Statistical analysis for the manufacturing of multi-strip patterns by roll-to-roll single slot-die systems

Roll-to-roll (R2R) slot-die coating systems are mostly devoted to the mass manufacture of printed electronics. This study examined the correlation among the operating conditions, thickness, and width of the patterned strip fabricated by the R2R slot-die system. A full factorial experiment was conducted to screen for effective parameters. The velocity of a moving substrate was found to be the most dominant parameter affecting the thickness and width of the patterned strips. The flow ratio of the supply to the slot-die, and gap between substrate and slot-die did not affect the width of the strip, but affected the thickness; therefore, the flow ratio and gap can be employed for the independent patterning of thickness against width. In addition, it was proposed to determine the R2R process conditions, such as gap, velocity, and flow ratio for the desired thickness and width of the patterned strips.     

Modelling of electrostatic fluidized bed (EFB) coating process using artificial neural networks

This paper involves experimentation on coating process of metal substrates in an electrostatic fluidized bed (EFB). Several operational parameters were covered like coating time, applied voltage and gas flow rate fed to the fluidized bed.First, a design of experiment (DOE) approach was used to define the experimental campaign and a general linear model based on analysis of variance (ANOVA) was used to elaborate and interpret the influence of all the operational parameters on coating thickness trends.Second, the experimental data were modelled using artificial neural networks. Different neural networks and training algorithms were employed to find the best technique to predict the coating thickness trends. The reliability of the best neural network solutions was checked by comparing them with a built ad hoc regression model. The multi-layer perceptron (MLP) neural network trained with back-propagation (BP) algorithm was found to be the fittest model. Besides, a genetic algorithm (GA) was also employed to improve the capability of MLP model to provide the best fit of experimental results all over the investigated ranges.Finally, a verification experimental plan was performed and a related analytical model was developed to check the reliability of the neural network model with GA to predict the whole coating thickness trends according to the operational parameters. A comparison between the neural network model and an analytical model was also carried out.     

矿用PCB板组件三防漆涂覆工艺改进

针对传统三防漆工艺受技术因素及环境因素影响存在可控度不高、涂覆一致性差等问题,提出了一种改进的矿用PCB板组件三防漆涂覆工艺;介绍了三防漆涂覆方式和材料的选择,研究了矿用PCB板组件三防漆涂覆工艺方法和内容。测试结果表明,选择双组份聚氨酯漆、采用选择性喷涂法的涂覆效果一致性好,提高了生产效率。     

Modeling of extrusion coating for applications in electronics manufacturing

This paper focuses on the development of a model of the extrusion coating process that can be used to generate an automated (as opposed to manual) procedure for determining the control setpoints for a desired deposition thickness and range of deposition materials. This model includes the dynamics of the bead formation process and the bead variation during the coating. The “bead” refers to the deposition material that collects in front and under the die head as the substrate is moved beneath it. Understanding and modeling the dynamics of the bead process as a function of the control‐variable settings is crucial in obtaining an automated procedure for setpoint determination in extrusion coating.     

A Kalman filter-based R2R control system with parallel stochastic disturbance models for semiconductor manufacturing processes

A Kalman filter-based run-to-run control system has been proposed for minimum variance control of semiconductor manufacturing process. In the proposed control system, both gain- and bias-varying process models combined with different stochastic disturbance models were considered and identified in parallel. The best-fit model is selected and used for the R2R controller design. Sub-models of the ARIMA(1,1,1) process were considered for stochastic modeling of the bias and gain variation, and the Kalman filters are used to find the optimum model parameter estimation. The control performance was analyzed for each case of the disturbance model to investigate the expected benefit from the control system in comparison with the EWMA filter-based controller.     

Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Chromium carbonitride coatings were formed on plain carbon and alloy steels by pre-nitrocarburizing, followed by thermoreactive deposition and diffusion in a salt bath below 700 °C. In the present study, an artificial neural network-based model (ANNs) was developed to predict the layer thickness of pre-nitrided steels. Seventeen parameters affecting the layer thickness were considered as inputs, including the pre-nitriding time, salt bath compositions ratio, salt bath aging time, ferrochromium particle size, ferrochromium weight percent, salt bath temperature, coating time, and different chemical compositions of steels. The network was then trained to predict the layer thickness amounts as outputs. A 2-feed-forward back-propagation network was developed and trained using experimental data form literatures. Five steels were investigated. The effects of coating parameters on the layer thickness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the layer thickness.     

Parametric design optimization of 2-DOF R–R planar manipulator—A design of experiment approach

This work illustrates simulation approach for optimizing the parametric design and performance of a 2-DOF R–R planar manipulator. Using dynamic and kinematic models of a manipulator different performance measures for the manipulator are obtained for different combination of parameters with effect of noise incorporated to imitate the real time performance of the manipulator. A novel approach has been proposed to model, the otherwise difficult to model, noise effects. The data generated during simulation for various parameter combinations are utilized to analyze the statistical significance of kinematic and dynamic parameters on performance of manipulator using ANOVA technique. The parameter combinations, which give optimum performance measures obtained for different points in workspace, are compared and reported.     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

基于全自动热喷涂技术的电力设备零部件涂层制备工艺研究

变电设施常常服役于空气环境中,容易受到腐蚀的影响,对设备进行表面改性处理可以大幅改善设施的防腐蚀性能,提升其使用寿命。全自动热喷涂技术作为一种新型表面改性方式,是通过热源将合金加热至熔融或半熔融状态并涂覆与基体表面的技术。本文对其工艺参数进行了系统研究,发现当喷涂速度为0.1m/s,涂层厚度最大(700μm)、孔隙最小(2.1%)、性能最佳。喷涂距离对涂层孔隙率的影响较大,在距离为180mm,孔隙率达到最低(4.3%),此外还可根据对涂层厚度需求以及孔隙率的大小选择合适的喷涂电压和电流。对最佳参数条件下热喷涂铝涂层的试样进行盐雾试验。发现涂层表面会发生氧化腐蚀,生成以Al(OH)₃为主的腐蚀产物并在涂层表面形成致密的氧化膜。此外,产生的腐蚀产物还可以填补热喷涂过程中形成的孔隙,阻止腐蚀介质继续向涂层内部渗入。     

二层以太网网络体系结构在汽车涂装生产中的应用

当前分析面向汽车生产线网络控制系统的设计规划,对提升汽车行业企业信息化水平具有重大的意义。本文结合汽车涂装生产工艺特点的基础上,提出汽车涂装网络控制系统的框架结构,研究了基于以太网技术的二层网络体系结构在汽车涂装生产中的应用。     

基于PLC和WinCC的玻璃镀膜控制系统优化设计

针对玻璃镀膜生产线工艺特点,根据国内某玻璃镀膜生产线的生产需求,对原有自动控制系统进行了改进和优化设计。基于工艺配气、磁控溅射、真空抽气、阴极电源和伺服传动等各子系统的控制难点及实现过程,优化设计了以西门子S7-400 PLC为核心和WinCC 7.0为上位监控软件的玻璃镀膜控制系统。在WinCC组态软件平台上,搭建了玻璃镀膜控制系统的监控界面,通过上位机处理大量生产过程数据和归档重要参数,实现生产工艺流程各个环节的实时监控、设备工作运行状态的参数显示和系统的故障报警等功能。     

Artificial neural network based approach for dynamic parameter design

The Taguchi parameter design method has been recognized as an important tool for improving the quality of a product or a process. However, the statistical methods and optimization procedures proposed by Taguchi have much room for improvement. For instance, the two-step procedure proposed by Taguchi may fail to identify an optimum design condition if an adjustment parameter does not exist, the optimal setting of a design parameter is determined only among the levels included in the parameter design experiment, and, for the dynamic parameter design, the signal parameter is assumed to follow a uniform rather than a general distribution. This paper develops an artificial neural network based dynamic parameter design approach to overcome the shortcomings of the Taguchi and existing alternative approaches. First, an artificial neural network is trained to map the relationship between the characteristic, design, noise and signal parameters. Second, Latin hypercube samples of the signal and noise parameters are obtained and used to estimate the slope between the signal parameter and characteristic as well as the variance of the characteristic at each set of design parameter settings. Then, the dynamic parameter design problem is formulated as a nonlinear optimization problem and solved to find the optimal settings of the design parameters using sequential quadratic programming. The effectiveness of the proposed approach is illustrated with an example.     

Autoregressive and adaptive estimation with an application to hurricane track prediction

The key result of this paper is that following a change in a parameter of AR (p), an autoregressive process of order p, the innovations sequence of the Kalman filter parameters will follow an autoregressive moving-average model in addition to a transient function. Furthermore, it is also shown that the first p values of the innovations' sample autocorrelation can be used to form a sufficient statistic to detect if at least one of the parameters in the AR (p) model did change at an unknown point in time. Following a parameter change detection process, improved estimates and noise statistics can be determined and implemented to modify the Kalman filter. The revised model will thus be more consistent with the most recent process behaviour. To motivate the reader, a simulation exercise was conducted to validate the on-line change detector and adaptive estimation algorithm. The proposed algorithm was used to predict hurricane movements with real data provided by the National Hurricane Center.     

Modeling of input–output relationships for a plasma spray coating process using soft computing tools

To automate any manufacturing process, its input–output relationships are to be known in both forward and reverse directions. The present work aims to correlate input process parameters with various responses of a plasma spray coating process. Statistical regression analysis had been carried out previously for this process based on the data collected through central composite design of experiments to establish input–output relationships in forward direction. However, the said relationships could not be accurately determined in reverse direction using the obtained regression equations due to the presence of a non-square transformation matrix. Soft computing-based approaches had been developed to model the process in both forward as well as reverse directions. The performances of the developed approaches had been tested on different cases obtained through real experiments. A comparative study had been made of these developed approaches in terms of accuracy in predictions.     

Optimization and predictive modeling using S/N,RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization, respectively. In the last phase, regression analysis and artificial neural network (ANN) models for predicting the MRR, EWR, AOC and TA. As a result of experimental analysis, discharge current was the most important parameter for micro-drilling with EDM. It was found out that this parameter influenced positively MR, while it has negatively an effect on EWR, AOC and TA. Mathematical model based on ANNs exhibited a successful performance for predication of outputs. Optimum process parameters which were discharge current of 10.18 Å, dielectric liquid pressure of 58.78 bar and electrode tool rotational speed of 100 rpm for multi-objective optimization were determined through RSM with desirability function analysis in micro-deep hole EDM drilling of AISI 304 stainless steel.

     

Ink drop filling by a slot coating die for an electrowetting display panel

Ink drops have to be filled into the square cells for an electrowetting display panel. Several ink solutions such as dodecane, tetradecane, and hexadecane were used as test liquids. These fluids have viscosities less than 4 mPas and surface tensions between 23.3 and 26 mN/m and contact angles less than 50°. A slot coating die was employed to deliver these liquids to fill up the square cells. The effects of several parameters such as coating thickness and coating speed were examined. Operating windows inside which the stable filling is possible could be found for these liquids. Several defects were observed outside the operating windows. A flow visualization technique was applied to observe the fluid motion in the coating bead region, particularly the movements of upstream and downstream menisci, the mechanism of liquid filling was discussed.     

Phase formation in a calcium phosphate coating growing on a zirconium substrate with an oxide layer

A model of the growth of a calcium phosphate coating on a zirconium substrate with respect to the formed oxide sublayer and formation of phases is proposed. The influence of the parameters of the model on the growth of the coating and the evolution of the stresses in the oxide layer and in the coating is investigated. It is shown that the content of the substances formed in the coating increases with the increasing constants of the reaction rates, increasing diffusion coefficients, or with a decreasing voltage of the process. It is shown that the stresses in the vicinity of the interface (the substrateoxide layer) are higher when there is an oxide layer than in its absence.     

光纤高温传感器表面镀膜技术研究

在高温测试中,光纤高温传感器具有独特的优势,在制作光纤高温传感器过程中,镀膜技术是一个关键的环节。根据高温测试需要,选取了合适的镀膜材料,对氧化锌（ZnO）和氧化镁铝（MgAl2O4）的物理化学性质进行了介绍,同时根据材料特性选择了适合的镀膜方法,分别适用于黑体腔温度传感器的头部镀层和光纤光栅传感器表面镀膜,取得了良好的效果,达到了温度测量的要求。     

多自由度非线性系统动态参数化模型建模方法研究

针对多自由度非线性系统的动态模型辨识问题,基于NARX(Non-linear Autoregressive with Exogenous inputs)模型的建模方法,考虑系统的物理设计参数,建立非线性系统动态参数化模型.首先,根据系统输入、输出数据建立系统不同参数下的NARX模型,并通过EFOR(Extended Forward Orthogonal Regression)算法对不同参数下NARX模型进行修正,以统一辨识得到的系统模型结构.随后,建立NARX模型系数与物理设计参数间的函数关系,得到多自由度非线性系统的动态参数化模型.以单输入、单输出两自由度非线性系统为例,根据数值仿真结果,对系统的动态参数化模型建模过程进行说明.最后,以带非线性涂层阻尼的悬臂梁作为试验对象,建立其动态参数化模型以反映其动力学特性.试验结果表明,非线性系统动态参数化模型能准确预测多自由度非线性系统的输出响应,为非线性系统的分析与优化设计提供了理论基础.