柔印分会走访潍坊东航、山东德创、青州神工等企业

2018年12月17-19日,中国印刷技术协会柔印分会周建宝理事长,蔡成基和王明烈顾问,陆敏副主任和副理事长黄鼎楼一行对潍坊东航印刷科技股份有限公司、山东德创精化科技有限公司、青州神工机械设备有限公司等企业进行调研。其间,还走访了青州博睿包装印务有限公司。

藏魂于天然 纳灵于神工——浙江省首届“大申神工杯”雕刻型根艺大展巡礼

由省文联组织的浙江省首届'大申神工杯'雕刻型根艺大展,于10月18日至22日在嵊州举行,来自全省各地的88名根雕艺术家,带着309件作品,参加了这次规模盛大的省级根艺大展。根艺,是一门化腐朽为神奇的艺术,也是一门充满活力的艺术,它从春秋战国时期拉开了蒙在身上的神秘面纱后,一直受到人们广泛而持久的欢迎。浙江,地处东南沿海,气候温和,山林茂密,得天独厚的自然条件,使这里的根雕艺术成为全国的名品。广大根艺工作者利用虫蚀腐朽之枯根朽木,遵循'三分人工。

上海市卢湾区爱国会组织青年教友参加避静神工

一九八九年三月二十二日,耶稣受难日前,上海市卢湾区天主教爱国会组织部分青年教友参加了“避静”神工。我国天主教会在独立自主自办的方针指引下,近年来,已有不少教区先后恢复了“避静”神工,使中断达三十年的神业功夫重新举行。卢湾区的教友也早已有这种愿望,这次参加“避静”的二十几位青年教友中,有出自世代教友之家的。

一剪之巧夺神工——扬州剪纸

一、扬州剪纸溯源扬州剪纸起于汉代,它伴随着民俗生活而产生,并应用在漆器、铜器、彩胜、灯彩、刺绣等民间艺术形式上。汉代,扬州民间剪纸前身——金银平脱技艺就很发达。这种工艺是用金银箔片剪镂成纹饰,贴在漆器、铜器的表面,然后上漆磨光。扬州出土的大量的漆器、铜器和残片就是例证。金银平脱漆器,不仅皇家用,民间也普遍使用,佛寺也常用金银平脱漆器作经盒等。

《神工》：为乡野天才立像

在书中这一幅幅神采奕奕的画像中，使我们鲜明地感受到画家对中华文化的钟爱，对大地与人民的挚爱；它在给我们美和文化的感染的同时，电给我们思想的启迪。

一种新型陶瓷原料加工节能系统的研究

本文讨论了一种新型陶瓷原料预加工设备系统——神工快磨系统,此系统优化了物料粒径与球石级配,在提高陶瓷球磨工序效率同时,也降低球磨工序的物耗及能耗等。

Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network

The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

The Spirit of Craftsmanship and Industrial Civilization

The spirit of craftsmanship applies to every trade. In the context of opportunities arising from the new industrial revolution and China＇s transition towards high-end manufacturing, Premier Li Keqiang put forward the concept of the spirit of craftsmanship. This paper reckons that the spirit of craftsmanship can be defined from six dimensions including dedication, standard, precision, innovation, perfection and human care. In fact, the spirit of craftsmanship abounds in China, as illustrated by the examples of Tongrentang and Haier. In comparing the three manufacturing powers of Germany, Japan and the United States, this paper reveals the differences in the connotations of the spirit of craftsmanship across these countries. According to specific national conditions and development stage, China must achieve structural re-engineering of corporate organizations at the level of firms, products, divisions and modules. In a nutshell, the spirit of craftsmanship is a core component of corporate and industrial civilization and it holds an important historic position in industrial and social civilization.

Constructing processing map of Ti40 alloy using artificial neural network

Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron（MLP） architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network（ANN） model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.

Prediction of mechanical properties of A357 alloy using artificial neural network

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid-solution time,artificial aging temperature and artificial aging time.An artificial neural network（ANN） model with a back-propagation（BP） algorithm was used to predict mechanical properties of A357 alloy,and the effects of heat treatment processes on mechanical behavior of this alloy were studied.The results show that this BP model is able to predict the mechanical properties with a high accuracy.This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy.Isograms of ultimate tensile strength and elongation were drawn in the same picture,which are very helpful to understand the relationship among aging parameters,ultimate tensile strength and elongation.

Hot deformation behavior and processing maps of Mg-Zn-Cu-Zr magnesium alloy

The deformation behaviors of a new quaternary Mg-6Zn-1.5Cu-0.5Zr alloy at temperatures of 523-673 K and strain rates of 0.001-1 s-1 were studied by compressive tests using a Gleeble 3800 thermal-simulator.The results show that the flow stress increases as the deformation temperature decreases or as the strain rate increases.A strain-dependent constitutive equation and a feed-forward back-propagation artificial neural network were used to predict flow stress,which showed good agreement with experimental data.The processing map suggests that the domains of 643-673 K and 0.001-0.01 s-1 are corresponded to optimum conditions for hot working of the T4-treated Mg-6Zn-1.5Cu-0.5Zr alloy.

Recovery of indium by acid leaching waste ITO target based on neural network

The optimized leaching techniques were studied by technical experiment and neural network optimization for improving indium leaching rate. Firstly, effect of single technical parameter on leaching rate was investigated experimentally with other parameters fixed as constants. The results show that increasing residual acidity can improve leaching rate of indium. Increasing the oxidant content can obviously increase leaching rate but the further addition of oxidant could not improve the leaching rate. The enhancement of temperature can improve the leaching rate while the further enhancement of temperature decreases it. Extension leaching time can improve the leaching rate. On this basis, a BPNN model was established to study the effects of multi-parameters on leaching rate. The results show that the relative error is extremely small, thus the BPNN model has a high prediction precision. At last, optimized technical parameters which can yield high leaching rate of 99.5%were obtained by experimental and BPNN studies：residual acidity 50-60 g/L, oxidant addition content 10%, leaching temperature 70 ℃ and leaching time 2 h.

Improvement and application of neural network models in development of wrought magnesium alloys

Neural network models of mechanical properties prediction for wrought magnesium alloys were improved by using more reasonable parameters, and were used to develop new types of magnesium alloys. The parameters were confirmed by comparing prediction errors and correlation coefficients of models, which have been built with all the parameters used commonly with training of all permutations and combinations. The application was focused on Mg-Zn-Mn and Mg-Zn-Y-Zr alloys. The prediction of mechanical properties of Mg-Zn-Mn alloys and the effects of mole ratios of Y to Zn on the strengths in Mg-Zn-Y-Zr alloys were investigated by using the improved models. The predicted results are good agreement with the experimental values. A high strength extruded Mg-Zn-Zr-Y alloy was also developed by the models. The applications of the models indicate that the improved models can be used to develop new types of wrought magnesium alloys.

Optimum design of flow distribution in quenching tank for heat treatment of A357 aluminum alloy large complicated thin-wall workpieces by CFD simulation and ANN approach

Based on the computational fluid dynamics （CFD） method, a quenching tank with two agitator systems and two flow-equilibrating devices was selected to simulate flow distribution using Fluent software. A numerical example was used to testify the validity of the quenching tank model. In order to take tank parameters （agitation speed, position of directional flow baffle and coordinate position in quench zone） into account, an approach that combines the artificial neural network （ANN） with CFD method was developed to study the flow distribution in the quenching tank. The flow rate of the quenching medium shows a very good agreement between the ANN predicted results and the Fluent simulated data. Methods for the optimal design of the quenching tank can be used as technical support for industrial production.

Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process

The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.

Compositional optimization of glass forming alloys based on critical dimension by using artificial neural network

An artificial neural network （ANN） model was developed for simulating and predicting critical dimension dc of glass forming alloys. A group of Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were designed based on the dc and their de values were predicted by the ANN model. Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were prepared by injecting into copper mold. The amorphous structures and the determination of the dc of as-cast alloys were ascertained using X-ray diffraction. The results show that the predicted de values of glass forming alloys are in agreement with the corresponding experimental values. Thus the developed ANN model is reliable and adequate for designing the composition and predicting the de of glass forming alloy.

Integrated assessment of sea water quality based on BP artificial neural network

In order to carry out an integrated assessment of sea water quality objectively, this paper based on the concept and principle of artificial neural network, generated appropriate training samples for BP artificial neural network model through the method of producing samples to the concentration of various pollution index of sea water quality from the viewpoint of threshold, established the BP artificial neural network model of sea water quality assessment using multi-layer neural network with error back-propagation algorithm. This model was used to assess water environment and obtain sea water quality categories of offshore area in Bohai Bay through calculating. The calculations shown that pollution index in river＇s wet season was higher than that in dry season from 2004 to 2007, and the pollution was particularly serious in 2005 and 2006, but a little better in 2007. The assessed results of cases shown that the model was reasonable in design and higher in generalization, meanwhile, it was common, objective and practical to sea water quality assessment.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.