A run-to-run control algorithm based on timely and delayed mixed-resolution information

In certain run-to-run (R2R) processes, timely accurate measurements are difficult to obtain due to slow laboratory measurement operations. Instead, only low-resolution categorical observations are observed online for important quality variables; continuous measurements for the same variables are provided after a specific amount of delay. Currently, most conventional R2R controllers cannot be applied if no continuous observations are available. It is therefore important to develop online algorithms for R2R process control based on mixed-resolution information that is partially timely and partially delayed. In this study, we take the lapping process in semiconductor manufacturing as an example and propose parameter estimation models with these mixed-resolution data for processes with the first-order autoregressive, AR(1), disturbance series. We also derive control strategies to generate recipes between production runs for better process control. The computational results of a performance evaluation show that the control performance of the proposed method is competitive compared to existing methods that are based on accurate measurements.     

Automation of a dosing-disc capsule filler from the perspective of reliability and safety

The aim of this paper is to explore the possibility to develop an automatically adjustable, reliable, and safe capsule filling operation. Process parameters that are critical for the tamping pin process were reviewed based on the literature and via experiment. Dosing disc height, powder bed height, machine speed, pressure on the tamping pin, and immersion depth were reviewed. Two investigations were performed on a GKF 702. In the first one, the powder feed rate onto the dosing disc was examined and modified. A distance sensor with a PID controller enabled a constant powder bed level with an online changeable set point. For a bad flowing product an improvement of the fill weight variation could be achieved by automatically adjusting the feed rate to the correct speed and matching the actual process conditions of the capsule filler. The second part of the study concerned the safety of the filler operation. Introducing a force transducer on the transfer station is a promising option for running the capsule filler safely within its process specifications. The tamping pin pressure was used to provoke different transfer forces. A deviation from a defined process specification led to a safe stop of the machine. In summary, the automated adjustment of several critical process parameters appears to be feasible and supports the rational development of efficient production processes using a dosing disc capsule filler. This is especially relevant for continuous production of pharmaceuticals.     

Optimal levels of process parameters for products with multiple characteristics

The purpose of off-line quality control is to design robust products using robust manufacturing processes before the actual manufacturing of the product. Most of the research work has focused on determining the optimal level settings of process parameters for products with a single quality characteristic. In this paper, we employ the loss function approach to determine the optimal level settings of the process parameters of the production processes for products with multiple characteristics.     

A Synthesis of Feedback and Feedforward Control for Process Improvement Under Stationary and Nonstationary Time Series Disturbance Models

Process adjustment strategy is an important part of the process improvement methods. The feedback control technique is used to compensate for the deviation of the output, and it has been intensively investigated. For continuous improvement and proactive strategies, feedback control has a delay and thus is not the ideal solution. In this article, motivated by a realistic manufacturing example, we propose the periodic shift disturbance models and investigate the feedforward control application from a new disturbance decomposition framework. We combine feedforward control with feedback control for maintaining the stability of the process and delivering products at target values. Then, we evaluate the performance of different control strategies for various disturbance models by using the output mean square error criterion. Sensitivity analysis of these control methods is made on different model parameter spaces, and robustness analysis for both model parameter and model structure misspecifications is presented. Two simulated examples show that the proposed control strategies can significantly reduce the variation of an evolving disturbance process. Copyright © 2014 John Wiley & Sons, Ltd.     

A computational control implementation environment for automated manufacturing systems

This paper presents a computational environment as a tool for supporting the implementation of control coding of an automated manufacturing system. The proposed environment considers a cyclic three-stage control development – modelling, synthesis and implementation – until the real system accomplishes the required specification, resulting in the automated and integrated manufacturing system. The research details the three stages and describes the steps executed for each one. The mathematical formalism used in this work is also presented, as a basis for control implementation. The implementation environment is proposed in order to validate the control structure of this formalism and to allow a progressive integration of control hardware and software. To submit to a test and validate the proposal environment, two experiments are performed, in two different manufacturing systems. Thus, it is demanded that the control system can be reconfigurable in a fast and reliable way.     

Optimal initial production control period based on a quality growth model

The initial production phase of new products or the initial installation phase of new manufacturing facilities is often unstable because of inexperienced workers and many defective products. An initial production process control, in which the defects in design, production technologies and products are fully fixed and removed, is switched to a normal process control whenever it is ready for actual mass production. This paper discusses a method of deciding the optimal initial production control period, based on a quality growth model. It is determined by the number of products with the minimum expected total quality control cost. Finally a penalty cost due to unattainable loss to the quality goal is introduced in the quality control cost: the realized stabilization level of the initial production process control is lower than the original quality objective. Numerical illustrations of the optimal policy are also presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Some Modifications of the Classical Pre‐control Technique

Pre‐control is a simple technique for the initial evaluations of the capability of a process. It can be seen as a tool to get the set‐up approval or fulfilment of the specifications of a production process. As the resultant information of pre‐control should be used to adjust the process, it can be understood as a form of feedback controller. It has sometimes been considered as an alternative to statistical control charts for monitoring processes, although these tools differ in a number of ways. In this work, we propose some new alternatives to the classical pre‐control, particularly in its initial phase that aim to qualify the process, that is, to certify that it is capable. We present a comparative analysis of the power of the different alternatives. Copyright © 2003 John Wiley & Sons, Ltd.     

Optimal process control for multiple dependent subprocesses

A renewal theory approach is proposed to derive the cost model for multiple dependent subprocesses. The optimal individual Y control chart and multiple cause-selecting control chart are thus constructed to monitor the overall product quality and specific product quality contributed by the multiple dependent subprocesses. They can be used to maintain the process with minimum cost and effectively distinguish which component of the subprocesses is out of control. The optimal design parameters of the proposed control charts can be determined by minimizing the cost model using a simple grid search method. An example is given to illustrate the application of the optimal individual Y control chart and multiple cause-selecting control chart. © 1998 John Wiley & Sons, Ltd.     

Metrological Assurance of Optimal Technological Process Control

The effectiveness of optimal control of technological process is shown to depend on the metrological characteristics of the measuring instruments chosen to monitor the optimal settings of the variables. Methods have been developed for determining the requirements relating to the metrological characteristics of the measuring instruments employed so as to ensure that the optimization is acceptably effective.     

Recent Developments in the Crystallization Process: Toward the Pharmaceutical Industry

Crystallization is one of the oldest separation and purification unit operations, and has recently contributed to significant improvements in producing higher-value products with specific properties and in building efficient manufacturing processes. In this paper, we review recent developments in crystal engineering and crystallization process design and control in the pharmaceutical industry. We systematically summarize recent methods for understanding and developing new types of crystals such as co-crystals, polymorphs, and solvates, and include several milestones such as the launch of the first co-crystal drug, Entresto (Novartis), and the continuous manufacture of Orkambi (Vertex). Conventional batch and continuous processes, which are becoming increasingly mature, are being coupled with various control strategies and the recently developed crystallizers are thus adapting to the needs of the pharmaceutical industry. The development of crystallization process design and control has led to the appearance of several new and innovative crystallizer geometries for continuous operation and improved performance. This paper also reviews major recent progress in the area of process analytical technology.     

Dynamic control in automated manufacturing: a knowledge integrated approach†

Flexibly automated facilities permit a wider variety of products as well as objectives for making those products—thus requiring manufacturing control strategies to face an environment of ever present change. To operate in this environment, a system composed of hard automation, flexible automation and humans, which can be responsive to product and process requirements, machine breakdowns and delays, engineering changes and improvement opportunities, is needed. Such a system does not fall into the realm of any current manufacturing solution techniques. Something more than exact optimization, heuristic algorithms or stochastic estimates must be utilized. The research discussed herein describes a dynamic solution strategy to operate in this changing environment with adaptive self-improving characteristics. The proposed methodology for optimizing the control of an automated manufacturing facility is an integrated approach utilizing real-time feedback from the operating facility, direct feedback from a simulation of the facility and guidance from a historical knowledge base. This system is being implemented in a knowledge based environment called CAYENE. CAYENE is a hybrid artificial intelligence system, written in Lisp, based on the idea of using object oriented programming as a unifying principle for functional, frame and rule-based programming.     

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.     

Relationships Among Control Charts Used with Feedback Control

Feedback control is common in modern manufacturing processes and there is a need to combine statistical process control in such systems. Typical types of assignable causes are described and fault signatures are calculated. A fault signature can be attenuated by the controller and an implicit confounding among faults of different types is discussed. Furthermore, the relationships between various control statistics are developed. Control charts have been proposed previously for deviations from target and for control adjustments. We describe why one or the other can be effective in some cases, but that neither directly incorporates the magnitude (or signature) of an assignable cause. Various disturbance models and control schemes, both optimal and non‐optimal, are included in a mathematically simple model that obtains results through properties of linear filters. We provide analytical results for a widely‐used model of feedback control. Copyright © 2006 John Wiley & Sons, Ltd.     

Cost modelling for feedback control adjustment

Process control is a management problem in the performance analysis of a continuous production process. Statistical process monitoring and feedback control recently have been of interest to process control practitioners. It is imperative for the process control practitioner to know the costs of sampling and adjusting a process in order to minimize the manufacturing cost without compromising the objectives of process control. This view is kept as a basis when proposing a cost modelling methodology using some of the sampling and cost modelling principles available in literature. The sampling (adjustment) intervals are obtained by direct simulation of a feedback control algorithm in the proposed methodology. The algorithm is developed by the application of statistical and automatic process control techniques. The suggested method to develop the cost model and the associated cost functions uses adjustment intervals and is less complex. The cost function for feedback control adjustment is based on a second-order transfer function process model and a first-order noise model. The cost model and a corresponding control regulation scheme have applications in basis-weight control on a paper machine and in a batch-polymerization process that produces polymer resins in two groups of batch reactors that run in parallel and share common raw materials.     

Directional control schemes for processes with mixed-type data

Mixed-type data consisting of both continuous observations and categorical observations are becoming prevalent in manufacturing processes and service management. The majority of existing statistical process control tools are designed to monitor either continuous data or categorical data but seldom both. In this article, we propose a directional exponentially weighted moving average control scheme composed of monitoring and diagnosis for mixed-type data. We assume that there is a latent unknown continuous distribution that determines the attribute levels of a categorical variable, and represent both continuous data and categorical data by standardised ranks. The proposed control chart also incorporates directional information to facilitate diagnosing the shift direction. Monte Carlo simulations demonstrate the efficiency of the proposed control scheme.     

Real time production improvement through bottleneck control

Variability is a key characteristic for evaluating the performance of a process. Small variability for a bottleneck machine can generate high production variability. Short-term production analysis and bottleneck identification are imperative for enabling optimal response to dynamic changes within the system. In comparison to the rich and abundant literature available on long-term analysis, only a small section of the literature addresses the dynamic bottleneck control policies, which may be used to maximise sustainable benefits. In this paper, a real time bottleneck control method is introduced to efficiently utilise the finite manufacturing resources and to mitigate the short-term production constraints by using two practical approaches: initial buffer adjustment and maintenance task prioritisation. The objective for real time bottleneck control is to obtain a continuous production improvement towards a balanced-line status to increase the throughput efficiently. The benefits of this method are presented by considering an industrial case study of an automotive assembly line. The results obtained from this case study show significant production improvements as compared to traditional approaches.     

Statistical monitoring and control of a low defect process

Advanced technologies today are such that it is possible to keep the occurrence of defects in manufactured products at very low levels. The use of the conventional c-chart for statistical control of defects in such products would encounter serious practical difficulties because the low defect counts would render invalid the theoretical assumptions used in the construction of the chart. Based on reasoning with fundamental probability distributions, this paper offers a simple and reliable solution that is particularly suited to on-line inspection and testing operations such as those found in an automated manufacturing environment.     

Bayesian real options control chart

In this paper, we present the concept of a novel control chart, which uses economic considerations within the real options–inspired framework together with the principles of Bayesian statistics to produce a continuously updated estimate of the parameters of the actual process, and thus to decide whether to continue running the process or to recalibrate it instead. Bayesian estimate allows the decision maker to combine prior information about the process with the continuously incoming data in a natural flexible manner. In the real options framework, at any given moment, we compare the cost of recalibrating the process to the cost of postponing the (optimal) decision for later. The decision is thus based on cost‐benefit analysis rather than statistically significant deviations from the in‐control process. To have a clear focus on the conceptual representation of the novel methodology, we consider a continuously sampled binary process. We derive the algorithm for the control chart, which can also, in this discrete setting, be represented as a table, a matrix, or a tree. We also investigate the performance of the method in different settings with particular attention being paid to the role of Bayesian prior. Being flexible in prior beliefs leads to better results anywhere outside of the in‐control process. Together, Bayesian paradigm and dynamic decision‐making approach create a realistic representation of a real‐life decision‐making process.     

EWMA control chart limits for first‐ and second‐order autoregressive processes

Today's manufacturing environment has changed since the time when control chart methods were originally introduced. Sequentially observed data are much more common. Serial correlation can seriously affect the performance of the traditional control charts. In this article we derive explicit easy‐to‐use expressions of the variance of an EWMA statistic when the process observations are autoregressive of order 1 or 2. These variances can be used to modify the control limits of the corresponding EWMA control charts. The resulting control charts have the advantage that the data are plotted on the original scale making the charts easier to interpret for practitioners than charts based on residuals. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal inspection of a finite population

This article presents a method for determining an optimal quality control (QC) inspection frequency for a manufacturing process where a specified number of items will be produced and where, if the process goes out of control and generates a defective item, it stays out of control. In addition, there is a QC inspection procedure than can detect a defective item. The frequency of inspection is based on minimizing the total cost. The total cost includes the cost of QC inspections plus the cost of manufacturing defective items. In this application the cost of manufacturing defective items is, after identifying a defective item, the cost of checking previously manufactured items until it is determined when the process went out of control, i.e., until a good item is found.