20MnCr5圆棒锯切困难原因分析

某厂在锯切20MnCr5圆棒过程中锯条容易断裂，采用光学显微镜(OM)和扫描电子显微镜(SEM)等观察分析手段，对锯条容易断裂的原因进行分析。分析结果表明：锯条断裂的原因为加工20MnCr5过程中粘刀崩齿。针对此类加工缺陷，提出了相应的改进措施。     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Electron transfer mediator PCN secreted by aerobic marine Pseudomonas aeruginosa accelerates microbiologically influenced corrosion of TC4 titanium alloy

Titanium alloys possess excellent corrosion resistance in marine environments,thus the possibility of their corrosion caused by marine microorganisms is neglected.In this work,microbiologically influenced corrosion (MIC) of TC4 titanium alloy caused by marine Pseudomonas aeruginosa was investigated through electrochemical and surface characterizations during a 14-day immersion test.Results revealed that the unstable surface caused by P.aeruginosa resulted in exposure of Ti2O3 and severe pitting corrosion with maximum pit depth of 5.7 μm after 14 days of incubation.Phenazine-1-carboxy[ate (PCN),secreted by P.aeruginosa,promoted extracellular electron transfer (EET) and accelerated corrosion.Deletion of the phzH gene,which codes for the enzyme that catalyzes PCN production,from the P.aeruginosa genome,resulted in significantly decreased rates of corrosion.These results demonstrate that TC4 titanium alloy is not immune to marine MIC,and EET contributes to the corrosion of TC4 titanium alloy caused by P.aeruginosa.     

基于可变临界孔隙度Nur模型的页岩气层横波速度预测

准确的横波测井速度是影响地震叠前属性分析及反演质量的重要参数，在缺乏偶极横波测井情况下需要用Krief、Pride等数学模型预测横波，近年来临界孔隙度模型在计算骨架的弹性模量中应用效果较好。在前人研究的临界孔隙度模型的基础上，发展出了可变临界孔隙度的Nur模型，并将遗传算法应用于可变临界孔隙度的计算，最终估算得到了页岩气层的横波速度。实例应用表明，遗传算法可以计算得到沿井眼不同深度的临界孔隙度，而且预测得到的横波与偶极横波测井（DSI）一致性好，证明该方法应用于页岩气的横波估算中是可行的。     

A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms

Portfolio selection is a key issue in the business world and financial fields. This article presents a new decision making method of portfolio optimization (PO) issues in different risk measures by using new evolutionary computing method and cardinality constrains which is mentioned as hybrid meta-heuristic algorithms. Based on mean–variance (MV) Method by Markowitz we collected three risk levels; mean absolute deviation (MAD), semi variance (SV) and variance with skewness (VWS). The developed algorithms are Electromagnetism-like algorithm (EM), particle swarm optimization (PSO), genetic algorithm (GA), genetic network programming (GNP) and simulated annealing (SA). Also a diversification mechanism strategy is implemented and hybridized with the developed algorithms to increase the diversity and overcome local optimality. The sustainability of this proposed model is verified by 50 factories on the Iranian stock exchange. Finally, experimental results of proposed algorithms with cardinality constraint are compared with each other by four effective metrics in which the algorithms performance for achieving the optimal solution discussed. In addition, we have done the analysis of variance technique to confirm the validity and accurately analyze of the results which the success of this method was proved.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

0CrMnTiH齿轮钢的淬透性计算及窄淬透性带控制

采用非线性方程计算法计算20CrMnTiH钢末端淬透性,只需利用生产实际数据加以修正,就能进行精确的淬透性计算和预报,与实测值偏差不超过2HRC。采用化学成分设计和控制,保证20CrMnTiH钢△[C]不超过0.007%,△[Si]波动不超过0.04%,△[Mn]、△[Cr]不超过0.02%,△[Ti]不超过0.01%,△[Alt]不超过0.010%,[Ni]、[Cu]不超过0.05%,[Mo]、[W]、[V]不超过0.03%,[P]、[S]不超过0.020%,[N]和[B]分别不超过0.006 0%和0.0005%,并控制碳偏析指数不超过1.035%,可实现国际先进水平的末端淬透性HRC4带宽控制。