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- 摘要
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- 实验方案
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Analytical Modeling of Residual Stress in Laser Powder Bed Fusion Considering Parta??s Boundary Condition
摘要： Rapid and accurate prediction of residual stress in metal additive manufacturing processes is of great importance to guarantee the quality of the fabricated part to be used in a mission-critical application in the aerospace, automotive, and medical industries. Experimentations and numerical modeling of residual stress however are valuable but expensive and time-consuming. Thus, a fully coupled thermomechanical analytical model is proposed to predict residual stress of the additively manufactured parts rapidly and accurately. A moving point heat source approach is used to predict the temperature ?eld by considering the e?ects of scan strategies, heat loss at part’s boundaries, and energy needed for solid-state phase transformation. Due to the high-temperature gradient in this process, the part experiences a high amount of thermal stress which may exceed the yield strength of the material. The thermal stress is obtained using Green’s function of stresses due to the point body load. The Johnson–Cook ?ow stress model is used to predict the yield surface of the part under repeated heating and cooling. As a result of the cyclic heating and cooling and the fact that the material is yielded, the residual stress build-up is precited using incremental plasticity and kinematic hardening behavior of the metal according to the property of volume invariance in plastic deformation in coupling with the equilibrium and compatibility conditions. Experimental measurement of residual stress was conducted using X-ray di?raction on the fabricated IN718 built via laser powder bed fusion to validate the proposed model.
关键词： residual stress prediction,IN718,additive manufacturing,experimental measurement of residual stress
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) - Singapore (2018.7.16-2018.7.19)] 2018 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) - Defect Prediction Approach to enhance Static Fault Localization of Functional Logic Failure Defects using NIR Photon Emission Microscopy
摘要： Studies on defect induced emission characteristics have significantly enhanced the effectiveness of static fault localization on functional logic failures due to open and short defects. In this paper, using the distinctive differences in the defect-induced emission characteristic between open and short defects, together with layout trace and analysis, a defect prediction approach has been derived. It assisted in the hypothesis of the defect type, narrowing down the defect location within long failure net(s) and even pin-pointing the exact defect location in some cases. Successful case studies on advanced technology node devices were used to describe four different emission signatures of open and short defects and the effective application of aforementioned approach in isolating the defect.
关键词： Static Fault Localization,Short defect,Defect Prediction,Photon Emission Microscopy,Functional failures,Open defect
更新于2025-09-23 15:21:01
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SSC prediction of cherry tomatoes based on IRIV-CS-SVR model and near infrared reflectance spectroscopy
摘要： As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand-held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO-SVR and CS-SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV-CS-SVR model for SSC prediction achieved the accuracy with R2 C of 0.9845. Thus, it is feasible to use NIRS with IRIV-CS-SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes.
关键词： IRIV-CS-SVR model,SSC prediction,cherry tomatoes,near infrared reflectance spectroscopy
更新于2025-09-23 15:21:01
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Using adaptive neuro-fuzzy and genetic algorithm for simultaneously estimating the dye and AgNP concentrations of treated silk fabrics with nanosilver
摘要： Purpose – Arti?cial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the simulation and the modeling of complex problems. The purpose of this study is to estimate the dye and the silver nanoparticle (AgNP) concentrations of silver nanoparticle-treated silk fabrics by the aforementioned methods. Design/methodology/approach – In this study, the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics were matched by using the GA technique based on spectrophotometric color matching. The ANFIS method was also used; this method is based on the grid partitioning algorithm across four different methods. The ?rst and second methods are provided for dye concentration prediction, and the third and the fourth methods are given for AgNP concentration prediction. Findings – The mean of absolute error and root mean square (RMS) of the best dye concentration prediction by the ANFIS method based on the second method are 0.087 and 0.103, respectively. In addition, the mean of the absolute error and the RMS of the best results for AgNP concentration prediction by the ANFIS method by using the third method is 0.002 and 0.003, respectively. The obtained results indicate that the performance of the ANFIS method is better than the GA method. Originality value – The simultaneous prediction of the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics was performed by using the GA and the ANFIS. The suggested method led to acceptable accuracy for color and antibacterial matching.
关键词： Prediction,Silver nanoparticle,Genetic algorithm,Neuro-fuzzy,Dye
更新于2025-09-23 15:21:01
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[Energy, Environment, and Sustainability] Advances in Solar Energy Research || Solar Radiation Assessment and Forecasting Using Satellite Data
摘要： Since the availability of ground data is very sparse, satellite data provides an alternative method to estimate solar irradiation. Satellite data across various spectral bands may be employed to distinguish weather signatures, such as dust, aerosols, fog, and clouds. For a tropical country like India, which is potentially rich in solar energy resources, the study of these parameters is of crucial importance from the perspective of solar energy. Furthermore, a complete utilization of the solar energy depends on its proper integration with power grids. Because of its variable nature, incorporation of photovoltaic energy into electricity grids suffers technical challenges. Solar radiation is subjected to reflection, scattering and absorption by air molecules, clouds, and aerosols in the atmosphere. Clouds can block most of the direct radiation. Modern solar energy forecasting systems rely on real-time Earth observation from the satellite for detecting clouds and aerosols.
关键词： Image processing,GHI,Numerical weather prediction,Forecasting
更新于2025-09-23 15:21:01
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[IEEE 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) - Brasov, Romania (2019.11.3-2019.11.6)] 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) - Analysis on Hotspot Technologies and Cutting-edge Technologies of Organic Solar Cells Based on Patent Data
摘要： In this letter, we investigate the residual link lifetime (RLL) in a mobile ad hoc network, such as a vehicular network. Although, owing to the underlying mobility of the network nodes, the RLLs of adjacent links are highly correlated, yet previous works typically neglected such correlation. In contrast, our study is based on an accurate modeling of the relative distances and speeds between neighboring mobile nodes. Firstly, a scenario is presented that demonstrates the dependence of RLLs of two adjacent links. We then derive the joint probability distribution of the RLLs of two adjacent links in terms of their parameters. Our model shows that neglecting the correlation between adjacent links results in serious overestimation of the path’s lifetime. Simulation is used to verify our model.
关键词： MANET,residual link lifetime,link lifetime prediction,path lifetime prediction
更新于2025-09-23 15:19:57
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Room-Temperature Electrically Pumped InP-based $1.3\boldsymbol{\mu} \mathbf{m}$ Quantum Dot Laser on on-axis (001) Silicon
摘要： We present a method for quantifying a risk for killer defects at layer level and estimating yield for substrate packages using information from design ?les. To calculate risk ranks and predicted yield, we de?ne a risk distance that is a key parameter extracted from designs using image processing techniques. In order to validate our model, we analyze two different designs, each having multiple layers, and compare with data from baseline lots. It is shown that there is an inverse correlation between risk layer ranks and yield. Estimated yield based on our model is compared with baseline yield for four layers of the second design. The model-to-baseline yield difference is less than 1% for three layers we tested.
关键词： yield estimation,assembly,circuit analysis,metrology sampling,Yield prediction,integrated circuit packaging
更新于2025-09-23 15:19:57
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Flexible Spoof Plasmonic Microfluidic Sensor for Detecting Liquid Solutions
摘要： Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon the standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a seven-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported.
关键词： prediction,machine learning,Rehabilitation monitoring,signal processing,wearable sensors
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Improvement of Global Horizontal Irradiance Forecasts from Unified Model over the Korean Peninsula by Using Model Output Statistics
摘要： Solar irradiance was forecasted by the operational numerical weather prediction model in Korea Meteorological Administration. The quantitative evaluation was made against the in situ measurements at 37 ground observing stations. The relative mean bias error values are ranging from -6.9% to 39.9% and then grouped by K-means clustering. For each cluster, the model output statistics are employed to correct the model biases, resulting in the reduction of mean absolute error for global horizontal irradiance forecasts.
关键词： model output statistics,error metrics,numerical weather prediction,solar irradiance forecast
更新于2025-09-23 15:19:57
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Effect of Annealing on The Bottom Cell in GaInP/GaAs/GaInNAsSb Triple Junction Solar Cells by MBE/MOCVD Hybrid Growth
摘要： We present a method for quantifying a risk for killer defects at layer level and estimating yield for substrate packages using information from design ?les. To calculate risk ranks and predicted yield, we de?ne a risk distance that is a key parameter extracted from designs using image processing techniques. In order to validate our model, we analyze two different designs, each having multiple layers, and compare with data from baseline lots. It is shown that there is an inverse correlation between risk layer ranks and yield. Estimated yield based on our model is compared with baseline yield for four layers of the second design. The model-to-baseline yield difference is less than 1% for three layers we tested.
关键词： yield estimation,assembly,circuit analysis,metrology sampling,Yield prediction,integrated circuit packaging
更新于2025-09-23 15:19:57