Dynamic globularization prediction during cogging process of large size TC11 titanium alloy billet with lamellar structure

The flow behavior and dynamic globularization of TCll titanium alloy during subtransus deformation are investigated through hot compression tests. A constitutive model is established based on physical-based hardening model and phenomenological softening model. And based on the recrystallization mechanisms of globularization, the Avrami type kinetics model is established for prediction of globularization fraction and globularized grain size under large strain subtransus deformation of TC11 alloy. As the preliminary application of the previous results, the cogging process of large size TCI 1 alloy billet is simulated. Based on subroutine development of the DEFORM software, the coupled simulation of one fire cogging process is developed. It shows that the predicted results are in good agreement with the experimental results in forging load and microstructure characteristic, which validates the reliability of the developed FEM subroutine models.     

An experimental assessment of finite-state Predictive Torque Control for electrical drives by considering different online-optimization methods

Finite-State Predictive Torque Control (FS-PTC) is experimentally investigated based on different online-optimization methods by using a two-level voltage source inverter for an induction machine. The calculation time and the switching frequencies are important research points for FS-PTC industrial applications. Long-step FS-PTC methods are expected to improve the performance of the system. However, the calculation time will increase exponentially with the increase of the prediction horizon. A reduced switching frequency PTC (RSF-PTC) method by considering the reductions of the switching frequency and the calculation time is tested. Based on this algorithm, an extended prediction horizon is proposed and verified on a common test bench. A torque-band based PTC (TB-PTC) method is proposed and discussed in this paper. The TB-PTC method pre-calculates the torque error between the predicted torque and the torque reference. The optimization method focuses on the flux error and the switching frequency for switching states which constrain the torque error within the torque-band. The conventional FS-PTC method, the RSF-PTC method with one-step and two-step horizons and the TB-PTC method are developed and experimentally compared in this work. The results confirm that conventional FS-PTC, RSF-PTC and TB-PTC methods can work well in the full speed range. When the switching frequencies and the calculation effort are taken into consideration, the RSF-PTC algorithm shows the better performance. However, the conventional FS-PTC method and the TB-PTC method have better current performance.     

A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—over-sampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates.     

A decision support methodology with risk assessment on prediction of terrorism insurgency distribution range radius and elapsing time: An empirical case study in Thailand

The objectives of this research are to design and develop a practical decision support methodology with efficient prediction tool and risk assessment analysis of a terrorism insurgency situation. The proposed methodology consists of two main parts as: (1) the prediction modelling and (2) risk assessment analysis. The Improvised Explosive Device (IED) incidents from 2007 to 2013 in the capital district of Yala province, the southern part of Thailand, are collected and generated to the methodology as a case study implementation. The proposed methodology is capable of indicating and illustrating the risk assessment prediction results of terrorism insurgency incidents. Furthermore, the demonstration of the Explosive Ordnance Disposal Mobile Unit (EODMU) based upon a Risk Assessment Radar Chart is investigated. In practical terms of applying the proposed methodology, the Thai Government can concentrate on a critical operation zone under a Risk Assessment Radar Chart, resulting in a more accurate operation and leading to a much lower number of casualties.     

Measurement of Bulk Mechanical Properties and Modeling the Load-Response of Rootzone Sands. Part 1: Round and Angular Monosize and Binary Mixtures

The bulk mechanical properties of two different types of rootzone sands (round and angular) were measured using a cubical triaxial tester. Two monosize sands (d 50 = 0.375 mm and 0.675 mm) and their 50:50 binary mixtures (d 50 = 0.500 mm) were studied. The compression, shear, and failure responses of the above-mentioned six compositions were analyzed, compared, and modeled. Two elastic parameters (bulk and shear moduli) and two elastoplastic parameters (swelling and consolidation indices) of the six sand compositions were also calculated and compared. The angular sand was more compressible than round sand during isotropic compression. In addition, the angular sands tended to have lower initial bulk density and high porosity values. Among the three different size fractions, the 0.375 mm mixture was least compressible for both sand shapes. The failure strength and shear modulus of the angular sand were higher than the round sands. In addition, due to their simplicity, phenomenological models were developed to predict the compression and shear behavior of the sands. The prediction models were validated using subangular and subround sands. Average relative difference values were calculated to determine the effectiveness of the prediction models. The mean average relative difference values for compression profiles, i.e., volumetric stress vs. volumetric strain, were from 16 % to 39 %, except for the initial load-response portion (< 1 % volumetric strain). The predictive models were effective in reproducing the failure responses: at 17.2 kPa confining pressure, the mean of average relative difference was 23 %; at 34.5 kPa , the mean difference was 24 %.     

Storage of hybrid rough rice – Consideration of microbial growth kinetics and prediction models

The objective of this study was to determine kinetics of mold growth on popularly grown hybrid long-grain rough rice during storage at conditions that simulate delayed drying and prolonged storage. Three long-grain hybrid rice cultivars, XL753 (2014) and CL XL745 (2014, 2015) and XL760 (2015) conditioned to four different moisture contents (MCs) (12.5%, 16.0%, 19.0%, and 21.0% wet basis) were stored in rough rice form at temperatures ranging from 10 °C to 40 °C for a period of 16 weeks. The study was repeated using rice from two consecutive crop seasons, 2014 and 2015. For all cultivars, a direct relationship between mold counts and rice MC was observed – whereas more complex trends were observed for the effect of temperature and the duration of storage on mold growth. Kinetic models including Baranyi, Weibull, Gompertz, Richard and Buchanan were successfully modified and fitted using non-linear regression and used to predict the mold counts (log₁₀ CFU/g) for varying conditions (correlations = 0.65–0.76). The study concluded that long-grain hybrid rough rice could be stored at low MC levels (≤17%) and moderate temperatures (≤27 °C) for up to 6 weeks without any change in the mold growth profile. However, storing rice at high MC (>17%) for more than 8 weeks, especially at higher (>27 °C) temperatures should be avoided due to the potential for high mold activity leading to loss of the grain quality.     

Phase-evolution in high entropy alloys: Role of synthesis route

Synthesis route can have a significant influence on the process of phase-choice and -evolution in High Entropy Alloys (HEAs). With the objective of instituting awareness, this communication on the foundation of phase diagrams, adopted using the CALPHAD approach, attempts at describing and deciphering the cause for such gradation.     

PWM驱动电机系统传导干扰等效电路模型

采用脉宽调制技术（PWM）驱动的电机系统通过功率变换器对电能进行变换和控制,使得电机系统的性能指标得到较大提高。但是由于功率变换器中的电力电子器件工作在开关状态,具有较大电压和电流变化率的功率脉冲信号产生很强的电磁干扰,对电机系统自身和周围环境产生很大影响。本文针对PWM变频器产生的电磁兼容问题,首先分析了PWM驱动电...     

直缝焊管液压成形极限理论预测模型

直缝焊管广泛应用于汽车车身管状零件液压成形中,焊接区影响着焊管塑性变形规律,准确评价焊管缩颈或破裂现象是工程上倍受关注的问题。基于金相分析法和显微硬度测量法分析高频感应焊管的结构特征,并根据液压成形条件下高频感应焊管的变形特点,提出一种用于计算直缝焊管液压成形极限的理论方法。基于该方法,选用Swift硬化方程和Hill屈服准则推导出直缝焊管液压成形极限理论预测模型,在已知焊管(包含焊接区和基体区)材料性能参数条件下可获得直缝焊管液压成形极限图。运用此理论预测模型,计算出QSTE340高频感应焊管的液压成形极限图。成形极限的计算结果与试验对比表明,二者吻合较好,这证明所建立的直缝焊管液压成形极限的理论预测模型是正确的。     

面向加工特征的工件制造过程能耗预测方法

在工件的制造过程中,具有多个特征的工件在加工时,其加工特征的能耗属性不同,为了研究在工件不同的加工特征对能耗的影响,创建了面向加工特征的工件制造过程能耗预测模型。首先,将工件看成单个加工特征的集合体,其次对工件特征切削功率的分析,创建有关切削功率的能耗数学模型;然后采用GM(1,1)模型完成工件加工时其切削功率的灰色建模,实现整个工件能耗的预测过程,最后通过对冲床顶支座的加工过程为例,说明了该模型的准确性。