Scheduling methods for the statistical setup adjustment problem

Feedback control methods have been proposed in recent literature to regulate the quality characteristic of parts or products in a manufacturing process. Depending on the costs involved, adjustments may not be needed at each time instant (i.e. for every part or product). This paper presents scheduling methods to determine the optimal time instants for adjusting a process. The focus is on the set-up adjustment problem, in which it is necessary to adjust the process in order to compensate for an initial offset that occurs due to an incorrect set-up operation. The performance of three scheduling methods are compared in terms of the expected manufacturing cost and computational effort of each method. The adjustment methods considered are based on estimates of the process variance and the size of the offset. The robustness of these methods with respect to biased estimates of the process variance and of the set-up error or offset are discussed. One simple method, a backward implementation of the Silver-Meal heuristic used for inventory control is recommended based on a performance analysis.     

Using Confidence Intervals to Compare Process Capability Indices

Process capability indices are widely used to measure process performance. In situations such as selecting a supplier and assessing process improvement, it is of interest to compare capability indices for two different processes or the same process before and after an adjustment. In this paper, we consider several methods for performing this comparison on the indices C_pk and C_pm. The methods are compared using a computer simulation. Recommendations are provided for selecting an appropriate method based on power and test size computations.     

Robust kernel distance multivariate control chart using support vector principles

It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false-negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process monitoring.     

Process analysis,monitoring and diagnosis,using multivariate projection methods

Multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance are becoming more important because of the availability of on-line process computers which routinely collect measurements on large numbers of process variables. Traditional univariate control charts have been extended to multivariate quality control situations using the Hotelling T² statistic. Recent approaches to multivariate statistical process control which utilize not only product quality data (Y), but also all of the available process variable data (X) are based on multivariate statistical projection methods (principal component analysis, (PCA), partial least squares, (PLS), multi-block PLS and multi-way PCA). An overview of these methods and their use in the statistical process control of multivariate continuous and batch processes is presented. Applications are provided on the analysis of historical data from the catalytic cracking section of a large petroleum refinery, on the monitoring and diagnosis of a continuous polymerization process and on the monitoring of an industrial batch process.     

Review of Text Classification Methods on Deep Learning

Text classification has always been an increasingly crucial topic in natural language processing. Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion, data sparsity, limited generalization ability and so on. Based on deep learning text classification, this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based (CNN-Based), Recurrent Neural Network-Based (RNN-based), Attention Mechanisms-Based and so on. Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets. The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data. In this paper, we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing, distributed representation of text, text classification model construction based on deep learning and performance evaluation.     

企业流程再造的熵模型

行比较分析的基础上,提出了适应于企业流程要素关系研究的4点假设。给出了流程熵计算的基本步骤,讨论了降低流程熵值的可操作性办法。结合实例验证了该研究的有效性。     

材料塑性成形时敏感性分析研究现状

敏感性分析在材料塑性成形过程中的工艺优化设计及控制方面有重要的应用。综述了材料塑性成形时敏感性分析方法及各种方法在工艺优化设计中的应用,介绍了国内外学者基于敏感性分析在微观组织的优化控制、预成形优化设计、模具优化设计以及参数设计等方面所取得的研究进展。     

A time-series approach to discrete real-time process quality control

Statistical process control methods are usually applied in an environment when periodic sampling and rational subgrouping of process output is appropriate. The resulting summary statistics can be graphically displayed and analysed using either traditional Shewhart control charts or other charts such as those based on the cumulative sum. This article presents an alternative approach, based on time series analysis of all the real-time process data. The time series approach is employed because the sequence of process observations may not be statistically independent. The autocorrelative structure in the data may be captured using an ARIMA model, and the residuals from this model are shown to be an effective input signal for a variety of statistical process control procedures.     

含有柔性电缆的复杂系统装配仿真

将装配单元划分为“刚性组件”和“柔性电缆”,从装配对象建模、装配工艺规划和装配过程模拟三个方面对复杂系统的装配仿真方法进行系统的研究。首先,根据刚性组件和柔性电缆的不同特点,给出相应的建模方法,即针对刚性组件的CAD三维建模方法和针对柔性电缆的虚拟布线方法;然后,将装配过程视为拆卸过程的逆过程,提出含有柔性电缆的复杂系统的装配工艺规划方法,其中考虑了电缆形变过程及相应的模拟方法;最后,以装配工艺为基础对仿真对象进行装配仿真。实例表明,所提出的方法可以用于含有柔性电缆的复杂系统装配仿真。     

Comparing Confidence Intervals for Multivariate Process Capability Indices

Multivariate process capability indices (MPCIs) are needed for process capability analysis when the quality of a process is determined by several univariate quality characteristics that are correlated. There are several different MPCIs described in the literature, but confidence intervals have been derived for only a handful of these. In practice, the conclusion about process capability must be drawn from a random sample. Hence, confidence intervals or tests for MPCIs are important. With a case study as a start and under the assumption of multivariate normality, we review and compare four different available methods for calculating confidence intervals of MPCIs that generalize the univariate index C_p. Two of the methods are based on the ratio of a tolerance region to a process region, and two are based on the principal component analysis. For two of the methods, we derive approximate confidence intervals, which are easy to calculate and can be used for moderate sample sizes. We discuss issues that need to be solved before the studied methods can be applied more generally in practice. For instance, three of the methods have approximate confidence levels only, but no investigation has been carried out on how good these approximations are. Furthermore, we highlight the problem with the correspondence between the index value and the probability of nonconformance. We also elucidate a major drawback with the existing MPCIs on the basis of the principal component analysis. Our investigation shows the need for more research to obtain an MPCI with confidence interval such that conclusions about the process capability can be drawn at a known confidence level and that a stated value of the MPCI limits the probability of nonconformance in a known way. Copyright © 2011 John Wiley & Sons, Ltd.     

Nanoparticles--production and role in biotransformation

Renewed interest has arisen in the manufacture of nanoparticles due to their unusually enhanced physico-chemical properties and biological activities compared to the bulk parent materials. The industrial scale production and wide variety of application of nanoparticles has resulted in broad range applications in biotechnology, more recently in the increase in efficiency of biotransformation processes. Biotransformation processes utilized to form different bio-products and nanoparticles demonstrate various roles in the bio-products formation. In order to address the issue, it is necessary to understand the different methods available for synthesis of nanoparticles and their effects on biotransformation process, an efficient process for utilization of nanoparticles. In this review, an overview of physical, chemical and biological methods for synthesis of nanoparticles and their role in biotransformation process on formation of different bio-products, such as bioethanol, biohydrogen, biodiesel, enzymes and bioplastics is outlined. In fact, the nanoparticles are going to prove revolutionary in the field of biotransformation by improving the efficiency and yield and often widening the application range.     

THE USE OF STATISTICAL PROCESS CONTROL AND DESIGN OF EXPERIMENTS IN PRODUCT AND PROCESS IMPROVEMENT

Quality and productivity improvement has become an essential element of the overall strategic plan for most organizations. This has sparked renewed interest in statistical methods for quality improvement. This paper reviews some recent developments in statistical methodology that have application in product and process improvement, concentrating on statistical process control and design of experiments. Some directions for future research are identified.     

TRIZ在管理流程优化中的应用

 TRIZ理论是基于专利分析提出的用于解决发明创造问题的方法论,已经在工程技术领域取得丰硕成果.随着TRIZ研究的不断深入,其基本思想和分析工具已开始在管理领域使用.由于管理问题非结构化的特点,TRIZ工具和解决问题的流程很难直接应用到管理问题中,缺乏TRIZ在管理领域应用的系统化方法.在分析大量文献的基础上,将TRIZ解决问题的一般流程和六西格玛的DMAIC流程作为基础,提出一种TRIZ在管理流程优化中的应用流程——DTSIC （define-translate-solve-improve-control,定义—转化—解决—改进—控制）.并详细分析了每一阶段可以使用的TRIZ方法和工具以及可以结合的管理流程优化工具,给出了一些选择工具的原则.该流程将管理方法与TRIZ原理和工具包有效结合,在不断循环过程中使流程得到改善.     

Adaptive 2D finite element simulation of metal forming processes

The development and integration of available current methods and the development of new methods for an adaptive finite element analysis of metal forming processes are presented. The analysis includes large-strain, elastic–plastic, and thermal effects. Many numerical methods such as mesh generation, simulation of the contact between the workpiece and tool and die, and optimization of the finite element mesh are integrated and incorporated. In addition, an algorithm is developed which can detect certain severely distorted elements where the area of integration is approaching zero. The advantage of correcting these regions of locally distorted elements is demonstrated. These numerical methods are implemented in a finite element program developed for simulating metal forming processes, with the emphasis on automating the analysis. Examples include an axisymmetric stress simulation of a coldheading process, a plane strain simulation of an extrusion process and a plane strain simulation of orthogonal metal cutting, all with noticeable thermal effects. The orthogonal cutting forces and feed forces calculated are compared with two sets of experimental data, with good agreement.     

Fast simulation of incremental sheet metal forming by adaptive remeshing and subcycling

In order to use incremental sheet forming (ISF) in an industrial context, it is necessary to provide fast and accurate simulation methods for virtual process design. Without reliable process simulations, first-time right production seams infeasible and the process loses its advantage of offering a short lead time. Previous work indicates that implicit finite element (FE) methods are at present not efficient enough to allow for the simulation of AISF for industrially relevant parts, mostly due to the fact that the moving contact requires a very small time step. Finite element methods based on explicit time integration can be sped up using mass or time scaling to enable the simulation of large-scale sheet metal forming problems. However, AISF still requires dedicated adaptive meshing methods to further reduce the calculation times. In this paper, an adaptive remeshing strategy based on a multi-mesh method is developed and applied to the simulation of AISF. It is combined with subcycling to further reduce the calculation times. For the forming of a cone shape, it is shown that savings in CPU time of up to 80 % are possible with acceptable loss of accuracy, and that the simulation time scales more moderately when the part size is increased, so that larger, industrially relevant parts become feasible.     

Process safety improvement--quality and target zero

Process safety practitioners have adopted quality management principles in design of process safety management systems with positive effect, yet achieving safety objectives sometimes remain a distant target. Companies regularly apply tools and methods which have roots in quality and productivity improvement. The "plan, do, check, act" improvement loop, statistical analysis of incidents (non-conformities), and performance trending popularized by Dr. Deming are now commonly used in the context of process safety. Significant advancements in HSE performance are reported after applying methods viewed as fundamental for quality management. In pursuit of continual process safety improvement, the paper examines various quality improvement methods, and explores how methods intended for product quality can be additionally applied to continual improvement of process safety. Methods such as Kaizen, Poke yoke, and TRIZ, while long established for quality improvement, are quite unfamiliar in the process safety arena. These methods are discussed for application in improving both process safety leadership and field work team performance. Practical ways to advance process safety, based on the methods, are given.     

Multiple-jet electrospinning methods for nanofiber processing: A review

Electrospinning is a versatile method of producing continuous nanofibers through the application of an electric field. The simplest and earliest form of electrospinning involves the use of a single-needle spinneret to draw the solution out and form fibers. The process, however, is very time consuming, limiting the potential wide-scale application of electrospun nanofibers. Many multiple-jet electrospinning methods have been proposed to overcome the problem of low productivity, but the generation of multiple jets brought about other problems such as jet repulsion, lower process controllability, and deterioration of fiber quality. Thus, continuous development is necessary for the optimization of the process. The aim of this review is to provide an overview of the recent electrospinning technologies reported in the literature, particularly on various multiple-jet electrospinning methods from both nozzles and free surfaces. The general principle underlying the electrospinning process, as well as important parameters involved are also presented in this paper.     

电子废弃物资源化处理现状

伴随着信息技术的高速发展,电子废弃物的种类和总量日益增多,它具有数量多、危害大、潜在价值高、回收利用困难等特点。本文中分析了火法处理、湿法处理、机械处理、热裂解等电子废弃物资源化处理技术,并指出了不同技术的优缺点,重点介绍了机械处理、湿法处理及热裂解处理方法工艺特点,同时对最新研究进展作了简要评述和展望。     

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。