Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.     

Aging fingerprint characterization of beer using electronic nose

In this work, attempts were made in order to characterize the change of aroma of alcoholic and non alcoholic beers during the aging process by use of a metal oxide semiconductor based electronic nose. The aged beer samples were statistically characterized in several classes. Linear techniques as principal component analysis (PCA) and Linear Discriminant Analaysis (LDA) were performed over the data that revealed non alcoholic beer classes are separated except a partial overlapping between zones corresponding to two specified classes of the aged beers. A clear discrimination was not found among the alcoholic beer classes showing the more stability of such type of beer compared with non alcoholic beer. In this research, to classify the classes, two types of artificial neural networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The classification success was found to be 90% and 100% for alcoholic and non alcoholic beers, respectively. Application of PNN showed the classification accuracy of 83% and 100%, respectively for the aged alcoholic and non alcoholic beer classes as well. Finally, this study showed the capability of the electronic nose system for the evaluation of the aroma fingerprint changes in beer during the aging process.     

La-Si-O-N glasses: Part II: Vickers hardness and refractive index

Vickers hardness and refractive index have been determined for a series of La-Si-O-N oxy-nitride glasses containing 30–62 e/o of La and 9–68 e/o of N. The hardness varies between 7.7 and 11.5 GPa at a load of 1 kg and is dependent of the N content, while the La content does not influence it significantly. The increase of the hardness with N content is, contrary to reported findings for other oxy-nitride glasses, found not to be linear over the whole compositional range. The refractive index varies between 1.8 and 2.3 and increases non-linearly with increasing N content. The compositional variations of hardness and refractive index are compared with previously published results.     

Prediction of H2 Content and Inert Gas Circulation Rate Effects on Batch Annealing Process

Steel strip coils need to be annealed after they leave the cold-rolling process. The purpose of annealing is to improve steel strip formability and to remove residual internal stresses imparted during rolling. Simultaneous increase of circulation rate and inert gas hydrogen content may have considerable effects on reducing the annealing process time. A mathematical model was developed to obtain a more accurate evaluation of the effects of these two parameters on heating and cooling cycles in the annealing process. The results from the simulation of an ordinary annealing process by the proposed model were compared with values measured at site and the accuracy of the model was confirmed. Finally, the effects of increasing hydrogen content and circulation rate were investigated, first independently from each other and then simultaneously. The predictions by the model showed that reduction of annealing time would be considerable when both parameters were simultaneously increased.     

Economical load distribution in power networks that include hybrid solar power plants

With respect to the growing share of renewable resources in secure provision of electrical energy, proper utilization of hybrid power plants is of great importance. Therefore, an optimal production planning for operation of these power plants is evidently necessary.     

Egyptian mango by-product 1. Compositional quality of mango seed kernel

Egyptian mango seeds were collected as wastes from local fruit processing units and the kernels were separated and dried. This study was carried out on mango seed kernels to clarify their proximate composition, amino acids, phenolic compounds and the characteristics of the extracted oil including unsaponifiable matter constituents, lipid classes and fatty acid composition. Mango seed kernels contained a considerable amount of total phenolic compounds, total lipid, unsaponifiable matter, and a low amount of crude protein, but the quality of protein was good because it was rich in all essential amino acids. Eight phenolic compounds were identified; tannin and vanillin were in highest amounts. Unsaponifiable matter showed the occurrence of high amounts of squaline followed by sterols and tocopherols. Stearic acid was the main saturated fatty acid, while oleic acid was the major unsaturated fatty acid in all lipid classes. The fatty acid composition of total lipid and neutral lipid was similar, while phospholipid had a high amount of palmitic, linoleic and linolenic acids.     

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.     

Taguchi统计学分析方法优化Al 7068-TiC纳米复合材料的凝固行为

采用机械合金化和热压工艺制备高强Al 7068?5%TiC(质量分数)纳米复合材料。基于致密化的重要性和晶粒生长的影响,以获得较高抗压强度为目标,采用Taguchi统计法对制备Al 7068?5%TiC块体纳米复合材料的热压条件进行优化。结果表明：在500 MPa和385°C下热压30 min能获得抗压强度为938 MPa、硬度为HV 265的Al 7068?TiC纳米复合材料。此外,方差分析结果表明,外加压力是影响纳米复合材料热压过程最关键的因素。各因素对纳米复合材料热压过程影响贡献率为外加压力(61.3%)、热压温度(29.53%)和热压时间(4.49%)。     

Evolution of vinyl chloride conversion below critical micelle concentration: A response surface analysis

Utilizing response surface methodology, the conversion of vinyl chloride monomer (VCM) was monitored when the polymerization temperature, the type of surfactant, and the weight ratio of water‐to‐monomer (W/M) were taken as the emulsion polymerization variables. Because the homogeneous nucleation was found to be the dominant mechanism in VCM emulsion polymerization, irrespective of the surfactant concentration, the whole experiments have been carried out below critical micelle concentration of the used surfactants. Among all the studied variables, the polymerization temperature appeared as the most effective parameter; moreover, its interactive effect with W/M caused different trends in the alteration of final conversion being observed. Also, depending on the reaction temperature, the VCM conversion would be affected by the type of the surfactant used. Contrarily, simultaneous change in the type of surfactant and W/M revealed an insignificant effect on the evolution of VCM conversion. The optimization of final conversion of VCM was also accessible through contour plots of response surface methodology. It is worth noting that, taking a conventional approach into consideration, the alteration of VCM conversion seemed to be a monotonic function of temperature. J. VINYL ADDIT. TECHNOL., 21:157–165, 2015. © 2014 Society of Plastics Engineers