A sub-space artificial neural network for mold cooling in injection molding

The applications of artificial intelligence (AI) have considerably expanded over recent years. A new class of industrial systems is beginning to evolve that incorporates using high volume data and advanced analytics to better optimize product quality while reducing energy consumption. Artificial neural networks (ANN) when combined with advanced modeling and control, begins to form an AI platform that can be further enhanced for factories of the future. This paper provides a demonstration of such initial work that can be further developed for future systems in a generic way. When considering polymer processing such as plastic injection molding, the mold cavity temperature (MCT) profile directly relates to part quality and part reject rates. Therefore, it is desirable to optimize the mold cooling process using real time control of MCT as it directly affect part quality. However, MCT is affected by a number of interacting nonlinear dynamic parameters that are often neglected due to the challenge of quantifying such parameters. Advanced model based control algorithms are often used for providing improved control of complex systems. However, they depend on good model formulations that are analytically insufficient. An online intelligent system identification approach for the mold cooling process is developed and tested. An ANN is designed to adjust online sub-space parameters that govern a mold cooling model. Results demonstrate that this online ANN approach can be used to accurately predict the dynamic behavior of mold cavity surface temperature. This is key to many industrial systems where their states are not directly observable and uncertainties are unknown. The methodology can be readily adapted for different operating conditions as in this case of polymer processing and has good potential for its integration with advanced model based control schemes and cloud computing approaches for the next generation of machines.     

Cost estimation of plastic injection molding parts through integration of PSO and BP neural network

Plastic injection molding technology has been widely used in a variety of high-tech products, auto parts and generic household products. Against the waves of globalization, the plastic injection enterprises must shorten time-to-market to enhancement of competence, and launch products ahead of all other competitors, and thus they can quickly seize a big target market and lead the price. The backpropagation (BP) neural network was used in this study to construct an estimating model for the cost of plastic injection molding parts so as to reduce the complexity in the traditional cost estimating procedures. Because the parameters of BP neural network have a significant influence on results, and particle swarm optimization (PSO) is capable of quickly finding optimal solutions. We integrated PSO and BP neural network so that the convergence rate was improved and precision was relatively enhanced through particle evolutions based on the optimum parameter combination from BP neural network.     

A three-layer back-propagation neural network for spam detection using artificial immune concentration

In this paper, a three-layer back-propagation neural network (BPNN) is employed for spam detection by using a concentration based feature construction (CFC) approach. In the CFC approach, ‘self’ and ‘non-self’ concentrations are constructed through ‘self’ and ‘non-self’ gene libraries, respectively, to form a two-element concentration vector for expressing the e-mail efficiently. A three-layer BPNN with two-element input is then employed to classify e-mails automatically. Comprehensive experiments are conducted on two public benchmark corpora PU1 and Ling to demonstrate that the proposed CFC approach based BPNN classifier not only has a very much fast speed but also achieves 97 and 99% of classification accuracy on corpora PU1 and Ling by just using a two-element concentration feature vector.     

Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.     

A variotherm mold for micro metal injection molding

In this paper, a variotherm mold was designed and fabricated for the production of 316L stainless steel microstructures by micro metal injection molding (MIM). The variotherm mold incorporated a rapid heating/cooling system, vacuum unit, hot sprue and cavity pressure transducer. The design of the variotherm mold and the process cycle of MIM using the variotherm mold were described. Experiments were conducted to evaluate the molded microstructures produced using variotherm mold and conventional mold. The experiments showed that microstructures of higher aspect ratio such as 60 m × height 191 m and 40 m × height 174 m microstructures could be injection molded with complete filling and demolded successfully using the variotherm mold. Molded microstructures with dimensions of 60 m × height 191 m were successfully debound and sintered without visual defects.     

Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding

Determination of initial process meters for injection molding is a highly skilled job and based on skilled operators know-how and intuitive sense acquired through long-term experience rather than a theoretical and analytical approach. Facing with the global competition, the current trial-and-error practice becomes inadequate. In this paper, application of artificial neural network and fuzzy logic in a case-based system for initial process meter setting of injection molding is described. Artificial neural network was introduced in the case adaptation while fuzzy logic was employed in the case indexing and similarity analysis. A computer-aided system for the determination of initial process meter setting for injection molding based on the proposed techniques was developed and validated in a simulation environment. The preliminary validation tests of the system have indicated that the system can determine a set of initial process meters for injection molding quickly without relying on experienced molding personnel, from which good quality molded parts can be produced.     

Intestinal broiler microflora estimation by artificial neural network

Microflora population of poultry was affected by various factors. Many methods and techniques were developed to study microflora population. But, most of them confronted some problems. Moreover, being costly, laborious, and time-consuming made it impossible to measure microflora population several times. In this study, we tried to estimate intestinal microflora population using artificial neural network (ANN). Lactic acid bacteria were used as model of microflora population. Time and lactic acid bacteria were used as input and output variables, respectively. The best model of ANN was determined based on coefficient of determination, root mean square error, and mean absolute error criteria. The results of current study have shown that ANN is appropriate, cheap, and reliable tools to estimate intestinal microflora population (lactic acid bacteria) of broiler at different ages.     

Dynamic weight estimation using an artificial neural network

A Multi-Layer Perceptron Artificial Neural Network is employed to enable the mass that is applied to a weighing platform to be rapidly and accurately estimated before the platform has settled to the steady state. This is achieved through training the network on a set of waveforms resulting from applied masses over the operating range of the weighing platform. Results are given for both simulated and experimental data that confirm the success of the method.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.     

基于ANN伪量测建模的配电网状态估计

针对配电网状态估计实时量测数量的不足,提出了一种基于ANN伪量测建模的配电网状态估计算法。该方法采用人工神经网络网络（ANN）,将部分实时量测数据作为神经网络的输入,产生较为精确的负荷伪量测数据。此外,应用高斯混合模型对产生伪量测的误差进行分解拟合,从而获得负荷伪量测的权重。最后,将获得的伪量测及其权重输入到状态估计模块中,实现了配电网的状态估计。通过英国标准配网系统（UKGDS）中16节点模型的仿真结果表明,该算法提高了配电网状态估计的精度,具有一定的现实意义和理论价值。     

基于神经网络的注塑机注射速度的迭代学习控制

对具有不确定性和干扰项的重复非线性注塑机控制系统,尤其是注射速度的控制,提出基于神经网络的迭代学习控制器,其中迭代学习控制器设计为神经网络控制器,它以前馈方式作用于对象。PD反馈控制器用于使系统达到稳定,同时和前馈的神经网络学习控制器一起使系统达到理想的控制效果。仿真结果表明,该控制器可以随着迭代次数的增加有效减小跟踪误差。     

基于RBF神经网络的软件成本估算模型

软件成本估算是软件开发过程中一项非常重要的活动,但现有的方法在准确估算软件成本方面还存在不足。针对软件成本估算不够准确的现状,提出了一种基于RBF神经网络的软件成本估算模型。该模型采用样本聚类的方法确定隐含层节点数,利用遗传算法对隐层节点中心值和高斯函数的宽度进行优化,利用线性最小二乘法训练网络的权值。实验证明,该模型能够准确有效地估算软件成本。     

Cost estimation in road construction using artificial neural network

Neural Computing and Applications - Road construction projects on the territory of the Republic of Croatia are characterized by the overrun of planned costs. The experience of the contractor on...     

Micro injection molding for mass production using LIGA mold inserts

Micro molding is one of key technologies for mass production of polymer micro parts and structures with high aspect ratios. The authors developed a commercially available micro injection molding technology for high aspect ratio microstructures (HARMs) with LIGA-made mold inserts and pressurized CO₂ gasses. The test inserts made of nickel with the smallest surface details of 5 μm with structural height of 15 μm were fabricated by using LIGA technology. High surface quality in terms of low surface roughness of the mold inserts allowed using for injection molding. Compared to standard inserts no draft, which is required to provide a proper demolding, was formed in the inserts. To meet higher economic efficiency and cost reduction, a fully electrical injection molding machine of higher accuracy has been applied with dissolving CO₂ gasses into molten resin. The gasses acts as plasticizer and improves the flowability of the resin. Simultaneously, pressurizing the cavity with the gasses allows high replication to be obtained. Micro injection molding, using polycarbonate as polymer resins, with the aspect ratio of two was achieved in the area of 28 × 55 mm² at the cycle time of 40 s with CO₂ gasses, in contrast to the case of the aspect ratio of 0.1 without the gasses.     

An artificial neural network for nonlinear estimation of the turbine flow-meter coefficient

Turbine flow meters find various applications in the process industries, such as batch control, measuring fuel oil and gas consumption, controlling blending processes, etc. The turbine meter is a rotor driven by the fluid being metered, at a speed proportional to the flow rate.The actual behavior of a turbine flow meter is a complex function of many variables; among these are the temperature, pressure, and viscosity of the fluid; the lubricating qualities of the fluid; bearing wear; and environmental factors. The turbine meter coefficient is referred to as the ‘K factor’, and is defined as the number of pulses per unit volume. At present, there is no single mathematical equation to predict the actual K factor. More accurate estimations and trending of the K factor will not only facilitate preventive maintenance, replacement analysis, etc., but will also ensure that material flow accounting is accurate.This research explores the use of neural-network models to aid in the estimation of the actual K factor that reflects the effect of the actual operating conditions of the turbine meter. This research analyzed data from three different turbine flow meters measuring the rate of pumping oil from the North Sea, for a company that operates off-shore oil platforms. The use of neural networks presents a new approach to the capturing of the underlying nonlinear relationships among the various input variables and the K factor. The results from this study report significant percentage reductions in mean absolute errors for the neural-network predictions over the company’s present estimation practices for the turbine flow-meter coefficient.     

变模温注射成型数值仿真

针对传统的恒定模温控制技术易导致塑件表面产生熔接痕、流动痕和凹陷等表面缺陷的问题,采用Moldex 3D对某典型型腔进行流动分析,预测模型中熔接痕产生的位置;利用该软件中的暂态冷却模块,采用改变冷却方式和注射时间的方法对普通冷却与变模温技术的保压和冷却效果进行对比.分析结果表明,变模温技术能在不影响生产效率的基础上,达到提升制品品质的目的.     

Reusing back-propagation artificial neural networks for land cover classification in tropical savannahs

This paper focuses on a method to overcome some of the disadvantages that are related with the use of artificial neural networks (ANNs) as supervised classifiers. The proposed method aims at speeding up network learning, improving classification accuracies and reducing variability on classification performance due to random weight initialization. This can be realized by transferring implicit knowledge from a previously learned source task to a new target task using the proposed algorithm, Discriminality Based Transfer (DBT). The presented approach is compared with conventional network training and a literal transfer method in a 13-class tropical savannah classification experiment using Landsat Thematic Mapper (TM) data. Knowledge was extracted from a network trained on the Kara experimental site in Togo. This information was used to classify the Savanes-L'Oti area which differs in terms of geographical position, image acquisition date, climatological condition and land cover. It was possible to speed up network learning 5.2, 4.3 and 1.8 times using, respectively, 5-, 10- and 20-pixels-per-class training sets. Larger training sets showed less speed improvement. After applying DBT, average classification accuracies were not significantly different from accuracies obtained after training random initialized networks, although DBT tended to show better performance on smaller training sets. It was possible to explain differences in individual class accuracies by analysing Bhattacharyya (BH) distances calculated between all Kara and Savanes-L'Oti classes. Finally, variability on classification performance decreased significantly when training with 5-, 10- and 20-pixels-per-class training sets after DBT application.     

Spiral and conformal cooling in plastic injection molding

Designing cooling channels for the thermoplastic injection process is a very important step in mold design. A conformal cooling channel can significantly improve the efficiency and the quality of production in plastic injection molding. This paper introduces an approach to generate spiral channels for conformal cooling. The cooling channels designed by our algorithms has very simple connectivity and can achieve effective conformal cooling for the models with complex shapes. The axial curves of cooling channels are constructed on a free-form surface conformal to the mold surface. With the help of boundary-distance maps, algorithms are investigated to generate evenly distributed spiral curves on the surface. The cooling channels derived from these spiral curves are conformal to the plastic part and introduce nearly no reduction at the rate of coolant flow. Therefore, the channels are able to achieve uniform mold cooling. Moreover, by having simple connectivity, these spiral channels can be fabricated by copper duct bending instead of expensive selective laser sintering.     

General frameworks for optimization of plastic injection molding process parameters

Plastic injection molding is widely used for manufacturing a variety of parts. Molding conditions or process parameters play a decisive role that affects the quality and productivity of plastic products. This work reviews the state-of-the-art of the process parameter optimization for plastic injection molding. The characteristics, advantages, disadvantages, and scope of application of all of the common optimization approaches such as response surface model, Kriging model, artificial neural network, genetic algorithms, and hybrid approaches are addressed. In addition, two general frameworks for simulation-based optimization of injection molding process parameter, including direct optimization and metamodeling optimization, are proposed as recommended paradigms. Two case studies are illustrated in order to demonstrate the implementation of the suggested frameworks and to compare among these optimization methods. This work is intended as a contribution to facilitate the optimization of plastic injection molding process parameter.