CUSUM charts for detecting special causes in integrated process control

This paper investigates control charts for detecting special causes in an ARIMA(0, 1, 1) process that is being adjusted automatically after each observation using a minimum mean‐squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0, 1, 1) process, but it is desirable to detect and eliminate special causes that occur occasionally and produce additional process variation. It is assumed that these special causes can change the process mean, the process variance, the moving average parameter, or the effect of the adjustment mechanism. Expressions are derived for the process deviation from target for all of these process parameter changes. Numerical results are presented for sustained shifts, transient shifts, and sustained drifts in the process parameters. The objective is to find control charts or combinations of control charts that will be effective for detecting special causes that result in any of these types of parameter changes in any or all of the parameters. CUSUM charts designed for detecting specific parameter changes are considered. It is shown that combinations of CUSUM charts that include a CUSUM chart designed to detect mean shifts and a CUSUM chart of squared deviations from target give good overall performance in detecting a wide range of process changes. Copyright © 2009 John Wiley & Sons, Ltd.     

Integrating Customer Perception into Process Capability Measures

Process capability indices provide a measure of the output of an in‐control process that conforms to a set of specification limits. These measures, which assume that process output is approximately normally distributed, are intended for measuring process capability for manufacturing systems. When the performance of a system results in a product that fails to fall within a given specification range, however, the product is typically scrapped or reworked, and the actual distribution that the customer perceives after inspection is truncated. In this paper, the concept of a truncated measure for three types of quality characteristics is introduced as the key to linking customer perception to process capability. Subsequently, a set of customer‐perceived process capability indices is presented as an extension of traditional manufacturer‐based counterparts. Finally, data transformation‐based process capability indices are also discussed. A comparative study and numerical example reveal considerable differences among the traditional and proposed process capability indices. It is believed that the proposed process capability index for various quality characteristics may more aptly lead to process improvement by facilitating a better understanding of the integrated effects found in engineering design problems. Copyright © 2015 John Wiley & Sons, Ltd.     

基于进入过程具有常利率和正则变化索赔的风险模型的渐近破产概率(英文)

本文研究了一类带常利率的,并且索赔过程由进入过程驱动的风险保险模型。在进入过程是一般更新过程以及索赔额是正则尾分布的条件下,得到了当初始资本趋于无穷时,破产概率的渐近行为,类似的结论对于进入过程是齐次泊松过程的情形也同样成立。     

An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement

Process mining (PM) is a technique to extract a process model from an event log to represent the process behaviour recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behaviour recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement and benchmarking. Genetic process mining (GPM) is a famous PM approach, which can simultaneously identify several process structures from event logs. However, GPM cannot effectively discover parallel structures from event logs. This study proposes a PM approach based on integration of GPM, particle swarm optimisation and differential evolution to find process models with high fitness for event logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.     

Laser metal deposition of functionally graded Ti6Al4V/TiC

Functionally graded materials (FGMs) are advanced materials with improved properties that enable them to withstand severe working environment which the traditional composite materials cannot withstand. FGM found their applications in several areas which include: military, medicine and aerospace. Various manufacturing processes are used to produce functionally graded materials that include: powder metallurgy, physical vapour deposition, chemical vapour deposition process and laser metal deposition process. Laser metal deposition (LMD) process is an additive manufacturing process that can be used to produce functionally graded material directly from the three dimensional (3D) computer aided design (CAD) model of the part in one single process. LMD process is a fairly new manufacturing process and a highly non-linear process. The process parameters are of great importance in LMD process and they need to be optimized for the required application. In this study, functionally graded titanium alloy composite was produced using optimized process parameters for each material combination as obtained through a model that was developed in an initial study and the FGM was characterized through metallurgical, mechanical and tribological studies. The results show that the produced FGM has improved properties when compared to those produced at constant processing parameters for all material combinations.     

A kalman filtering process control scheme with an application in semiconductor short run manufacturing

A quality control chart for monitoring a short run process during the start-up phase is presented in this article. The chart is based on the Kalman filter recursive equations being applied to a stable process where the process variance is unknown prior to the start of the production run. The run length properties of this control scheme are discussed. It is shown that for the proposed scheme the run length properties are independent of the unknown process variance and that these properties are appropriate for monitoring a stable process during start-up. An economic model for the optimal design of the control scheme is presented and illustrated with a wet etching process used in semiconductor manufacturing.     

Applying a reusable framework for software selection

With increasing use of component-based development (CBD), the process for selecting software from repositories is a critical concern for quality systems development. As support for developers blending in-house and third party software, the context-driven component evaluation (CdCE) process provides a three-phase approach to software selection: filtering to a short list, functional evaluation and ranking. The process was developed through iterative experimentation on real-world data. CdCE has tool support to generate classifier models, shortlists and test cases as artefacts that provide for a repeatable, transparent process that can be reused as the system evolves. Although developed for software component selection, the CdCE process framework can be easily modified for other selection tasks by substituting templates, tools, evaluation criteria and/or repositories. In this article the authors describe the CdCE process and its development, the CdCE framework as a reusable pattern for software selection and provide a case study where the process is applied.     

A Generalized Quasi-MMSE Controller for Run-to-Run Dynamic Models

This study proposes a generalized quasi-minimum mean square error (qMMSE) controller for implementing a run-to-run process control where the process input–output relationship follows a general-order dynamical model with added noise. The expression of the process output, the long-term stability conditions and the optimal discount factor of this controller are derived analytically. Furthermore, we use the proposed second-order dynamical model to illustrate the effects of mis-identification of the process I-O model on the process total mean square error (TMSE). Via a comprehensive simulation study, the model demonstrates that the TMSE may inflate by more than 150% if a second-order dynamical model with moderately large carryover effects is wrongly identified as that of a first-order model. This means that the effects of mis-identification of the process I-O model on the process total mean square error (TMSE) is not negligible for implementing a dynamic run-to-run (RTR) process control. Supplementary materials for this article are available online.     

Intelligent model-based control of preform permeation in liquid composite molding processes, with online optimization

Manufacturing of quality products via liquid molding processes such as Resin Transfer Molding (RTM), calls for a precise control of resin progression through fibrous preforms during mold fill. Lack of an effective process control leads to formation of dry spots and voids that are detrimental to product quality. This study presents the use of physics-based process simulations in real-time, towards a generalized process control. The implementation of process simulations for on-line model-predictive control requires that the simulation time scales be less than the time scales of the process. An artificial neural network trained using data from numerical process models is used to provide rapid, real-time process simulations for the model-based control. A simulated annealing algorithm, working interactively with the neural network process model, is used to derive optimal control decisions rapidly and on-the-fly. The controller performance is systematically demonstrated for several processing scenarios.     

基于KBE的汽车覆盖件冲压工艺方案设计

汽车覆盖件的冲压工艺方案设计是一个复杂且经验性强的创造性过程,需要一种支持多种知识表达模式和多智能决策技术的方法,解决工艺方案决策问题.通过以工艺特征为核心来表达知识,建立了基于KBE的覆盖件工艺知识模型,采用基于知识驱动决策树表达模式的特征转换机制来实现设计特征到工艺特征的映射,在工艺知识库的支持下完成工艺方案设计.实现了知识的多种描述形式及其应用,有效地解决了工艺方案设计中基于图形、规则及经验知识的决策判断,提高了汽车覆盖件工艺方案设计的智能化程度.     

Process development: a theoretical framework

There have already been a number of review articles that focus on product development, but none on process development. Process development is technically difficult, time-critical and competitively important, thus worth being explored as a distinct unit of analysis. This paper develops a theoretical framework to fill this gap in knowledge based on a comprehensive literature review. Five objectives for process development are summarised to align with the goal of product realisation, and 12 enablers (including intra-functional enablers, inter-functional enablers, and learning enablers) are identified and discussed. The authors first locate process development in the product realisation process (including product design, process design, process execution and process improvement) and draw necessary parallel factors between product design and process design as a foundation. Then the attributes of all relevant literature are organised and general principles for successful performance of development projects are used to infer the enablers. The developed framework offers opportunities to further verify the new enablers and theoretical links in future studies. One implication is that process development should be viewed as a strategic function for competitive edge and as such specific measures should be set to create an environment that is conducive to successful performance.     

A green productivity based process planning system for a machining process

In the metal cutting industry, manufacturers have strived to increase energy efficiency and to reduce environmental burdens through the use of dust collectors and waste disposers. It is more beneficial and efficient to apply the front-of-pipe technology that prevents the sources of pollutants and minimises energy use through the redesign of products and the change of process planning and machining operations. In particular, process planning for the environment, called eco-process planning, is central to increasing energy efficiency and reducing environmental burdens because process planning decisions greatly influence machining performance. At present, greenability, a term used to indicate environmental friendliness, has been little considered as a major concern in the process planning stage because process planning decisions have focused on improving productivity aspects that include speed, cost and quality. Thus, it is essential to develop an eco-process planning approach that enables the harmonisation and enhancement of greenability performance while improving productivity performance, termed green productivity (GP). This paper presents the development of a GP-based process planning algorithm that enables the derivation of process parameters for improving GP in machining operations. The core mechanism of the algorithm is the realisation of the process improvement cycle that measures GP performance by the collection of machining data, quantifies this performance by categorical representation and predicts the performance through prediction models. To show the feasibility and applicability of the proposed algorithm, we have conducted an experiment and implemented a prototype system for a turning machining process.     

Multiresponse optimization of a multistage manufacturing process using a patient rule induction method

Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage affects the stage that follows, and the process often has multiple response variables. In this paper, we suggest a new procedure for optimizing a multistage process with multiple response variables. Our method searches for an optimal setting of input variables directly from operational data according to a patient rule induction method (PRIM) to maximize a desirability function, to which multiple response variables are converted. The proposed method is explained by a step-by-step procedure using a steel manufacturing process as an example. The results of the steel manufacturing process optimization show that the proposed method finds the optimal settings of input variables and outperforms the other PRIM-based methods.     

An alternative mean estimator for processes monitored by SPC charts

Statistical Process Control (SPC) chart is a graphical tool that helps to identify a possible process shift. Though the SPC chart is, in theory, a monitoring tool that reveals only the result of a statistical hypothesis testing, in practice the chart's signaling is often used for making process adjustment. In this paper we develop a robust estimator of the process mean for processes monitored by SPC charts. This mean estimate can serve as an important reference for investigating assignable causes and taking appropriate corrective actions. When the control chart is implemented as a control device, the mean estimate can also provide a more accurate assessment of the amount of adjustment to be made to the process. We also demonstrate in this paper that when the process shift is relatively small, the proposed mean estimate outperforms the mean estimate usually used for the CUSUM scheme.     

Optimal process capability analysis for process design

Conventional process capability analysis is used to measure and control the quality level of a production process in real exercises for on-line quality management. There has been a deficiency in this type of management; namely, the defects which occur in the production process are only passively detected and modified afterwards. Additionally, conventional process capability expression has difficulty distinguishing between alternatives for process selection among possible candidates before process realisation. There is, therefore, considerable motivation for developing a process capability expression which can be used to evaluate alternatives at the beginning of the process design, i.e., off-line application. The conventional C_pm expression is built up by measuring mean deviation and process variances for on-line application. However, if C_pm is used for the process capability analysis for process design, an erroneous C_pm value is found and an inappropriate process design is ended. Thus, the proposed process capability expression revised from the conventional C_pm in consideration of the balance between tolerance cost and quality loss has been developed. This development is the main contribution of this research and, with this development, the appropriate mean and tolerance values can be determined simultaneously prior to the real production process so as to maximise the proposed process capability value. The production is then processed with the pre-determined mean and tolerance values in a real production process. The expectation after process realisation is that the produced responses will be the best of all the alternatives in terms of quality and cost, and that the process capability value obtained after the real production process will be close to the proposed process capability value maximised prior to the real production process.     

Framework on robotic percussive riveting for aircraft assembly automation

Presented in this paper is a framework for the implementation of a robotic percussive riveting system, a new robot application for aircraft assembly. It is shown here that a successful robot application to the automation of a process requires in-depth research of the process and the interaction with the robot. For this purpose, a process planning-driven approach is proposed to guide a robot application research.Atypical process planning will involve a list of
key considerations including: process sequence, process parameters, process tooling, and process control. Through this list, a number of key research issues are identified for robotic percussive riveting, such as rivet pattern planning,rivet time determination, rivet tooling design and rivet insertion control. The detailed research on these issues has effectively created know-how for the successful implementation of our robotic percussive riveting system.     

与CMOS兼容的MEMS气压传感器工艺实现与性能分析

采用CMOS标准工艺,同时采用三种典型MEMS后处理关键工艺,重点通过对牺牲层释放工艺进行研究,制作实现了一种新型CMOS兼容的电容式气压传感器．在该传感器结构中,作为牺牲层的是在CMOS工艺中形成的掺硼氧化硅．通过释放使电容上电极悬空从而感应气压变化．释放过程采用氢氟酸HF、氯化铵、甘油和水的混合溶液．由于释放孔大小和释放孔间距的设计十分关键,通过实验验证优化了4μm×4μm的释放孔更适用于此传感器结构,并对此结构进行了性能分析与实验测试．结果表明,该气压传感器结构合理,工艺成功,重点解决了MEMS后处理中的牺牲层释放工艺与CMOS标准工艺的兼容问题,为利用CMOS标准工艺进行MEMS传感器的研制做出了有益的尝试．     

Optimum arrangement of gate and vent locations for RTM process design using a mesh distance-based approach

Resin transfer molding (RTM) is considered a promising manufacturing process for high performance composite materials. In the RTM process, gate/vent location is one of the most important variables in process design. It has a great impact on mold filling time and resin flow pattern, thus affects the process efficiency and product quality. Many studies have been conducted on optimization of RTM mold filling process to determine the locations of gates and vents. Most of them approached the optimum process design problem via numerical method-based process simulations. Due to the extensive computations involved, such approaches are difficult to be implemented for large and complex parts. This paper introduces a new approach to optimum arrangement of gates and vents for two-dimensional or two and a half-dimensional parts based on the mesh distance concept. With the assumption that the resin first fills the nodes closest to gates, the vent locations that minimize the area of trapped air can be determined. With the objective of minimizing the maximum distance between gates and vents and avoiding dry spot formation, genetic algorithm was employed to carry out the gate location optimization. The new RTM process design approach was tested on numerous cases obtained from the literature. It was found that the new approach was very efficient and effective in finding the optimal locations for gates and vents. The resulting gate and vent locations lead to satisfactory process performance as indicated by the optimal process performance index. The computational time required by the new approach was only a fraction of that reported by those simulation-based methods in the literature.     

A magnitude‐robust control chart for monitoring and estimating step changes for normal process means

Statistical process control charts are intended to assist operators of a usually stable system in monitoring whether a change has occurred in the process. When a change does occur, the control chart should detect it quickly. If the operator can also be provided information that aids in the search for the special cause, then critical off‐line time can be saved. We investigate a process‐monitoring tool that not only provides speedy detection regardless of the magnitude of the process shift, but also supplies useful change point statistics. A likelihood ratio approach can be used to develop a control chart for permanent step change shifts of a normal process mean. The average run length performance for this chart is compared to that of several cumulative sum (CUSUM) charts. Our performance comparisons show that this chart performs better than any one CUSUM chart over an entire range of potential shift magnitudes. The likelihood ratio approach also provides point and interval estimates for the time and magnitude of the process shift. These crucial change‐point diagnostics can greatly enhance special cause investigation. Copyright © 2002 John Wiley & Sons, Ltd.