FINAL FINAL Review 2017

th

Proceedings of the 12 Manufacturing Science and Engineering Conference MSEC2017 June 4-8, 2017, Los Angeles, California, USA

MSEC2017-3104 PROCESS AND OPERATIONS CONTROL IN MODERN MANUFACTURING Dragan Djurdjanovic University of Texas Austin, Texas, USA Farbod Akhavan Niaki Clemson University Greenville, South Carolina, USA

Laine Mears Clemson University Greenville, South Carolina, USA Asad Ul Haq University of Texas Austin, Texas, USA

ABSTRACT Dramatic advancements and adoption of computing capabilities, communication technologies, and advanced, pervasive sensing have impacted every aspect of modern manufacturing. Furthermore, the very character of manufacturing is changing fast, with new, complex processes and new products appearing in both the industries and academe. As for traditional manufacturing processes, they are also undergoing transformations in the sense that they face everincreasing requirements in terms of quality, reliability and productivity. Finally, across all manufacturing we see the need to understand and control interactions between various stages of any given process, as well as interactions between multiple products produced in a manufacturing system. All these factors have motivated tremendous advancements in methodologies and applications of control theory in all aspects of manufacturing: at process and equipment level, manufacturing systems level and operations level. Motivated by these factors, the purpose of this paper is to give a high-level overview of latest progress in process and operations control in modern manufacturing. Such a review of relevant work at various scales of manufacturing is aimed not only to offer interested readers information about state-of-the art in control methods and applications in manufacturing, but also to give researchers and practitioners a vision about where the direction of future research may be, especially in light of opportunities that lay as one concurrently looks at the process, system and operation levels of manufacturing. INTRODUCTION Quality and productivity are two coupled behaviors of manufacturing at the process, system, and operations levels. Control of these parameters has been addressed in a number of

Lin Li University of Illinois, Chicago Chicago, Illinois, USA

forms for a large range of manufacturing applications. In this paper, we explore currently evolving control approaches at each of these organizational levels, computational enablers for the application of these methods, and threads of commonality among these approaches across time and scale of the manufacturing enterprise. This scope encompasses first with quality control in single manufacturing operations from variability mitigation and time-dependent change (nonstationary) perspectives, and productivity from efficiency to production planning viewpoints. The importance of uncertainty in estimation and decision-making is emphasized, and methods and architectures for implementing such methods are elucidated, with recommendations for upcoming areas of research focus. Second, state-of-the-art in control strategies of multi-stage manufacturing processes with emphasis on recent control advances on sequential operations of multiple products passing through a machine and multiple operations on a single product are discussed. Due to lack of in-situ quality measurements in some manufacturing industries, e.g. semiconductor industry, run-2-run control framework gained significant momentum for single product or multiple product manufacturing operation which was thoroughly addressed here as well as control approaches considering error propagation in manufacturing of a single product in multi-stage operations. In addition to product quality in process and system levels, productivity and methods of optimal maintenance in operational and decision-making levels are discussed as the third scope of this work. Maintenance and production management are two critical factors determining productivity are discussed here. Decision making strategies for optimal maintenance interval, failure mode identification and cost reduction is reviewed along with production planning and scheduling for improving decision making strategies. This was

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Copyright © 2017 by ASME

later expanded to integrated approaches of maintenance and production management for improved decision making. To that end, the article is organized as follows: in the first section advances in process control strategies for servomotor drive control and tool path planning and recent methods of feedrate scheduling and state estimation is discussed. In the second section, advances in system control methods in part-topart interaction of single operation or multi operational modes. And In the last section, decision making approaches for maintenance and operation management will be discussed. ADVANCES IN PROCESS CONTROL AND ESTIMATION STRATEGIES In this first section, we present an overview and some of the most recent advances in applied methods of control for manufacturing processes, which rely on material or process models or observers to predict process performance for the purpose of control. We use machining as a primary exemplar, but encompassing other innovative methods as well in this domain. We examine the evolution of adaptive control for processes, and how more computationally-intensive methods have evolved in recent years due to the improving capability of equipment controllers. The first section describes methods that have emerged for controlling output quality of a process through disturbance rejection and variability minimization of output characteristics, and explores the concepts of prediction, control, time delay effect, and the time scale of control which gives rise to estimation approaches from milliseconds to years in the same framework. The second section explores an aligned result of the control approach: state estimation of manufacturing process operating parameters/estimators. Rapid advancement in sensory technology, multi-sensor integration for control and monitoring of machine tools as well as computational processing power, enables real-time estimation and implementation of advanced data-mining strategies for machining health assessment which are discussed. The third section reviews approaches for optimization of manufacturing productivity through path planning and modeling of hard constraints followed by the fourth section elucidating the role played by uncertainty/variation in process parameter estimation and in the use of estimators for adaptive control, and for classification of process behaviors. 1.1. Control Strategies for Process Quality Part quality and dimensional integrity are two key factors in defining the productivity of machine tools specifically in high-speed regimes. Several factors in cutting processes such as tool wear, cutting parameters, external disturbances and uncertainties of the motion system can have detrimental effects on the part quality and dependently the production rate. The focus of this section is given to reviewing state-of-the-art in strategies for control of servo-systems, estimating contour errors, tool path profile planning and feedrate scheduling for cycle time optimization. While in the following sections, incremental advances in tool condition monitoring since the

previous state-of-the-art paper [1] on this subject will be reviewed, as well as approaches for productivity optimization. The approach to adapting process inputs to predicted responses of the plant, or hybrid prediction/feedback control, in contrast with traditional automatic feedback control on sensed signals, is broadly termed model-based process control. Through such an approach, manufacturing process consistency and productivity can be improved through anticipation and advance correction of deviation from expected behavior, rather than reacting to departure from ideal. Here we briefly review some architectures of these approaches, and detail the most recent evolutions of manufacturing process control using these techniques. The primary exemplar for these approaches will be made using the machining process, though other significant approaches to control are described. 1.1.1. Adaptive Control Adaptive control techniques have been developed over the past 50 years in response to more improved understanding of physical system behavior and the need to continually improve process performance. In machining, this term has been applied primarily to feedrate adjustment based on measured conditions such as chatter or size (see Figure 1). Disturbance CNC System

constraints strategy performance metrics

Machine/ Process

Sensors

Adap9ve Controller

Figure 1. Adaptive control. Operational strategy(ies) and process performance assessment is used directly to modify the machining path plan in real time (after [2]).

Koren provided the first extensive review of adaptive machining control, touting its evolution as a response to subjective judgment by part programmers [2]. In this paper, he covered basic classes of adaptive control: adaptive control with optimization (ACO), adaptive control with constraints (ACC), and geometric adaptive control (GAC), as well as self-tuning control approaches. A key drawback at the time was lack of reliable sensing for germane phenomena such as tool wear or friction, or prohibitive cost for force and torque sensing. In this case, model-based approaches can fill the gap. Landers, Ulsoy, and Ma also surveyed model-based machining control approaches using force information [3]. They contrast four control approaches in terms of stability and robustness: linearization, log transform, nonlinear, and robust. The conclusion is that such model-based approaches are insensitive to unmodeled dynamics, and that the choice of control is dictated by economics. Landers and Ulsoy [4] had carried out

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early implementation of model-based machining control for force, as Elbestawi had for accuracy in turning [5]. Intelligent chatter mitigation using this approach has been explored by Giorgio Bort et al., and also includes adaptive learning behaviors [6]. Observer-based adaptive control has been applied by Jamaludin et al. for disturbance force rejection in machining with sustainability objective [7]; this class of adaptive control is detailed in the following subsection. Liu et al. used constraint-based adaption for geometric optimization of corner cutting in molds [8]; this has driven new research in more accurate chip thickness models. 1.1.2. Observer-Based Control External disturbances in addition to modeling error and parameter variations contribute the most to the inaccuracy of the motion system (i.e. servomotors) of the machine tools. Huang et al. used Disturbance Observer (DOB) proposed by Ohnishi [9] to estimate and compensate the uncertainties in velocity and current loop of a CNC servomotor, and further included an adaptive tuning of motor inertia and viscous damping coefficient [10]. By using this method over conventional PI controller, they could improve the accuracy of circular trajectory and reduced roundness error by 84% [10]. In another work by Yeh and Su, DOB was used in the speed control loop for compensating speed fluctuations in constant velocity motion and a static friction model was obtained (ignoring the dynamic effects of friction on motion accuracy) based on position-dependent perturbation [11]. DOB as an effective control scheme was also used for dual-drive systems in gantry NC machines to improve the synchronization error of double drive motors [12]. In [13], Adaptive Disturbance Compensation (ADC) control with LuGre dynamic friction model has been tested against PI controller for speed control of servo-systems in circular motion. Having the essential features of adaptive controllers, the ADC method is capable of mitigating uncertainties and disturbance by adjusting the observer gains. Sliding Mode Control (SMC) is another control approach utilized for compensating disturbances and parameter variations in various articles [14-15-16]. Adaptive SMC was successfully applied in [17] for mitigating the harmonic torque ripples in servo-drives. In another work by Li et al., they used dual SMC for contouring error estimation of 5-axis CNC machine [18]. Integral SMC was also implemented for the same purpose in [19] and was shown successful in contouring error mitigation. 1.1.3. Model-Predictive Control As a class of model-based approach, model-predictive process control emerged in the second half of the 20th century in continuous process industries such as pharmaceuticals and petroleum in order to minimize quality variability as cost was reduced. The approach is widely used today in the chemical industry, and has begun finding application in other manufacturing sectors. The basic approach is shown in Figure 2.

predic!on horizon

reference predic!on

output

control horizon

input

simula!on

t0

!me

Figure 2. Model Predictive Control. The MPC controller uses a model of the process to predict behavior, then optimizes control action for the next time step. Penalty is placed on large changes to the input(s).

Model Predictive Control (MPC) for contour error mitigation enables the users to estimate the error in finite time interval, i.e. the prediction horizon, and tune the control input accordingly. Yang et al. in [20] used a MPC scheme for contour error compensation in a 2-axis drive system and compared it to the conventional error-mitigation strategy of Zhang et al. in [21] and uncompensated method. Significant improvement was achieved specifically in the sharp corners [20]. A linear time varying MPC was utilized in [22] that enables the trade-off between accuracy and time, however the controller was only capable of operating up to 4ms (far from real-time industrial controller speed). In [23], the authors discussed the computational cost of using MPC in high rate servo-system and introduced a trajectory horizon (in addition to the prediction horizon) into the MPC scheme to lower the computational burden and reached satisfactory performance (with 100 µs sampling period) compared to traditional MPC. A MPC scheme with adaptive feed rate control for tracking diamond and freeform contours in high feedrate motions were also adapted in [24]. In [25], an algorithm based on polynomial root finding of tool path for exact contour error measurement in CrossCoupled Controllers (CCC); where the control input for one axis is influenced by the error in other axis, was introduced as a replacement strategy for approximate methods contour error measurements (i.e. linear, circular or second order estimates). Application of MPC to machining has been carried out at Aachen for decoupling the machining process control from machining system control [26]. In this work, they are also predicting unknown states through Kalman estimation, and accounting for computational time delays as laid out in the following section. 1.1.4. Time-Delay Control A final difficulty in online estimation techniques is the balance between a more accurate yet computationally expensive model (which introduces time delay), and the departure from an ideal control state when a simpler or linearized model is used. The Smith predictor architecture was developed to hybridize predictive and real feedback control in

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order to deal with such time-delay systems. The basic architecture is given as Figure 3. Disturbance ε e

reference

Controller

u

Physical Plant G

y

Sensor H yd

Plant Model Gp

yp

Time Delay e-t/τ

ypd

dy

Figure 3. Smith predictor. To address time delay, such as introduced by model calculations, an additional inner loop is included to predict the behavior in between actual feedback signals to the controller using a generalized system model.

The Smith predictor has been implemented in vision-based positioning processes, where the servo is driven to bring a part targeted into a camera frame of reference, necessitating computationally-expensive image processing [27]. Variables or states on which to control by model vary by process, but nominally include states which are not directly observable but which may carry a large influence on the process output. For machining, process variables such as true position, force, friction, and wear are employed, as well as output quality states such as dimensional consistency, surface condition, and residual stress profile, each somewhat difficult to assess in real time at the machining center. Such control approaches have been implemented recently directly to manufacturing processes, particularly machining. Researchers at Nanjing have proposed new approaches to 5axis machine tool error compensation [28]. Shin et al. at NIST are using virtual machining models to generate data needed for open-architecture platforms and protocols such as the emerging MTConnect [29]. This brings the point that despite several control approaches being proposed over the years and proving to be effective in disturbance/uncertainty mitigation and improving contour accuracy, one major drawback is the fact that current industrial CNC machines do not allow for overriding their control algorithms in order to keep the stability in all cutting conditions [20]. The MTConnect open protocol that serves as a universal language of obtaining information from the machine is only a read-only protocol in the market. Therefore, precompensation of contour error and making modification beforehand in servo-axis motion is an alternative solution to work-around the accessibility to the machine control unit. In [30], a pre-compensating contour error strategy was introduced taking into account the residual vibration of machine structure and axis limited bandwidth in high speed motion of a 2-axis machine which was later implemented on a 5-axis CNC machine for high acceleration/jerk cutting [21]. A great deal of research work led by Yusuf Altintas at the University of British

Columbia has focused on implementing pre-compensation contour error for improving dimensional tolerances in machine tools [21-30-31-32]. 1.1.5. Long-Time Process Control Machine health monitoring and prognosis can be viewed as a long-time feedback loop where model information is generated and evaluated. To that end, Wang et al. are using probabilistic model-based approach for machinery condition evaluation [33]. Zhu, Devor and Kapoor were some of the first to use a model-based approach to fault diagnosis in ball-end milling, a difficult model to implement due to complexity [34]. Part and fixture flexibility and friction are simultaneously evaluated and fed to a control scheme in [35]. These approaches have been applied in other areas of manufacturing as well. As machining processes evolve, new implementations require new control measures. Model-based stability prediction and control of a machining robot is investigated by [36]. Cen et al. also investigate wireless force sensing and its use in control for robotic machining [37]. Lu et al. have incorporated modelbased observers for tension control in steel strip manufacturing [38]. Itoh applies model-based control of rotational speed in a form rolling machine to eliminate transient vibration [39]. Rabani et al. have executed an Iterative Learning Control (ILC) for estimating and prescribing the depth of a waterjet cutting process [40]. Surface roughness is also predicted and controlled in this type of process by [41]. Davis et al. described observerbased adaptive control of friction stir welding [42]. Even human behavior is included in such approaches. Huang et al. describe an interval control approach for machine-assisted feedback to a manual welding operation [43]. An aligned benefit to the predictive control approach in manufacturing is the ability to estimate the process and output states in real time. This information can be incorporated to machine or process health assessment programs, and is also of key importance to creating the process “digital shadow” or “digital twin” for incorporation to digital manufacturing approaches on the shop floor. Researchers at Nagoya are applying this identification approach to identify and share cutting process parameters for flexible parts [44]. This builds on similar work by Karpat et al., who used machining output to estimate constitutive material parameters [45]. Using this perpiece knowledge estimate, system-level control can be improved by passing not only the physical part from process to process, but concurrently estimates about its material, condition, and behavior in the process in order to adapt and optimize in real time. Therefore, state estimation is an essential benefit of predictive and observer-based control approaches. 1.2. State Estimation for Manufacturing Processes Monitoring, tracking and forecasting state of health for critical elements in manufacturing processes have gained significant momentum in the past decade [46-47-48]. Advancement in low cost sensing technologies along with data analytics enables the users to better assess the performance of a

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process or a machine. In general, state of health assessment in manufacturing can be attributed to state of health monitoring for the cutting tool, state of health monitoring for the manufactured part and state of health of the machine. Cutting tools play a significant role on the quality of the product; therefore significant attention was given to studying the Remaining Useful Life (RUL) of the tool or monitoring the state of tool wear using Artificial Intelligence (AI) tools. Neural Networks (NNs) as one of the most dominant methods for classification or tracking of tool wear evolution has been widely studied by researchers. Rao et al. used a NN with a back-propagation training algorithm for state of tool wear, surface roughness and workpiece vibration evaluation and could reach to less than 3% error in wear estimation [49]. In [50], da Silva et al. implemented a probabilistic NN in milling of stainless steel and showed using only one sensor cannot provide enough accuracy; therefore the sensory data from acoustic emission and spindle power sensors were fused for wear state estimation. Despite the satisfactory accuracy rate of 91%, the effects of cutting conditions were not considered in [50] and that limits the robustness of the proposed strategy. Wang and Cui utilized a new method, using associative NN which only requires training set in normal state of wear condition, this way the need for large training set can be eliminated and training becomes easier in actual production line [51]. ANNs were also used with on-machine spindle power sensor by Akhavan Niaki et al. and Corne et al. in milling and drilling of nickel-based difficult to machine superalloys [5253]. The advantage of using spindle power sensor lies in the fact that the data can be read from the MTConnect protocol as a universal industry standard for retrieving information from the micro-controllers of modern CNC machines [29-54-55]. Support Vector Machine (SVM) and Support Vector Regression (SVR) are another class of data-driven classifiers/estimators which has been significantly used for tool wear monitoring. Brezak et al. used a classification-estimation strategy based on the analytic fuzzy classifier and SVM and could reach satisfactory results [56]. However a critical study is required to assess the computational cost of their method with simpler and faster estimation algorithms due to linear behavior of wear evolution they observed. Garcia-Nieto et al. conducted a comprehensive study using 3 different sensors (power, vibration, and acoustic emission) installed in 5 locations in the machine for tool wear estimation with SVR with an optimized nonlinear kernel using Particle Swarm Optimizer (PSO) and showed that time of cut and spindle power are the most sensitive factors in tool wear [57]. To increase the training speed of SVM method, Wang et al. proposed v-SVM and compared the performance of their method in terms of training time and accuracy with classical SVMs [58]. They showed that NN rule based v-SVM can be up to 100 times faster in training than classical SVM without affecting the accuracy of estimation. On a different application, the feasibility of k-means methods for RUL prediction was also investigated in [59] by Mosallam et al. for battery and turbofan engine degradation as

well as wear monitoring in gear coroning operation by Kannatey-Asibu et al. [60]. 1.3. Model-Based Process Control for Productivity In addition to control of servo systems for achieving the best geometrical tolerances and reduce the contour error, optimization of machining time is the second critical factor defining the efficiency and productivity of the cutting process. Feedrate scheduling is a technique introduced mainly for optimizing the cutting time in complex shape free-form profiles. In [61], the two main strategies; material removal ratebased and force-based were compared in ball-end milling of a complex free form surface and it was shown that force-based feedrate scheduling provides more conservative solution with less chance of tool damage compared to material removal ratebased approach. In [62], an enhanced force-based feedrate scheduling based on the instantaneous chip load-force model and cutter geometry was introduced in cutting free-form impeller in a 5-axis machine which reduced the cycle time by 44% compared to constant feedrate. In [63], feedrate scheduling for real-time deflection control of thin-wall blade based on nonlinear root-finding algorithm was implemented into a 5-axis CNC with Open Architecture Control (OAC), while the force-feed-deflection relation was extracted from a finite element model of cutting tool and blade. In [64], an offline feedrate scheduling using the exact geometry of material removed per feed path in plunge milling was introduced and shown effective in increasing productivity. Velocity bounds, acceleration and jerk of the motor-drives are important factors in determining the optimal feedrate path. In [65], an offline feedrate scheduling based on finding the critical points with high curvature value of tool path under chord tolerance, acceleration and jerk confinement was introduced. The tool path was first approximated with several Non-Uniform Rational Basis Spline (NURBS) curve and feedrate was adjusted based on critical points and acceleration/jerk constraints to find the optimal plan. In another work [66], an analytical relation between drive axis kinematic and NURBS tool path parameters was developed with an iterative algorithm for reducing velocity, acceleration and jerk in critical points. Considering the fact that NURBS path has limited use in industrial machines, Beudaert et al. proposed a feedrate scheduling algorithm that considers both geometrical treatment of tool path and feedrate interpolation with axis jerk limitation for both linear and NURBS paths [67]. In [68], Ridwan and Xu demonstrated a 3-level strategy for intelligent machining taking into account the critical cycle time and quality metrics. On the 1st level, and optimization strategy is needed based on machining constraints such as vibration, spindle power, motor drive limitations such as acceleration or jerk, to optimize the tool path in order to achieve the best cycle time. The 2nd level is an adaptable CNC controller capable of executing optimal commands based on machine condition, tool-workpiece combination and desired quality and the 3rd level is knowledgebased platform, i.e. MTConnect, for recording the process performance of machines in the manufacturing plant.

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1.4. Role of Uncertainty Uncertainty in the manufacturing process due to several factors such as material or tooling variation is one of the key factors in determining the robustness of state estimation strategy. The health assessment method should be as resistive as possible to process uncertainty. Probabilistic-based methods of classification or tracking such as Bayesian network [69-70], Hidden Markov Models (HMM) [71-72-73], Markov Random Fields [74-75], and Kalman and Particle Filters [76-77-78] are some of the probabilistic-based algorithms based on Bayesian learning from the data capable of providing output with corresponding uncertainty. Bayesian networks are directed acyclic graphs used for identifying the correlation between causes and effects; therefore suitable for root cause diagnostics of defects or variations in manufacturing processes. Dey and Stori used a Bayesian Network for root cause analysis of sequential-machining (i.e. face milling and drilling) where workpiece hardness, dimensional variation and tool wear were considered as potential causes of variation [79]. In another effort by Correa et al., Bayesian Network and tree augmented network (TAN) were utilized in milling process for surface roughness classification with 81% accuracy [80]; however material and geometry types were not included in their model. Naïve Bayes as simple form of Bayesian classifier has also been used in [69-81] for spindle bearing health monitoring and detecting state of tool wear in milling operation. To include the temporal behavior of a system where sequence of information in the form of measuring variable is available, HMMs were designed. HMMs were first used in speech recognition society [82] and later implemented for classifying state of cutting tool in manufacturing processes. Geramifard et al. suggested a segmented HMM for continuous estimation of tool wear and showed that proposed approach outperforms multilayer perceptron NN and Elman NN [83]. Their method was further improved with multimodal HMM to achieve faster computational time for training several models [84]. To avoid the high computational cost in training large number of HMMs for continuous wear estimation, Yu et al. proposed a weighted HMM in which the wear evolution process was discretized by l wear stages and tool wear rate at each stage was considered as the hidden state [72]. Bhat et al. in [85] took a different approach for wear classification and used a CCD camera and texture features of the machined surface for tool wear state classification. While the 95% accuracy in estimation was satisfactory, their method is limited to only dry machining and existence of coolant puts major limitation in using vision-based method for health monitoring [48]. HMMs are proven to be effective for both wear state classification or continuous estimation. However, the major drawback is the computational cost needed for training multiple models. The alternative is to use stochastic observers based on discrete dynamic state space model of the system. Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) are some examples of stochastic filters used for tracking state of a system in different

domain like navigation and target tracking [86-87] as well as health monitoring [77-78]. Akhavan Niaki et al. used KF and EKF for tool wear tracking in both and milling and turning operations [76-77] and showed the significance of stochasticbased tracking algorithms over deterministic methods. The performance of PF was also compared with KF by the same authors for joint state-parameter estimation using multimodal Gaussian prior belief [77]. The application of PF was further investigated by Wang and Gao with using continuous resampling strategy for joint state and time-varying parameter estimation in predicting tool wear growth [78] and engine performance tracking [88]. In another application, Lim and Mba used a multiple model of state of a gearbox bearing to make an inference on most probable degradation process in ball bearings. In [89], Zhang et al. used least square SVM for tool wear prediction in milling with ball-end cutter. To improve the estimated wear and update the parameters of SVM, the Kalman filter was utilized when actual tool wear measurement becomes available. Despite the high accuracy they reached, their proposed approach is only limited for intermittent cut where actual tool wear value can be measured. ADVANCES IN SYSTEM LEVEL CONTROL METHODS AND APPLICATIONS The research reviewed in the previous section concentrates on the control of a single operation as an independent manufacturing process. However, a wide array of industries rely on multistage manufacturing processes, as shown in Figure 4 (taken from [149]), which introduce interactions between sequentially performed operations as well as across the execution of particular operations on sequentially processed products. Such processes are found for instance in automotive and aerospace parts manufacturing and assembly, traditional machining and turning, semiconductor manufacturing, etc. The importance of these interactions in the design of process control is reflected in the volume of literature in this field. The works discussed in this section shall be classified into two main categories; control design considering interactions at a particular operation from product to product, and control design considering interactions from operation to operation in the processing of a single product.

Figure 4. Multistage manufacturing process

2.1. Process Control Based on Interactions across Products Statistical Process Control (SPC) had led to great gains in industries employing Multistage Manufacturing Systems (MMSs) during the 1960’s and 1970’s, and remains a critical

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part of such processes even today. However, SPC does not prescribe corrective measures when quality drifts are observed [90], which has led to investment of significant resources to study and implement run-to-run (R2R) control since the 1990’s. The basic concept of R2R control is displayed in Figure 5 (taken from [134]) During the early 1990’s Sachs and his colleagues proposed an R2R controller based on the concept of exponentially weighted moving average [91]. The key motivation for the development of such a control approach is the need for quality control in processes that do not allow for in situ measurements of product characteristics of interest.

Figure 5. Run-to-run control

Most early investigations, as well as some current research, was conducted under the assumption that there is only a single product being manufactured. The reality, however, is that most manufacturing environments must produce a variety of products during the same interval. This often introduces aspects of behavior that cannot be handled by methods developed under the assumption of a single product alone being manufactured, as was pointed out in [120]. In view of this, many recent works have attempted to include these aspects directly included into the control development. 2.1.1. Single Product Environment Much work in the development of R2R control techniques has been conducted under the assumption that only a single product being manufactured. While the applications of such R2R control techniques encompass a number of industries, including automotive manufacturing and assembly [92], batch crystallization [93], [94], nano-scale machining [95], [96] and traditional machining and turning [97], a major part of academic research in this field has been oriented towards the semiconductor manufacturing industry. Within this there are a number of processes that rely on R2R control, such as photolithography, overlay control, etching, deposition and chemical mechanical polishing [98]. In general, the R2R controller is envisaged as part of a larger Advanced Process Control (APC) system, along with the SPC and Fault Diagnostics (FD) components [99]. As early as 1996, Ning et al. [100] published a study comparing the various R2R control algorithms in semiconductor manufacturing processes, specifically the Exponentially Weighted Moving Average (EWMA) “Gradual Mode” (GM) controller, the time-based GM controller, the Knowledge-based Interactive R2R Controller (KIRC) and the Optimizing Adaptive Quality

Controller (OAQC). Their results showed that while there is insignificant nonlinearity the performance of these control algorithms is quite similar, but in more nonlinear regions of behavior only the OAQC approach is able to perform the required control. A few years later, Campbell et al. [90] performed another comparative study of R2R control algorithms for MMSs in which they discussed the standard and double EWMA controllers, as well as MPC and OAQC. In this study the authors focused on the potential benefits of MPC over the alternatives due to its flexibility, in terms of both the cost function optimized and the constraints it is inherently able to handle. The EWMA statistic for disturbance rejection was first introduced by Box and Jenkins [101] and extended to R2R control applications by Ingolfsson and Sachs in [102]. The EWMA controller remains the most popular R2R control strategy [98], possibly due to its relative simplicity. As such it remains an active field of study, with many variants on the original form being proposed over the years, to tweak and improve performance under different conditions [98]. For instance, Patel and Jenkins [103] propose an adaptive optimization scheme for the EWMA controllers and [104] proposes a Bayesian EWMA R2R controller using a Bayesian disturbance and classification tool. At the same time, there have been studies to determine the properties of these controllers, such as the study on closed-loop stability of systems with EWMA R2R controllers in the presence of metrology acquisition delays [105]. The same authors also presented a study on the stability properties of the double EWMA controller in [106]. Linear MPC has also gained much popularity in R2R control applications over the years. In [107], the authors provide an early comparison of EWMA and linear MPC R2R control performance. An example application to overlay control is presented in [108], while applications to batch crystallization processes are presented with both offline and moving horizon parameter estimation [93], [94]. Applications of MPC in MMS were recognized in Qin and Badgwell’s surveys on industrial applications of MPC [109]. Along similar lines, Jiao and Djurdjanovic offered a stochastic optimization based MPC-like approach, with a horizon that was shortened after each step, in [110]. Among other control methods there has been some consideration of the similarities and relationship between ILC and R2R control [91], [111]. Along these lines, [112] proposes an adaptive terminal ILC to deal with uncertainties in initial conditions and desired outputs based on a high-order stochastic internal model. Yet other control strategies explored under the framework of R2R control have included the General Harmonic Rule (GHR) controller [113]. A combination of output entropy minimization and mean value optimization is enabled by an adaptive controller informed by probability distributions in [114], and in [97] Bayesian inference and Bayesian least squares estimation are employed for R2R control in bar-turning operations. A commonly explored thread in the field of R2R control is the nature of the model used by the controller. For instance, in

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[92] the authors explore the use of state space models for dimensional control in automotive assembly operations. On the other hand, [95] looks at nano-scale manufacturing applications with a combination of a state space model with a Kalman filter, and piecewise linear models are considered in [115]. In [116], Chen et al. propose the use of both a deterministic linear model and a stochastic model based on the error probability distributions. In [117] the inclusion of a reliance index with the model used to inform control in semiconductor manufacturing is suggested. More advanced models such as NNs and Fuzzy cognitive maps have also been proposed in recent years [118], [119]. 2.1.2. Multiple Product Environment In Miller’s 1997 paper [120], the problem of R2R control was posed in a new light with the consideration of multiple products and processes, which reflects the true environment of most modern-day applications of R2R control. The multiproduct environment is shown in Figure 6 (taken from [130]). The R2R control methods for such environments have been classified into two main categories, ‘threaded’ and ‘nonthreaded’. Threaded control essentially attempts to model and control processes that have common ‘thread context’, which refers to the tool, product, layer, etc. A separate R2R control must then be implemented for each thread. However, some issues that arise from such an approach have been recognized in recent years, such as the difficulties that arise when faced with rare products which lead to the “Data Poverty” problem [121]. In addition to the data poverty problem, there is also a major challenge associated with the performance of threaded R2R controllers when a new thread comes into being. Due to these challenges, non-threaded methods have also become an important field of study, offering an alternative with the potential to alleviate many issues associated with threaded R2R control. The non-threaded methods attempt to combine the information from production runs with different context information to define the control actions for future products, i.e. models and control algorithms can be based on products with varied context information.

Figure 6. Mixed product manufacturing environment

2.1.2.1. Threaded R2R Control In Tan et al.’s [98] recent survey, threaded methods such as tool-based and product-based EWMA control, Output Disturbance Observer (ODOB) structure based R2R control and G&P EWMA control, which uses grouping of products based on k-means clustering along with a product-based EWMA controller. The authors then go on to look into non-threaded control methods from the literature, such as the Just-in-Time Adaptive Disturbance Estimation (JADE) algorithm with a deadbeat control, an Analysis of Variance (ANOVA) method that looks at product and tool states relative to the overall performance across all products and tools, Kalman filter based control and the use of non-threaded state-space models. Expansion of the threaded R2R control field has continued as there are many environments in which this can be employed with great success, which is also reflected in the fact that the threaded EWMA control remains the most popular of these approaches in industry. In [122], Zheng et al. established that tool-based EWMA R2R control is unstable if the plant is nonstationary and modeling error varies from product to product. On the other hand, they found that product-based threaded EWMA R2R control is stable, but its performance does suffer compared to the single product control where the products are frequently produced. In the case of infrequent products, the authors propose a “more active controller”. The work in [123] proposes to overcome the issues identified by Zheng et al. by exploiting a feedforward controller to eliminate drift and make the disturbance stationary, thereby allowing stability to be established by a simple integral controller. Ai et al. [124] consider the use of a threaded double EWMA control approach in order to reduce the rework required. The authors introduce a drift compensatory approach and discuss the stability conditions, going on to evaluate behavior in the presence of step and ramp type faults. In [125], a cycled resetting of discount factors in a threaded EWMA controller is proposed to prevent large deviations during the first few runs in a given cycle. A fault tolerant version of the method is also proposed to handle step faults and the overall performance is compared to that of the standard threaded EWMA, showing reduction in Mean Squared Error (MSE) of about 30% to 50%. Lee et al. [126] go on to consider the robustness of the threaded R2R control methods, incorporating a number of popular control forms under the unified framework of the ODOB structure and proposing a methodology for obtaining the optimal nominal performance under the constraint of providing robust stability. 2.1.2.2. Non-threaded R2R Control Non-threaded control approaches for MMSs have continued to gain popularity and have become the subject of much recent work, especially in environments such as semiconductor manufacturing where the data poverty issue is exacerbated by the limited rate of quality measurements in intermediate manufacturing stages. Many works attempt to retain the benefits of threaded methods while relieving some of the associated challenges. For instance, Hirarchi et al. [127]

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proposed the use of a hybrid initialization approach for threaded controllers, whereby they benefit from a non-threaded approach during the initial runs of a new thread and then revert to a threaded form. The JADE algorithm for non-threaded R2R control was among the first non-threaded approaches. It was introduced by Firth et al. [128] in 2006, with a detailed comparison to the popular threaded EWMA controller also provided. The results showed that the non-threaded controller better handled shifts in context information and provided better performance when the number of context items increased. The authors also, however, demonstrated that the JADE algorithm suffers in a similar manner to the EWMA controller when there are mismatches between the plant and model. Another common approach in the design of non-threaded R2R control algorithms is the use of statistical methods for the selection of context information that impacts the outputs of interest. Ma et al. [129] proposed the use of ANOVA methods for this purpose and went on to expand this method with the use of Auto-Regressive Integrated Moving-Average (ARIMA) disturbance estimators [130]. Vanli et al. [131] propose a similar method under the assumption of a sufficiently high mix production environment. In this case, the authors distinguished interacting context items from those that could be considered independently. In [132], Bian and Pan go on to reinforce an ANOVA approach with Bayesian statistical theory, showing that this enables handling shifts in context information and can, thereby, outperform the threaded EWMA controller. In an alternative approach, a linear combination of biases related to context elements is used and Kalman filters are employed to estimate these biases [133]. Hrirachi et al. [134] then propose a method that simplifies the addition of novel context information and allows for an associated computationally efficient Kalman filter. In [135] Yelverton and Agarwal discuss a dynamic R2R control that overcomes the issues associated with common threaded and non-threaded approaches. This method was implemented in a high mix environment at the Global Foundries company. 2.2. Process Control Based on Interactions across Operations In contrast to the works previously discussed in this section, which focus on the consideration on interactions from product to product in the execution of a single operation, numerous works have also been conducted to explore the interactions from operation to operation in the processing of a single product. The works in this field are motivated by the fact that, in MMSs, variability in the final product quality characteristics are a consequence of the accumulation of errors introduced at various operations in the overall process. It was recognized that, in these cases, the design of control to minimize errors in a single operation would not suffice to control quality variation of the final product, and that this would require the control design at each operation to consider the propagation of errors through the downstream operations. Some works have considered modifying the product design tolerances to allow the product to perform satisfactorily despite

the propagation of errors through the multiple operations, e.g. [136]. Much of the work in this field deals with the modeling of MMSs in a manner that accounts for the propagation of errors throughout the process. Mantripragada and Whitney [137], for instance, propose the use of state transition models to model and control propagation of errors in mechanical assembly systems. The most popular approach for modeling and control of MMSs is the Stream of Variations (SoV) approach. This approach was introduced by Hu [138] in order to enable analysis and control of MMSs. Jin and Shi [139] proposed the use of the SoV model to represent a sheet metal assembly process to enable dimension control. Similarly, Ding et al. [140] used a state space model to describe an MMS as a discrete-time linear time varying stochastic systems. The authors argued that such model forms would naturally lend themselves to the implementation of traditional control approaches. A detailed study of the role of SoV methods in MMSs was presented in [141]. Jiao and Djurdjanovic [142] studied the compensability of errors in product quality via control based on these types of models. Djurdjanovic and Zhu [143] proposed a method for error compensation in MMSs based on SoV models, which had previously only been used for analysis [144] and fault diagnostics [145,146]. In [147], the authors then went on to present an online stochastic optimization based control scheme for based on SoV models. Fenner et al. [148] also proposed an “across-stage” control design via dynamic programming. Izquierdo et al. [149] proposed a feedforward control algorithm based on programmable tooling and SoV models. More recently, Wang et al. [150] proposed the combination of multivariate SPC and SoV models for control of machining errors. An important recent contribution has been presented by Jiao and Djurdjanovic [151], where an online stochastic optimization based control for overlay in lithography processes is presented. The uniqueness of this work is that the control strategy suggested integrates both a feedforward strategy based on an SoV model and a feedback R2R strategy. Thus, the approach proposed in this work incorporates both the productto-product and operation-to-operation interactions that contribute to variations in product quality. To the best of authors’ knowledge, this is the only publication where R2R and feed-forward model-based control approaches are considered concurrently. Such integrated control approaches, as well as robust versions of both R2R and model-based feed-forward control are likely to appear in the future. ADVANCES IN CONTROL OF MANUFACTURING OPERATIONS In this section we discuss the literature on manufacturing operation and system level decision-making for maintenance and production management. The studied research covers the time period from 1997 to 2016. A summary of the manufacturing operations decision-making frameworks

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discussed in this section is shown in Figure 7. More precisely, for the production operations section we focus on production planning and improvement, and production scheduling. Finally, we also address the work on integrated maintenance and production decision-making.

Figure 7. Manufacturing operation decision-making outline

3.1 Decision-making in the Domain of Maintenance Operations In this section, we discuss the state of the art of research on operation management and decision-making. Two major areas related to this are maintenance and productivity. Thus, we will review the state of the art on maintenance-oriented decisionmaking, productivity oriented decision-making, and joint maintenance and production decision-making. Maintenance cost accounts for a significant portion of the cost in manufacturing and can have a substantial impact on system stability and cost. Furthermore, maintenance is necessary for eliminating or minimizing machine/station failures, and stability in the production line. It is estimated that maintenance cost in the US industrial sector had reached more than $600 billion in 1981 and this figure has doubled in the past decades [152]. Thus, research on intelligent maintenance decisionmaking has become of interest in both industry and academia. Several promising and novel studies have been done to develop methods and tools for maintenance management decision-making. Accordingly, we summarize a few of them as follows. In [153] Li et al. developed a novel failure prevention model using reliability-based Dynamic Maintenance Threshold (DMT). Here, a reliability-centered DMT is used to improve the maintenance effectiveness and reduce equipment failures by maximizing system availability. They also develop an Updated Reliability Limited Maintenance (URLM) policy to update the DMT by considering both the system’s operating condition and lifetime distribution. The proposed URLM policy using the DMT has been shown to reduce system failure, effectively utilizes system remaining useful life, and increases system availability. In [154] a modified two-stage degradation model for dynamic maintenance threshold calculation considering uncertainty is developed. In this study, an approach to dynamically extract the optimal reliability threshold of a

continuously monitored system subject to degradation and uncertainty is proposed. A hybrid Preventive Maintenance (PM) model is presented. An illustration of the upper and lower bound dynamic reliability thresholds for the hybrid model is shown in Figure 8. Results indicate that unnecessary or excessive maintenance is effectively avoided and machine availability increase.

Figure 8. Hybrid model with dynamic reliability threshold [112]

In addition, in [155] a novel dynamic maintenance strategy is established and integrates single-machine optimization and the whole-system scheduling for series–parallel systems. Multiple attribute value theory and maintenance effects for the singlemachine optimization and Multi-Attribute Model (MAM) are used to define optimal maintenance intervals. The series– parallel structure of the system is investigated for the wholesystem schedule. Furthermore, a Maintenance Time Window (MTW) model for cost-effective system schedule by dynamically applying maintenance opportunities is developed and has been shown to significantly reduce costs. The study [156] presents a new approach for selecting an optimum maintenance strategy using qualitative and quantitative data while considering expert judgement and guidance. This approach uses a modified Linear Assignment Method (LAM) to develop an Interactive Fuzzy Linear Assignment Method (IFLAM). The proposed method will aid management in finding the best maintenance strategy considering a set of determined criteria. In [157] a predictive maintenance (SPII PdM) policy for single-unit batch systems is proposed using a statistically planned and individually improved method. The SPII PdM policy considers the capability of classical Statistical Lifetime Distribution based (SLD-based) PM policies for longterm planning, and the capability of PdM techniques for predicting the Residual Life (RL) of an individual system. Two lifetime margins are proposed in the decision-making process to further improve the availability of some individuals. This study illustrates the possibility of partially applying the emerging PdM techniques in the widely used SLD-based PM policy in a theoretically effective means. Also, [158] develops a two component-level control-limit PM policies for systems subject to the joint effect of partial recovery PM acts (imperfect PM acts) and variable operational conditions. The Extended Proportional Hazards Model (EPHM) is used to model the system failure likelihood influenced by both factors. The proposed policies extend the applicability of PM optimization

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techniques. Next, Fan et al. proposes a novel cooperative predictive maintenance model that incorporates hazard-rate function and effective age [159]. In this model, two failure modes are statistically dependent in such a way that the hazard rate of one failure mode depends on the accrued number of failures of the other failure mode. The successive maintenance actions and the cooperatively allocated degree of resources are determined. This approach relies on both the predicted number of future failures, and the minimization of the expected maintenance cost rate defined in the long term. In [160] the authors develop a condition-based maintenance policy considering imperfect repair for complex systems based on reliability analysis for a system subject to s-dependent competing risks of internal degradation and external shocks. They model the internal degradation as stochastic deterioration process. Using the proposed condition-based maintenance policy, the system is inspected at fixed time intervals, and a decision for an appropriate maintenance action is made based on the actual health condition of the system detected through inspection while minimizing the long-run maintenance cost rate. Their findings showed that the degradation-based failure threshold increases, the time lag between two inspections could be extended, and subsequently the cost rate is reduced. Finally, in [161] Wang et al. propose a multi-agent reinforcement learning based maintenance policy for a resource constrained flow line systems. They consider a two-machine-one-buffer system. Both machines deteriorate with time and may fail. Each quality state is represented by a yield level. A relationship between the improvement factor in the yield function and the allocated proportion of maintenance resource is developed. In addition, they formulate a discrete-time semi-Markov decision process model to describe the deteriorating process of each machine with the buffer. These above mentioned literature provides manufactures with a variety of tools for implementing maintenance management and also provide researchers with a solid framework to build upon. 3.2 Decision-making in the Domain of Production Operations Next we discuss some recent and promising work on production driven decision-making and scheduling. In manufacturing, productivity related processes constitute the largest portion of costs and energy consumption; thus interest in productivity improvement and production planning has grown. Productivity improvement involves making decisions such that key objectives such as production throughput level, make span, minimizing cost, etc. are enhanced [162]. Within productivity improvement two main areas will be covered in this review, higher level decision-making (production planning) and production scheduling. 3.2.1 Production Planning and Improvement The following production improvement and planning research has been conducted. In [163] a method for real time production improvement that can lead to continuous production

improvement towards a balanced-line status to increase the throughput efficiently is presented. A bottleneck control method that considers finite manufacturing resources to alleviate the short-term production constraints using initial buffer adjustment and maintenance task prioritization strategies is developed. The on-line bottleneck control framework used in this study is shown in Figure 9. The proposed method has shown to significantly improve system performance compared to the traditional long-term bottleneck analysis methods.

Figure 9. On-line bottleneck control framework [121]

Next in [164] a novel Parallel Tabu Search (PTS) algorithm equipped with a proper adaptive neighborhood generation mechanism is used to solve the buffer allocation problem. It consists of minimizing the total buffer capacity of a serial production system under a minimum throughput rate constraint. A multi-factorial experimental is also developed to analyze the influencing factors of the problem under investigation. In [165] Karakas et al. propose a method of determining the optimal product mix and production quantities based on an expanded activity-based costing approach when dealing with uncertain estimation of parameters. They present a mixed zero-one programming model such that the profit is maximized and is subjected activity capacities and demand of products. Additionally, fuzzy programming to handle capacity and demand vagueness. The paper in [166] presents a customer driven production planning model. The model combines buying characteristics of the customers with the production capacity needed to meet the customer orders. The proposed model can be used to determine Work-In Process (WIP) cap and the workahead-window of a CONWIP controlled production. The model can be used to implement market driven production planning. Additionally, in [167] a production-planning problem with inventory capacity as a limiting factor is investigated. These features have their roots in practice. A stockout model is presented and an algorithm with polynomial time complexity using the network flow approach is developed. Moreover, Leung et al. presents an optimization model for multi-site production planning in an uncertain environment. The total cost consists of production cost, labor cost, inventory cost, and workforce changing cost. Using this model, production management can determine an optimal production loading plan and workforce level while considering economic growth [168]. Finally, in [169], the paper proposes a production planning problem for load-dependent lead times that integrates order acceptance decisions and flexible due dates. It can determine which orders to accept and in which period they should be

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produced. The problem is formulated as a mixed integer linear program for which two relax-and-fix heuristic solution methods are proposed. In all, the above mentioned research focuses on higher level decision-making and control. 3.2.2 Production Scheduling Next we will review production scheduling research. In [170] the authors propose a method for a dynamic order acceptance and scheduling problem for single machine environments. Orders come stochastically during the planning horizon and a sequence-dependent setup time is required for orders types. A rule based on the opportunity cost of the remaining system capacity for the current system state is proposed to make order acceptance decisions and system capacity is estimated by a heuristic. Next, in [171], an effective Bi-population Estimation of Distribution Algorithm (BEDA) is presented to solve theN-Idle Permutation Flow-shop Scheduling Problem (NIPFSP) with the total tardiness criterion. Two sub-populations are used in the BEDA; and are generated by sampling the probability models that are updated for the global exploration and the local exploitation. Compared to published algorithms the proposed BEDA is effective. Next, Lin et al. in [171] studied the No-Wait flowshop Scheduling Problem (NWFSP) with makespan minimization (NP-hard). They formulate the problem as an asymmetric traveling salesman problem, and propose two matheuristics to solve it. Computational results show that the presented matheuristics outperform all existing algorithms. Subsequent we mention a novel and fundamental paper by Taillard [172] that compares different heuristic methods to solve the flow shop sequencing problem. A PTS algorithm is presented and experimental results show that this heuristic allows very good speed-up. Building upon this, [173] focuses on the problem of scheduling jobs in a permutation flowshop with the objective of makespan minimization subject to a maximum allowed tardiness for the jobs. Both machine utilization and customer satisfaction are considered. Although several approximate algorithms have been proposed for this NP-hard problem, none of them can use the method in [174] for makespan minimization due to the special structure of the problem under consideration. The proposed method allows further exploitation of the problem structure. Two algorithms are proposed: a bounded-insertionbased constructive heuristic and an advanced non-populationbased algorithm. The results show that both algorithms improve existing ones. Additionally, [175] considers hybrid flowshop scheduling problem with machine and resource-dependent processing times. In this research a new solution method and a robust hybrid metaheuristic are established to solve the sequence-dependent setup time hybrid flowshop scheduling problems. To minimize makespan and total resource allocation costs, the proposed hybrid approach comprises of two components: genetic algorithm and variable neighborhood search. Last but not least, [176] aims to integrate energy consumption as an explicit criterion in shop floor scheduling, and leverage variable speed of machining operations leading to different energy consumption levels. They develop a mixed

integer linear multi-objective optimization model to find the Pareto frontier comprised of makespan and total energy consumption. By analyzing the areas along the Pareto frontier they conclude energy saving can be justified at the expense of reduced service level and vice versa. Both production planning and scheduling are very essential components of operation management; however, machine failure and reliability can inhibit robust implementation. Integrating maintenance and production can help eliminate or reduce this problem. 3.3 Integrated Maintenance and Production Decision-making After having discussed some literature on maintenance and production driven decision-making, we now address the research on integrated production and maintenance decisionmaking. This can provide added benefits when implementing operations management activities. Some noteworthy papers are listed as follows. [177] presents an analytical, option-based cost model for integrated production and PM scheduling when demand is uncertain. The model can balance the tradeoff between reducing risk due to uncertainty and the increased cost to invest in PM resources. The proposed model increases flexibility in the production system and reduces the risk of shortage or overage of demand. Also, in [178] a novel study focusing integrating maintenance, production and quality is reported. This paper integrates production, maintenance, and quality for an imperfect process in a multi-period multi-product capacitated lot-sizing context. During each period, an imperfect machine is inspected and imperfect PM activities are simultaneously performed to reduce its age while minimizing costs and satisfying products’ demand. Furthermore, an algorithm was developed to solve the proposed problem by comparing results from several multi-product capacitated lotsizing problems. It is found that the increase in PM level leads to reductions in quality control costs. They conclude that using non-periodic PM with the possibility of different PM levels may result in an improvement of the total cost. Also, in [179] Xiao et al. develops a joint optimization scheduling model to minimize the total cost including production cost, PM cost, minimal repair cost for unexpected failures and tardiness cost. The model outputs the optimal group PM interval for machines and the assignment of each job. Due to the NP-Hard nature of the problem, the authors use random keys genetic algorithms and compare it to PSO and conclude the random keys genetic algorithm is superior on much larger problem instances. In [180] a bi-level maintenance strategy is proposed for multi-unit batch production systems. Here the authors develop a production-driven opportunistic maintenance model for batch production based on MAM–APB(Advance-Postpone Balancing) scheduling. The proposed method aims to help manufacturers eliminate unrequired production breaks, achieve significant cost reduction, and overcome system scheduling complexity. For each single machine, a MAM is used to determine maintenance intervals based on availability maximization and cost minimization. Based on real-time machine-level intervals, an APB strategy is used for the

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scheduling and produces real-time schedules. An illustration of APB programming framework is shown in Figure 10. The proposed method can proficiently eliminate unnecessary production breaks, achieve significant cost reduction, and overcome complexity of system scheduling.

Figure 10. Illustration of APB programming framework [138]

Next, [181] integrates imperfect maintenance imperfect production with economic coordination of inspection in a single machine system with multiple products. The output of the proposed model is length of sampling intervals, sample size, and the width of in-control area on the control chart. Considering imperfect maintenance, machine age, several possibilities for improving the machine conditions, a maintenance plan that determines the type of operations to be performed on the machine is developed. Lastly, [182] presents a method for integrated multi-state systems noncyclical PM and production planning. The maintenance policy suggests noncyclical preventive replacements of components, and minimal repair on failed components. Also, the model gives appropriate PM and production planning decisions. It determines an integrated lot-sizing and PM strategy of the system that will minimize the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs, and production costs, while satisfying the demand for all products over the entire horizon. A simulated annealing algorithm is recommended and developed. CONCLUDING REMARKS AND FUTURE WORK In the first section of this paper on process control approaches which rely on prediction from physical and phenomenological models to better control the manufacturing process. The general class of adaptive control strategies for quality improvement was covered, including observer-based control, Model Predictive Control, which includes both response and input parameters in the objective, and modelbased approaches that compensate for time delay processes. We also reintroduce the concept of the time scale of control, and how this framework can be used to visualize long-term process degradation and predictive maintenance approaches as just a

further class of quality feedback control. These concepts are related through the need to estimate process states and associated uncertainties; state of the art methods in this domain are also explored. Process control is also related to the productivity objective, and methods elucidated and compared with those for quality. With an accelerated growth of multi sensor-based control of manufacturing processes and generation of large sets of data stemmed from the process, future research in all these areas of process feedback control should be directed toward current efforts in industry 4.0 and digital manufacturing. Of particular interest, detection of model form changes or significant departures of process behavior from current model, quantification of marginal value from additional sensing streams being considered in a process and standardized data structures for control information, can be mentioned. In the realm of manufacturing systems control, we see a tremendous proliferation of research that takes into account interactions between products that pass through a given machine, or between operations performed on a given product. These works utilize various forms of predictive quality models, ranging from time-series models that statistically model the dynamics of quality characteristics, to first-principle based models that explicitly model influence of process parameters on product quality and transformation of product quality as it progresses through a manufacturing system, to, more recently, artificial intelligence based, data-driven models of these phenomena. These papers show potentials for tremendous improvements in product quality, if corresponding run-to-run and model-based methods were applied, especially in sophisticated manufacturing processes with numerous controllable process parameters, such as those we see in semiconductor manufacturing or chemical plants. Nevertheless, new opportunities of application of such methods in other areas of manufacturing remain. E.g., machining and assembly could tremendously benefit from run-to-run and model-based feedforward active control approaches, especially in light of accelerated developments in flexible, robotic fixtures and networked CNC machines [183-186], which is something one can expect in future Industry 4.0 enabled factories. Furthermore, from the research point of view, robust version of system-level quality and process control approaches that can handle ever-present modeling inaccuracies and noise have not been sufficiently addressed. Finally, theoretical and practical solutions to the problem of concurrently modeling and controlling product-to-product and operation-to-operation quality interactions in a manufacturing system still evade the research community and remain a major challenge. At the operations control side, one can notice intense proliferation of research on simulation-based optimization solutions to various aspects of manufacturing operations. These developments are directly spurred by advancements in computing technologies that finally enable bypassing of overlyrestrictive assumptions required by more traditional, analytical solutions pursued in the past, and allow one to realistically simulate various scenarios on the factory floor, modify,

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evaluate and optimize decisions in real-time, using fast, multicore computing infrastructures. One can expect continued research interests in this area, as well as slow adoption of such decision-making tools in manufacturing, as adequate computing infrastructure in manufacturing becomes more available (supercomputing facilities), and, perhaps more importantly, as manufacturing workforce knowledgeable and capable of operating in parallel computing environments becomes more readily available. At the end, let us please note that, besides the aforementioned opportunities for future research and application of modern control capabilities at individual process, systems and operations levels of manufacturing, a relatively untapped opportunity exists in pursuing “multiscale” solutions that cross boundaries between these aspects of manufacturing. Namely, integrated solutions that concurrently control individual processes, as well as the entire manufacturing system in which those processes exist, and are optimally coordinated with production, maintenance and logistic operations in the plant could carry significant benefits over any fragmented solution pursued in any individual domain. Such exciting solutions are certain to appear in the future.

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