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Causality is important in many engineering applications for the optimization, robustness, and control of manufacturing processes. Randomized experiments have been the conventional tool for establishing and quantifying causal effects. With great advances in sensorics and information technology, the temptation to use an unprecedented amount of observational data for this purpose has been growing. Most classically trained data scientists are warned against such practice as jumping from correlation to causality is presented as a treacherous leap. Yet causal inference based on observational data has been of great interest in, for example, social and medical studies for which randomized experiments can be infeasible or even unethical. In this Quality Quandaries, we propose a compromise where observational data, with the help of process expertise, is used to establish the set of factors that will be further tested for causality. We demonstrate a practical application of our proposal through a case study in additive manufacturing. [ABSTRACT FROM AUTHOR]

We present an overview of predictive maintenance (PdM) in industrial operations, highlighting its evolution, benefits, challenges, and potential economic impact. It is assessed that there are considerable benefits associated with PdM but also its practical implementation can prove difficult due to the uncertain benefits of its application, the need for advanced IT infrastructure as well as expert personnel, and, perhaps most importantly, a lack of failure-related data for the PdM model training. We conclude with three PdM case studies to elaborate on some of the issues discussed throughout the paper. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been a commonly used unsupervised learning method with broad applications in both descriptive and inferential analytics. It is widely used for representation learning to extract key features from a dataset and visualize them in a lower dimensional space. With more applications of neural network-based methods, autoencoders (AEs) have gained popularity for dimensionality reduction tasks. In this paper, we explore the intriguing relationship between PCA and AEs and demonstrate, through some examples, how these two approaches yield similar results in the case of the so-called linear AEs (LAEs). This study provides insights into the evolving landscape of unsupervised learning and highlights the relevance of both PCA and AEs in modern data analysis. [ABSTRACT FROM AUTHOR]

Sensor implementation together with data processing in manufacturing provides continuous measurements enabling process monitoring, optimization, and automation. Additive manufacturing (AM) technologies are shifting from one-off and prototyping production to batch, mass, and continuous production. Current AM technologies are mostly focused on small production devices, without monitoring systems. This study aims to bring AM closer to automation by proposing an online monitor system for bottom-up photopolymerization AM (VPP) systems. The sensor-generated data is used to capture the detachment error of built parts from the build platform that otherwise cannot be observed physically by the machine operator. The detachment itself will not stop the build job, which results in lost material and operating time. The online monitoring procedure consists of two phases, an offline and an online one, respectively. The offline phase is used for training a prediction model to be used in connection with a control chart for online monitoring. The monitoring control chart is advantageous as only the detachment predictions need to be recorded. The research carried out in this article brings novelty in both the vat photopolymerization set-up, as well as in an online monitoring procedure that can be easily extended to similar technologies. [ABSTRACT FROM AUTHOR]

The current work addresses an industrial problem related to injection moulding manufacturing with focus on mould wear-out prediction. Real data sets are provided by an industrial partner that uses a multitude of moulds with different shapes and sizes in its production. An analysis of the data is presented and begins with clustering the moulds based on their characteristics and pre-chosen running settings. Using the results of the clustering, the mould wear-out is modelled using Kaplan-Meier survival curves. Furthermore, a random survival forest model is fitted for comparison and model performance is assessed. The main novelty of the case study is the implementation of mould wear-out prediction in real-time with the outcomes presented in terms of conditional survival curves including a proposed early warning system. For visualization and further industrial implementation, an R Shiny dashboard is developed and presented. [ABSTRACT FROM AUTHOR]

Manufacturing has been rejuvenated by automation and digitalization. This has brought forth the new industrial era also called Industry 4.0. During the last few years, we have collaborated with companies from various industries that have all been going through this transformation. Through these collaborations, we have collected numerous examples of (sometimes troublesome) experiences with Big Data applications of production analytics. These experiences reflect the current state of production data and the challenges it poses. Our goal in this paper is to share those experiences and lessons learned in dealing with practical issues from data acquisition to data management and finally to data analytics. [ABSTRACT FROM AUTHOR]

Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical process control (SPC) and design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these challenges are instrumental in research and education. This article focuses on the revised Tennessee Eastman process simulator providing guidelines for its use as a testbed for SPC and DoE methods. We provide flowcharts that can support new users to get started in the Simulink/Matlab framework, and illustrate how to run stochastic simulations for SPC and DoE applications using the Tennessee Eastman process. [ABSTRACT FROM AUTHOR]

The analysis of split plot experiments can be challenging due to a complicated error structure resulting from restrictions on complete randomization. Similarly, standard visualization methods do not provide the insight practitioners desire to understand the data, think of explanations, generate hypotheses, build models, or decide on next steps. This article demonstrates the effective use of trellis plots in the preliminary data analysis for split plot experiments to address this problem. Trellis displays help to visualize multivariate data by allowing for conditioning in a general way. They can also be used after the statistical analysis for verification, clarification, and communication. [ABSTRACT FROM PUBLISHER]

Most of today's complex systems and processes involve several stages through which input or the raw material has to go before the final product is obtained. Also in many cases factors at different stages interact. Therefore, a holistic approach for experimentation that considers all stages at the same time will be more efficient. However, there have been only a few attempts in the literature to provide an adequate and easy-to-use approach for this problem. In this paper, we present a novel methodology for constructing two-level split-plot and multistage experiments. The methodology is based on the Kronecker product representation of orthogonal designs and can be used for any number of stages, for various numbers of subplots and for different number of subplots for each stage. The procedure is demonstrated on both regular and nonregular designs and provides the maximum number of factors that can be accommodated in each stage. Furthermore, split-plot designs for multistage experiments with good projective properties are also provided. [ABSTRACT FROM AUTHOR]

The article offers the author's insights on the statistical analysis of categorical data. The author discusses the usage of kappa statistic in agreement studies based on the comprehensive review of Professor Jeoren de Mast and colleagues. The author mentions that de Mast also presents the confusing behavior of using kappa statistic in fictitious datasets.

In many industrial applications, autocorrelated data are becoming increasingly common due to, for example, on-line data collection systems with high-frequency sampling. Therefore, the basic assumption of independent observations for process capability analysis is not valid. The purpose of this article is to compare decision methods using the process capability index Cpk when data are autocorrelated. This is done through a case study followed by a simulation study. In the simulation study the actual significance level and power of the decision methods are investigated. The outcome of the article is that two methods appeared to be better than the others. [ABSTRACT FROM AUTHOR]

One of the main features that distinguish split-plot experiments from other experiments is that they involve two types of experimental errors: the whole-plot (WP) error and the subplot (SP) error. Taking this into consideration is very important when computing measures of adequacy of fit for split-plot models. In this article, we propose the computation of two R[sup2], R[sup2]-adjusted, prediction error sums of squares (PRESS), and R[sup2]-prediction statistics to measure the adequacy of fit for the WP and the SP submodels in a split- plot design. This is complemented with the graphical analysis of the two types of errors to check for any violation of the underlying assumptions and the adequacy of fit of split-plot models. Using examples, we show how computing two measures of model adequacy of fit for each split-plot design model is appropriate and useful as they reveal whether the correct WP and SP effects have been included in the model and describe the predictive performance of each group of effects. [ABSTRACT FROM AUTHOR]

The article considers the use of a model fitting criterion in analyzing parsimonious time-series data, as well as in the decision-making process. The authors selected specific models based on the properties of the sample autocorrelation function (ACF) and the sample partial autocorrelation function (PACF). They also assesses the impulse response function for each model by reviewing time-series theory. Analyses of Internet server users are provided to expound some of the issues related to selecting an adequate model.

The article provides a detailed discussion about how to apply Box-Cox transformations to sales data. It discusses the forecasting model proposed by C. Chatfield and D. L. Prothero and the alternative model proposed by G. E. P. Box and G. M. Jenkins. It then evaluates the two models and interpret them to be able to understand why Chatfield and Prothero felt the autoregressive integrated moving average (ARIMA) approach did not provide sensible forecasts. It also discusses an unusual seasonal time series model proposed by G. T. Wilson.

The article examines how seasonal time series can be modeled and used for forecasting. The authors utilized the international airline data and modeled it with a seasonal autoregressive integrated moving average (ARIMA) time series model by Box and Jenkins to show how seasonal ARIMA models can be used to model cyclic data and how the model can be used for short-term forecasting. The most vital structural issue to consider about seasonal time series is that if the season is s periods long. Another consideration is that observations s time intervals apart are alike.

The article demonstrates how to characterize the behavior of a chemical manufacturing process using a time series model to control and adjust the manufacturing process. The authors discuss a different interpretation of the word control, and consider the situation where the process is adjusted whenever data coming from the process indicates that the process is too far from a given target value. They describe the idea behind time series control of a process and proceed more intuitively and use the chemical process as an illustrative example.

In many experimental situations, it may not be feasible or even possible to run experiments in a completely randomized fashion as usually recommended. Under these circumstances, split-plot experiments in which certain factors are changed less frequently than the others are often used. Most of the literature on split-plot designs is based on 2-level factorials. For those designs, the number of subplots is a power of 2. There may however be some situations where for cost purposes or physical constraints, we may need to have unusual number of subplots such as 3, 5, 6, etc. In this article, we explore this issue and provide some examples based on the Plackett and Burman designs. Also algorithmically constructed D-optimal split-plot designs are compared to those based on Plackett and Burman designs. [ABSTRACT FROM AUTHOR]

This article presents an analysis of time series data from industrial processes. The time series considered is consists of 197 consecutive concentration observations from a full-scale chemical process. Computation of the sample autocorrelation function is presented. It is concluded that the nonstationary model with the interpretation that the exponentially moving average represents is the best estimate of the current level of the process.

The article uses an example from economics to show how regression analysis can be misleading if the assumption of independence of the errors is violated. It says that the issue of autocorrelation is of importance when one uses regression to estimate input-output relationships from process data sampled over time that likely may be autocorrelated.

The article analyzes the effect of a drastic change to the regular behavior of a process that interrupts the quality engineers from analyzing the input-output relationships. This change occurs when operators make significant changes while operating a process. This phenomenon is illustrated by observing changes in the temperature profile of a ceramic furnace. These changing patterns are explained by graphical patterns in scatter plots that are necessary to understand the process behavior.

The article demonstrates with a simulated example the problem of spurious regression for autocorrelated data. This is one of the problems quality engineers may encounter when investigating possible input-output relationships. Suppose the process is an industrial furnace and one wants to see if there is a possible relationship between an input variable, such as fuel rate at a given time and the temperature in the furnace.

The investigation of aliases or biases is important for the interpretation of the results from factorial experiments. For two-level fractional factorials this can be facilitated through their group structure. For more general arrays the alias matrix can be used. This tool is traditionally based on the assumption that the error structure is that associated with ordinary least squares. For situations where that is not the case, we provide in this article a generalization of the alias matrix applicable under the generalized least squares assumptions. We also show that for the special case of split plot error structure, the generalized alias matrix simplifies to the ordinary alias matrix. [ABSTRACT FROM AUTHOR]

Split-plot experiments are commonly used in industry for product and process improvement. Recent articles on designing split-plot experiments concentrate on minimum aberration as the design criterion. Minimum aberration has been criticized as a design criterion for completely randomized fractional factorial design and alternative criteria, such as the maximum number of clear two-factor interactions, are suggested (Wu and Hamada (2000)). The need for alternatives to minimum aberration is even more acute for split-plot designs. In a standard split-plot design, there are several types of two-factor interactions, not all of them equally interesting. However, minimum aberration is not designed to distinguish among the different types of two-factor interactions. It should be noted that this criticism is valid not only for the minimum aberration but also for any other design criteria originally proposed for completely randomized designs. Consequently, we provide a modified version of the maximum number of clear two-factor interactions design criterion to be used for split-plot designs. [ABSTRACT FROM AUTHOR]

The article demonstrates a few techniques in monitoring industrial processes. Graphical and statistical tools that can be used when dealing with multivariate situations are presented. An example of monitoring the temperature of an industrial process at five different locations is also explained. Principal component analysis can be used when trying to uncover the structure of a large multidimensional data set.

When some factors are hard to change and others are relatively easier, split-plot experiments are often an economic alternative to fully randomized designs. Split-plot experiments, with their structure of subplot arrays imbedded within whole-plot arrays, have a tendency to become large, particularly in screening situations when many factors are considered. To alleviate this problem, we explore, for the case of two-level designs, various ways to use orthogonal arrays of the Plackett-Burman type to reduce the number of individual tests. General construction principles are outlined, and the resulting alias structure is derived and discussed. [ABSTRACT FROM AUTHOR]

Characterizes the dynamic behavior of time series models. Dynamics of a stationary processes; Basic physics of a pendulum; Information on the impulse response function; Discussion on the superposition principle.

*Edited by Søren Bisgaard [ABSTRACT FROM AUTHOR]

Describes the use of variogram in statistical quality control process. Temperature readings from a ceramic furnace; Periodicities in stationary process.

Computers are increasingly employed to monitor the performance of complex systems. An important issue is how to evaluate the performance of such monitors. In this article we introduce a three-dimensional representation that we call a “waterfall chart” of the probability of an alarm as a function of time and the condition of the system. It combines and shows the conceptual relationship between the cumulative distribution function of the run length and the power function. The value of this tool is illustrated with an application to Page's one-sided Cusum algorithm. However, it can be applied in general for any monitoring system. [ABSTRACT FROM AUTHOR]

Discusses the factors that influence discovery and development processes in engineering and industries. Ways in which scientific investigation differs in industry and engineering; Means of determining active subspaces by Bayesian analysis; Notions about scientific investigations.

*Edited by George Box and Søren Bisgaard [ABSTRACT FROM AUTHOR]

Industrial experiments are frequently performed sequentially using two-level fractional factorial designs. In this context, a common strategy for the design of follow-up experiments is to switch the signs in one column. It is well known that this strategy, when applied to two-level fractional factorial resolution III designs, will clear the main effect, for which the switch was performed, from any confounding with any other two-factor interactions and will also clear all the two-factor interactions between that factor and the other main effects from any confounding with other two-factor interactions. In this article, we extend this result and show that this strategy applies to any orthogonal two-level resolution III design and therefore specifically to any two-level Plackett- Burman design . [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the guest editor discuss contributions of various researchers and practitioners to the Second Stu Hunter Conference held in Tempe, Arizona.