000 MPa with the inclusion size increasing above 30 Izm. The internally induced crack initiation is mainly attributed to the surface compressive residual stress of 300-450 MPa. Fractographic evaluation demonstrates that the crack initiation and propagation controlling factors of the two materials are almost the same, indicating that the two factors would be insignificantly affected by the inclusion size level. Paris sizes of the two materials both show a tendency to decrease as the ratio of stress intensity factor of crack origin to factor of fish-eye increases. The investigation into the relationship between stress intensity factors and fatigue life of the two materials further indicates that the high cycle fatigue behavior of AISI 11 is controlled by crack propagation.

关键词： powder metallurgy tool steel high cycle fatigue fractography fish-eye inclusion

Study on the impact force and green properties of high-velocity compacted aluminum alloy powder

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International Journal of Minerals,Metallurgy and Materials 2012年第12期19卷 1107-1113页

作者： Xian-jie Yuan Hai-qing Yin rafi-ud din Dil-faraz Khan Xuan-hui Qu State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing 10083 China School of Materials Science and Engineering University of Science and Technology Beijing Beijing 10083 China

High-velocity compaction （HVC） provides an effective means in the field of powder metallurgy （P/M） to reduce the porosity as well as to ameliorate the mechanical properties of products. In this study, the green density of an aluminum alloy is found to be 2.783 g cm 3. The ejection force for the aluminum alloy is in the range of 23 to 80 kN and the spring back is found to be less than 0.40%. The hardness of the green body is in the range of HRB 30 to 70. The bending strength of the green body is in the range of 6 to 26 MPa, which are higher than that of other aluminum alloys prepared by the traditional compaction method.

关键词： aluminum alloys powders powder metallurgy compaction impact mechanical properties

Impact of light intensity on flowering time and plant quality of Antirrhinum majus L. cultivar Chimes White

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Journal of Zhejiang University Science 2004年第4期5卷 400-405页

作者： MUNIRMuhammad JAMILMuhammad BALOCHJalal-ud-din KHATTAKKhalidRehman FacultyofAgriculture GomalUniversityD.I.KhanPakistan

Shades of different light intensities (29%, 43%, 54%,60% or 68%) along with control (no shade) were studied to observe their effects on the flowering time and plant quality. A hyperbolic relationship was observed between different light intensities under shade,and time to *** total number of flower buds showed a curvilinear relationship with light intensities. Growth parameters related to the plant characteristics such as plant height,leaf area and plant fresh weight were improved under shading treatments at the expense of flowering time and number of flower ***,both linear and polynomial models applied assumed that cultivar Chimes White was equally sensitive to light intensity throughout development.

关键词： Antirrhinum majus L. Snapdragon Growth and development Flowering Light intensity.

Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

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Big Data Mining and Analytics 2024年第1期7卷 205-216页

作者： Aafaq Mohi ud din Shaima Qureshi Department of Computer Science and Engineering National Institute of Technology SrinagarSrinagar 190006India

Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of *** study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural *** research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck *** also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate *** experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these *** findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.

关键词： Graph Neural Networks(GNNs) learning on graphs over-smoothing over-squashing isotropic-GNNs anisotropic-GNNs

Assessment of seismic hazard of roller compacted concrete dam site in Gilgit-Baltistan of northern Pakistan

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Earthquake Engineering and Engineering Vibration 2021年第3期20卷 621-630页

作者： Shahzada Khurram Perveiz Khalid Jahanzeb Qureshi Zia ud din Institute of Geology University of the PunjabLahore 54590Pakistan Department of Space Science University of the PunjabLahore 54590Pakistan

The proposed site of the Diamer Bhasha Dam in northern Pakistan is situated in an active tectonic zone with intensive seismicity,which makes it necessary for seismic hazard analysis(SHA).Deterministic and probabilistic approaches have been used for SHA of the dam *** Main Mantle Thrust(MMT),Main Karakaram Thrust(MKT),Raikot-Sassi Fault(RKSF)and Kohistan Fault(KF)have been considered as major seismic sources,all of which can create maximum ground shaking with maximum potential earthquake(MPE).Deterministically estimated MPE for magnitudes of 7.8,7.7,7.6,and 7.1 can be produced from MMT,MKT,RKSF and KF,*** corresponding peak ground accelerations(PGA)of 0.07,0.11,0.13 and 0.05 g can also be generated from these earthquakes,*** deterministic analysis predicts a so-called floating earthquake as a MPE of magnitude=7.1 as close as 10 km away from the *** corresponding PGA was computed as 0.38 g for a maximum design earthquake at the project ***,the probabilistic analysis revealed that the PGA with 50%probability of exceedance in 100 years is 0.18 ***,this PGA value related to the operational basis earthquake(OBE)is suggested for the design of this project with shear wave velocity(V_(s30))equal to 760 m/s under dense soil and soft rock conditions.

关键词： seismic hazard analysis peak ground acceleration Gilgat-Biltastan Diamer Bhasha Dam

Human Verification over Activity Analysis via Deep Data Mining

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Computers, Materials & Continua 2023年第4期75卷 1391-1409页

作者： Kumar Abhishek Sheikh Badar ud din Tahir Senior Member IEEESeattle98103USA Department of Software Engineering Capital University of Science and Technology(CUST)Islamabad44000Pakistan

Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and *** human interaction using RGB images is one of the most complex machine learning tasks in recent *** models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB *** paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based *** use human interaction image sequences as input data,we first perform a few denoising ***,human-to-human analyses are employed to deliver more precise *** phase follows feature engineering techniques,including diverse feature ***,we used the graph mining method for feature optimization and AdaBoost for *** tested our proposed HVAA model on two benchmark *** testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)*** second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event *** proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.

关键词： AdaBoost classification deep features mining graph mining human detection human verification

PREPARATION OF THERMALLY EVAPORATED CdTe THIN FILMS

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Chinese Physics Letters 1990年第11期7卷 510-513页

作者： Shahzad Naseem Naseer-ud-din Khadim Hussain Centre for Solid State Physics University of the PunjabQuaidCampusLahorePakistan

Thin films of cadmium telluride were prepared by evaporating cadmium telluride powder in a vacuum of less than 10^(-4) *** substrate temperature was varied systematically from room temperature to 300℃.The results of optical and structural characterizations have revealed that the films posses a high value of absorption coefficient with a bandgap value m the range 1.475-1.769eV,and that the films are of cubic structure with a preferred orientation along(111)*** probe measurements have shown that resistivity of these films is affected by the change in substrate temperature,with the lowest value(12.6 Ω·cm)being at 200℃.

关键词： films value cadmium

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

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Computers, Materials & Continua 2023年第1期74卷 965-981页

作者： Hamayun Khan Irfan ud din Arshad Ali Sami Alshmrany Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor *** the advancement of technology,the performance management of central processing unit(CPU)is *** densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip *** energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life *** this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power *** selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)*** in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more *** proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple *** performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for *** are running and accurately scheduled to make an energy-efficient

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

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Petroleum Research 2024年第3期9卷 393-408页

作者： Saud Qadir Khan Farzain ud din Kirmani Mari Petroleum Company Limited IslamabadPakistan Department of Petroleum and Gas Enginering NFC.Institute of Engineering&Technology MultanPakistan

Parametric understanding for specifying formation characteristics can be perceived through conven-tional ***,attributes of reservoir lithology are practiced for hydrocarbon *** logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation ***,manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for ***,in this study,Deep Neural Network(DNN)has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring *** model has been developed for predicting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons,number of layers,optimizer,learning rate,dropout values,and activation *** of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training,validation and ***,an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class *** is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification *** model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracyas well as a high F1-score.

关键词： Lithology identification Deep learning LSTM Imbalanced dataset Conventional well logs