Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

Traditional statistical process control (SPC) techniues of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore the reduction in process variability obtained through the use of SPC techniques has not been realized in process industries. Techniques are needed to serve the same function as SPC control charts, that is to identify process shifts, in correlated parameters. Radial basis function neural networks were developed to identify shifts in process parameter values from papermaking and viscosity data sets available in the literature. Time series residual control charts were also developed for the data sets. Networks were successful at separating data that were shifted 1.5 and 2 standard deviations from nonshifted data for both the papermaking and viscosity parameter values. The network developed on the basis of the papermaking data set was also able to separate shifts of 1 standard deviation from nonshifted data. The SPC control charts were not able to identify the same process shifts. The radial basis function neural networks can be used to identify shifts in process parameters, thus allowing improved process control in manufacturing processes that generate correlated process data.     

Performance comparison between on-line sensors and control charts in manufacturing process monitoring

The rapid evolution of sensor technology, using techniques such as lasers, machine vision and pattern recognition, provides the potential to greatly improve the Statistical Process Control (SPC) method for monitoring manufacturing processes. This paper studies the method of using on-line sensors to monitor manufacturing processes and compares that method with the control chart method, a widely used SPC tool. Two separate economic models are formulated for using either a sensor or a control chart to monitor a manufacturing process. Then, the two models are compared in a sensitivity analysis with lespect to several process parameters.     

Adaptive resonance theory-based neural algorithms for manufacturing process quality control

The demand for quality products in industry is continuously increasing. To produce products with consistent quality, manufacturing systems need to be closely monitored for any unnatural deviation in the state of the process. Neural networks are potential tools that can be used to improve the analysis of manufacturing processes. Indeed, neural networks have been applied successfully for detecting groups of predictable unnatural patterns in the quality measurements of manufacturing processes. The feasibility of using Adaptive Resonance Theory (ART) to implement an automatic on-line quality control method is investigated. The aim is to analyse the performance of the ART neural network as a means for recognizing any structural change in the state of the process when predictable unnatural patterns are not available for training. To reach such a goal, a simplified ART neural algorithm is discussed then studied by means of extensive Monte Carlo simulation. Comparisons between the performances of the proposed neural approach and those of well-known SPC charts are also presented. Results prove that the proposed neural network is a useful alternative to the existing control schemes.     

Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA

As manufacturing quality has become a decisive factor in competing in a global market, statistical quality techniques such as statistical process control (SPC) are becoming very popular in industries. With advances in sensing and data capture technology, large volumes of data are being routinely collected in automatic controlled processes. There is a growing need for SPC monitoring and diagnosis in these environments, but an effective implementing scheme is still lacking. This research provides an integrated approach to simultaneously monitor and diagnose an automatic controlled process by using dynamic principal component analysis (DPCA) and minimax distance classifier. Through a step-by-step implementation procedure, the proposed scheme is expected to have an impact on many manufacturing industries with automatic process control (APC) or engineering process control (EPC).     

Control chart pattern recognition using back propagation artificial neural networks

In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). Online automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. Two back-propagation artificial neural networks are used to model traditional Shewhart SPC charts and identify out-of-control situations as specified by the Western Electric Statistical Quality Control Handbook , including instability patterns, trends, cycles, mixtures and systematic variation. Using back propagation, patterns are presented to the network, and training results in a suitable model for the process. The implication of this research is that out-of-control situations can be detected automatically and corrected within a closed-loop environment. This research is the first step in an automated process monitoring and control system based on control chart methods. Results indicate that the performance of the back propagation neural networks is very accurate in identifying control chart patterns.     

The role of SPC in printed circuit board manufacture

This paper reports on research which examined the use of statistical process control (SPC) in the quality improvement process of a printed circuit board (PCB) manufacturer. The implementation of SPC is discussed along with the difficulties encountered and benefits achieved. The findings indicate that SPC is a tool which can be of considerable assistance in the quality improvement process of PCB manufacture. However, the variety of manufacturing technologies used and the number of interconnecting processes makes the application of SPC more difficult than in other traditional industries. The lessons learned include that the introduction of SPC must not be rushed, that discipline and support from all levels in the organization are crucial to its success, that SPC cannot be used in isolation—it needs the structure of a continuous improvement initiative, and that getting processes in a state of statistical control and capable, and keeping them there, is a difficult task which involves considerable effort and patience.     

基于神经网络的质量控制图模式识别技术的研究

提出了一种用于质量控制图模式识别的新的神经网络模型，它与以往的神经网络模型相比，具有较强的识别能力和较短的训练时间。     

基于神经网络的SPC/EPC整合过程监测方法研究

为解决统计过程控制(SPC)/工程过程调整(EPC)整合引起的传统SPC控制图监测异常扰动效率低的问题,提出了采用神经网络技术监测SPC/EPC整合过程的策略,并对神经网络模型结构和参数设置进行分析,构建过程输入、过程输出及两者的协方差为输入参数,异常扰动发生与否为输出参数的3层神经网络模型。为验证该方法的性能,进行了大量的比较实验:即对相同的样本,分别采用Shewhar图、CUSUM图和上述神经网络模型进行监测。实验结果表明:神经网络模型能准确监测幅度大于2的阶跃扰动和大于2的过程漂移,平均运行步长(ARL)为1;传统SPC监测技术只能较准确地(监测率大于90%)监测幅度大于5的阶跃扰动和大于2的过程漂移,ARL大于2。与传统监测方法相比,该方法能快速有效地监测异常扰动的发生。     

A new stochastic neural network model and its application to grouping parts and tools in flexible manufacturing systems

Recently, some stochastic neural network models have been presented for the purpose of overcoming the defect that the deterministic neural network models do not have the ability to escape from a local optimal solution. However, the specification of the values of various parameters and weights in these stochastic neural network models is more complicated than that in the deterministic neural network models. In this paper, a new stochastic neural network model is proposed in order to reduce the complication of specifying the values of parameters and weights. For a practical purpose, the proposed model is applied to the problem of grouping parts and tools in flexible manufacturing systems (FMSs).     

Monitoring Profiles Based on Nonparametric Regression Methods

The use statistical process control (SPC) in monitoring and diagnosis of process and product quality profiles remains an important problem in various manufacturing industries. The SPC problem with a nonlinear profile is particularly challenging. This article proposes a novel scheme to monitor changes in both the regression relationship and the variation of the profile online. It integrates the multivariate exponentially weighted moving average procedure with the generalized likelihood ratio test based on nonparametric regression. The proposed scheme not only provides an effective SPC solution to handle nonlinear profiles, which are common in industrial practice, but it also resolves the latent problem in popular parametric monitoring methods of being unable to detect certain types of changes due to a misspecified, out-of-control model. Our simulation results demonstrate the effectiveness and efficiency of the proposed monitoring scheme. In addition, a systematic diagnostic approach is provided to locate the change point of the process and identify the type of change in the profile. Finally, a deep reactive ion-etching example from semiconductor manufacturing is used to illustrate the implementation of the proposed monitoring and diagnostic approach.     

Autocorrelated SPC for Non‐Normal Situations

The importance of statistical process control (SPC) techniques in quality improvement is well recognized in industry. However, most conventional SPC techniques have been developed under the assumption of independent, identically and normally distributed observations. With advances in sensing and data capturing technologies, large volumes of data are being routinely collected from individual units in manufacturing industries. These data are often autocorrelated and skewed. Conventional SPC techniques can lead to false alarms or other types of poor performance monitoring of such data. There is a great need for process control techniques for variation reduction in these environments. Much recent research has focused on the development of appropriate SPC techniques for autocorrelated data, but few studies have considered the impact of non‐normality on these techniques. This paper investigates the effect of skewness on conventional autocorrelated SPC techniques, and provides an effective approach based on a scaled weighted variance approach to improve SPC performance in such an environment. Copyright © 2005 John Wiley & Sons, Ltd.     

Process monitoring with ICA‐based signal extraction technique and CART approach

In order to reduce the variation in a manufacturing process, traditional statistical process control (SPC) techniques are the most frequently used tools in monitoring engineering process control (EPC)‐controlled processes for detecting assignable cause process variation. Even though application of SPC with EPC can successfully detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this paper, we propose an independent component analysis‐based signal extraction technique with classification and regression tree approach to identify disturbance levels in the correlated process parameters. For comparison, traditional cumulative sum (CUSUM) chart was constructed to evaluate the identifying capability of the proposed approach. The experimental results show that the proposed method outperforms CUSUM control chart in most instances. Copyright © 2008 John Wiley & Sons, Ltd.     

Integrating artificial intelligence into on‐line statistical process control

Statistical process control (SPC) is one of the most effective tools of total quality management, the main function of which is to monitor and minimize process variations. Typically, SPC applications involve three major tasks in sequence: (1) monitoring the process, (2) diagnosing the deviated process and (3) taking corrective action. With the movement towards a computer integrated manufacturing environment, computer based applications need to be developed to implement the various SPC tasks automatically. However, the pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process. The remaining two tasks still need to be carried out by quality practitioners. This project aims to apply a hybrid artificial intelligence technique in building a real time SPC system, in which an artificial neural network based control chart monitoring sub‐system and an expert system based control chart alarm interpretation sub‐system are integrated for automatically implementing the SPC tasks comprehensively. This system was designed to provide the quality practitioner with three kinds of information related to the current status of the process: (1) status of the process (in‐control or out‐of‐control). If out‐of‐control, an alarm will be signaled, (2) plausible causes for the out‐of‐control situation and (3) effective actions against the out‐of‐control situation. An example is provided to demonstrate that hybrid intelligence can be usefully applied for solving the problems in a real time SPC system. Copyright © 2003 John Wiley & Sons, Ltd.     

基于回归方法和神经网络的电弧增材制造单道成形形貌预测

目的 针对电弧增材制造技术实际应用中工艺参数选取困难和成形结果难预测的问题,确定高效、准确的电弧增材制造单道成形形貌预测的数学方法,以快速、方便地选取丝材电弧增材制造工艺参数并指导成形质量控制。方法 在单道单层丝材电弧增材制造实验的基础上,采用多种回归方法和神经网络方法分别建立焊接电流、电压和焊枪移动速度等多个工艺参数与增材层宽度、增材层高度及熔池深度等成形形貌参数之间的数学关系模型。结果 电弧增材制造单道成形形貌与焊接电流、电压和焊枪移动速度显著相关,且各参数间存在非线性交互作用;采用多元线性回归法可较准确地预测单道增材层宽度,但对于增材层高度和熔深的预测效果较差;神经网络可良好地处理各工艺参数间复杂的非线性关系,其对增材层宽度、增材层高度和熔深的预测平均误差率分别为4.17%、6.60%和7.01%,显著优于多元线性回归法。结论 采用神经网络法可以准确预测电弧增材制造单道成形的形貌参数,进而指导增材制造工艺参数的选取和成形质量的控制。     

神经网络方法在自相关过程控制中的应用

将传统休哈特控制图应用于自相关过程控制时,会引发大量虚发报警.本文将使用时间序列模型模拟自相关过程并将神经网络方法引入自相关过程控制中.以神经网络特有的模式识别技术,对自相关过程中均值发生突变的情况进行监控,取得了良好效果.     

Convolutional and generative adversarial neural networks in manufacturing

Manufacturing is undergoing transformation driven by the developments in process technology, information technology, and data science. A future manufacturing enterprise will be highly digital. This will create opportunities for machine learning algorithms to generate predictive models across the enterprise in the spirit of the digital twin concept. Convolutional and generative adversarial neural networks have received some attention of the manufacturing research community. Representative research and applications of the two machine learning concepts in manufacturing are presented. Advantages and limitations of each neural network are discussed. The paper might be helpful in identifying research gaps, inspire machine learning research in new manufacturing domains, contribute to the development of successful neural network architectures, and getting deeper insights into the manufacturing data.     

A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance

Statistical process control (SPC) has natural applications in data network surveillance. However, network data are commonly autocorrelated, which presents challenges to the basic SPC methods. Most existing SPC methods for correlated data assume parametric models to account for the correlation structure within the data. Those model assumptions can be difficult to justify in practice. In this paper, we propose a nonparametric cumulative sum (CUSUM) control chart for autocorrelated processes. In our proposed approach, we incorporate a wavelet decomposition and a nonparametric multivariate CUSUM control chart to obtain a robust procedure for autocorrelated processes without distribution assumptions. Extensive simulations show that the procedure appropriately controls the in‐control average run length and also has good sensitivity for detecting location shifts.     

基于神经网络的电弧增材制造铝合金力学性能预测

目的 预测不同工艺参数下电弧增材制造铝合金的力学性能。方法 通过实验建立了电弧增材制造6061铝合金及Ti C增强6061铝合金力学性能的数据集,并建立了一种以焊接电流、焊接速度、脉冲频率、TiC颗粒含量为输入,以屈服强度和抗拉强度为输出的神经网预测模型,对比了反向传播神经网络(BP)、粒子群算法优化BP神经网络(PSO-BP)、遗传算法优化BP神经网络(GA-BP)3种预测模型的精度。结果 与BP模型和PSO-BP模型相比,GA-BP预测模型具有更好的预测精度。其中,GA-BP模型预测6061铝合金屈服强度最佳结果的相关系数(R)为0.965,决定系数(R²)为0.93,平均绝对误差(Mean Absolute Error,MAE)为2.35,均方根误差(Root Mean Square Error,RMSE)为2.67;预测Ti C增强的6061铝合金抗拉强度最佳结果的R=1,R²高达0.99,MAE为0.46,RMSE为0.49,GA-BP具有良好的预测精度。结论 BP、PSO-BP、GA-BP 3种神经网络模型可以用来预测电弧增材制造...     

Designing a multivariate–multistage quality control system using artificial neural networks

In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate–multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate–multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average run lengths and correct classification ratio indices have been chosen to investigate the performance of the designed network. The results of a simulation study show that the network is capable of detecting both in-control and out-of-control signals appropriately.     

Support vector machines for recognizing shifts in correlated and other manufacturing processes

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries where the data from the facilities are often autocorrelated. This is often true in piece-part manufacturing industries that are highly automated and integrated. Several attempts have been made in the literature to extend traditional SPC techniques to deal with autocorrelated parameters. However, these extensions pose several serious limitations. The literature discusses several machine-learning methods based on radial basis function (RBF) networks and multi-layer perceptron (MLP) networks to address the limitations, with some success. This paper demonstrates that support vector machines (SVMs) can be extremely effective in minimizing both Type-I errors (probability that the method would wrongly declare the process to be out of control or generate a false alarm) and Type-II errors (probability that the method will be unable to detect a true shift or trend present in the process) in these autocorrelated processes. Even while employing the simplest type of polynomial kernels, the SVMs were extremely good at detecting shifts in papermaking and viscosity datasets (available in the literature) and performed as well or better than traditional as well as machine learning methods. It was also observed that SVMs are good at minimizing both Type-I and Type-II errors even in monitoring non-correlated processes. When tested on datasets available in the literature, they once again performed as well or better than the classical Shewhart control charts and other machine learning methods.