12
Process Control

Process control can be viewed as the executive portion of a unit process. It provides the means to direct a process so that it produces the desired results. It is complementary to the role played by the process equipment itself (discussed in Chapter 14), which furnishes the physical means to accomplish the process. The trend for process controllers is to incorporate a greater degree of "intelligence" and to be integrated into a plant-wide information network that automates many management tasks, such as detailed scheduling (Considine, 1993). This chapter addresses issues and opportunities for the individual process controllers.

The traditional approach to process control involves an initial calibration of the equipment, monitoring of primary process parameters, and results in a final accept/reject inspection of the resulting product. This approach does not recognize the interdependence of process parameters and does not allow adjustment of the process to optimize yield. However it can be a realistic control strategy for relatively simple processes. A more sophisticated control strategy is known as feedforward control. It can compensate for dynamic delay in the feedback loop by anticipating the control settings using a process model. Such a control system has been commercialized in modem machine tool controllers that can provide an order of magnitude improvement in machining accuracy (Tomizuka, 1989).

Advanced control methodologies (such as adaptive control and intelligent control), as well as improvements in computer and information technologies (such as digital signal processors, workstations, and real-time operating systems), can be used to make manufacturing processes more flexible and adaptive, while maintaining optimum process performance (Wright and Bourne, 1988).



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--> 12 Process Control Process control can be viewed as the executive portion of a unit process. It provides the means to direct a process so that it produces the desired results. It is complementary to the role played by the process equipment itself (discussed in Chapter 14), which furnishes the physical means to accomplish the process. The trend for process controllers is to incorporate a greater degree of "intelligence" and to be integrated into a plant-wide information network that automates many management tasks, such as detailed scheduling (Considine, 1993). This chapter addresses issues and opportunities for the individual process controllers. The traditional approach to process control involves an initial calibration of the equipment, monitoring of primary process parameters, and results in a final accept/reject inspection of the resulting product. This approach does not recognize the interdependence of process parameters and does not allow adjustment of the process to optimize yield. However it can be a realistic control strategy for relatively simple processes. A more sophisticated control strategy is known as feedforward control. It can compensate for dynamic delay in the feedback loop by anticipating the control settings using a process model. Such a control system has been commercialized in modem machine tool controllers that can provide an order of magnitude improvement in machining accuracy (Tomizuka, 1989). Advanced control methodologies (such as adaptive control and intelligent control), as well as improvements in computer and information technologies (such as digital signal processors, workstations, and real-time operating systems), can be used to make manufacturing processes more flexible and adaptive, while maintaining optimum process performance (Wright and Bourne, 1988).

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--> Architectures for A Self-Sustaining Work Environment Control system architectures that feature a closed feedback loop involving the process, sensors, controller, and actuators are a step beyond the traditional approach. Control algorithms, such as the PID (proportional, integral, derivative) algorithm, reside in the controller, which is a special type of digital computer. The challenge is to control what is basically a dynamic analog process (e.g., machining) with discrete digital logic. The control architecture must be designed to ensure that the process can always operate optimally under the presence of various uncertainties. Thus there may be multiple layers of feedback control loops (e.g., servo-control loops around the machinery itself, control loops around the tool and the workpiece for fine adjustment of operating condition, etc.). The design of feedback control algorithms is affected by a number of factors. The key issue arises from the dynamics involved in the process. The controlled variable does not respond instantaneously to the controlling input, which results in a characteristic response curve with dynamic delay. Process models implemented in the controller must manage the dynamics properly. A fixed-parameter controller has difficulty in keeping up with the nonlinear, time-varying behavior of a process. Good control performance at one operating condition can give way to poor performance at another operating condition. These models may be further constrained by the amount of bandwidth for the feedback loop (i.e., the closed-loop response speed) and the product specifications, such as error tolerance. Control algorithms currently used in manufacturing are commonly simple PID control algorithms that use a low-order transfer function model. This technology is adequate for traditional machining operations in which the machining speed is low. Also, the performance limitation of these PID controllers provides for only a low level of closed-loop performance in unit manufacturing processes, which is reflected in the final product quality level. Sophisticated control architectures are required for modem unit processes that inherently possess time-varying, nonlinear process dynamics and are high performance in terms of speed and control accuracy (e.g., high-speed machining). For instance, a manufacturing manager recently observed that "a high-speed spindle is worthless unless the machine can feed fast enough to exploit it and the cnc [computer numerical control] is fast enough to keep everything under control" (Coleman, 1992). Advances in control theory, as well as those in microprocessor and digital signal processing technology achieved over the last several decades, can be and should be utilized aggressively to face these new challenges in modern manufacturing.

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--> There are several advanced control methodologies applicable to manufacturing process control. One type of controller adjusts set points as a result of data received from sensor arrays (Hardt, 1993; Ulsoy and Koren, 1993). For example, a model-based adaptive controller1 could employ an algorithm to compensate for dimensional errors induced by thermal distortion of workpieces. Adaptive and robust control theory has been an active research topic for the past two decades. The research addresses the problem of how to attain optimum system performance when a process model is not known precisely in advance, the operating conditions are variable, the process parameters vary nonlinearly during operation, etc. The philosophy behind adaptive control theory is that the controller must adapt its control gains so that the overall system remains at or near the optimal condition in spite of varying process dynamics. Adaptive control is a key element to provide flexibility to unit manufacturing processes, which must be responsive to rapidly changing needs of products. On the other hand, the philosophy behind robust control theory is that a fixed-gain controller should be selected, so that the performance of the overall system remains acceptable under variations of process dynamics. There is significant potential benefit to applying adaptive and robust process controllers. Disturbance observer theory, a robust control methodology, has been shown to be ideally combined with feed-forward control algorithms to provide high accuracy performance for servo-systems, which are essential in high-speed machining. Successful experimental results have been reported for adaptive force control in machining and adaptive weld-pool control in welding. Important forms of adaptive control are the self-tuning controllers that were developed to overcome the limitations of fixed-point controllers in responding to time-varying process dynamics, variable operating conditions, nonlinear process dynamics, and lack of operator expertise during control-loop commissioning. Self-tuning controllers use process identification algorithms to estimate or track the time variation of key process parameters in real time. Based on these results, control parameters are computed in real time to ensure optimal system performance. Two entirely different types of self-tuning controllers have been developed—expert systems and process models. An expert system consists of a set of rules that are derived from the knowledge of experienced process engineers and operators. A fuzzy logic controller is a viable candidate to translate human 1   The word "adaptive" is used here to emphasize the use of temperature sensor information that goes beyond normal PID controls for servo-loops. However, it should be noted that, in the control community, adaptive control includes more than sensor and model-based feedback control.

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--> knowledge to control strategies and algorithms suitable for computer implementation. Advantages of the expert system approach are that it is robust and thus additional rules can be readily added and that a process model is not required. But there are some disadvantages. The expert system usually is developed using a particular controller structure and thus cannot be readily ported to another type of controller. Also, the rule base itself can not be readily analyzed. The model-based controller uses rigorously defined performance criteria, and hence mathematical analysis of these criteria is possible. It can be adapted for implementation in different controller structures, and it may be used for process diagnostics, such as to locate a failed sensor or actuator. The disadvantages include the chance that the model structure may not match the physical process; for example, an actuator dead zone or backlash could cause underestimation of process gain. Also, rapidly occurring process changes can cause problems if the model execution time is too slow. The controller of the future will most likely incorporate both the expert system and the model-based technologies. A critical issue is the customization of these modem control algorithms to specific manufacturing applications such that stable performance results. "Internal state" has been a key concept in modem control, and control theory has been advanced together with estimation theory. Estimation theory provides methodologies to estimate "state," which may not be directly measured. The estimated state can be utilized for state feedback control as well as for monitoring and failure detection. Learning control can be used to learn the optimum control input through repeated trials (Dagli, 1994). When unit processes repeat the same task, this control methodology fine tunes the controller's performance. For example, in injection molding, the piston speed must be controlled so that the flow of molten plastic reaches all parts of the mold and no voids are created. Learning-control algorithms can be combined with simulation models and operational data to evaluate the performance of each trial injection. The time profile of the piston speed is adjusted after every trial until a quality product is produced. The number of trials required depends on the complexity of the process. This type of scheme also has been tested in machining to compensate for low-velocity friction forces. It has been demonstrated that a dozen or so trials are sufficient to construct a compensation signal to remove undesirable glitches, which are visible in the part geometry as irregularities in machining that are caused mainly by static friction. Intelligent control has received increasing attention over the past few years. Intelligent control systems have the ability, to varying degrees, to find strategies autonomously in an uncertain environment. Intelligent controllers rely on a knowledge base, which may contain experts' knowledge about operations of unit

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--> processes. The knowledge base may come from process study and modeling and may be updated by a learning mechanism during operation. Intelligent controllers may provide a signal to switch operational modes for a process responding to sensor outputs. The development of strategies for intelligent controllers includes expert systems. Introduction of a new controller usually requires some modification to other machine functions. For example, in some cases the controlling input (i.e., manipulated variable) for adaptive force control in machining is the tool feedrate. The adjustment of feedrate requires coordination of the machine tool's servo-controllers as well as its computer numerical control functions. Traditional computer numerical controls generate reference signals for servo-loops after linear and circular interpolation in accordance with the tool feedrate supplied by part programmers. However, this approach will not be feasible if the feedrate is varied in real time. Controllers To implement advanced control algorithms, modem technology in computer workstations, real-time operating systems,2 and bus structures should be utilized. Today's computer numerical controls are very limited in terms of programming flexibility and communications with external computers and devices. Standard configurations cannot accommodate nonmachining devices such as work-holding accessories, force sensors, vision sensors, and other subsidiary devices. Although they use advanced electronics, computer numerical controls design concepts are conservative, especially in terms of hardware and user interfaces. Computer devices and architectures such as magnetic disk storage and data busses have only recently appeared—usually as proprietary products—and the application of computer innovations such as the latest microprocessors are always late. Advancements in software engineering, such as object-oriented programming, should be exploited to allow rapid development of the computer programs that implement the advanced control algorithms. Current computer numerical control communications are principally through slow serial lines, such as RS-232, which cannot support real-time control. Advanced communication networks (e.g., the Manufacturing Automation Protocol), introduced in 1989, provide for real-time control, but the effectiveness 2   "Real-time" here means that the time constant for the measurement and analysis is commensurate with the time scale of the process itself.

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--> depends on adherence to standard protocols that allow easy integration accommodation of computer programs and peripheral hardware. Although full-scale integration of all the possible hardware peripherals has not been implemented, many of the individual technologies and components of unattended machine tools already exist as experimental devices or as commercial products. For example, a self-sustaining machine, for any kind of processing operation, would be serviced by a dexterous manipulator or other automatic loading device, dedicated to the continuous needs of the process, such as supplying material and unloading finished parts. A variety of on-machine sensors would provide vision, touch, force, and temperature senses in order to recognize unexpected events, perform in-cycle inspection, and optimize the production parameters. Access to a rich supporting design environment would also be essential, including computer-aided design and computer-aided manufacturing for part and tool design, expert systems, and libraries of technical information for an optimal and efficient design. The following is a cursory list of available devices and products that can satisfy many of the needs of the above example: a dextrous manipulator (Greenfeld et al., 1988); sensors for machining (Tlusty and Andrews, 1983); an automated fixturing system (Hazen and Wright, 1988); in-cycle gauging (Valysis Corp., 1988); adaptive control; advanced computer-aided design and computer-aided manufacturing applications (Beeby and Collier, 1986); machinist expert system for setup planning (Hayes and Wright, 1986); tools and fixtures (CarrLane Manufacturing Co., 1988); manufacturing languages (Nackman et al., 1986 and Bourne, 1986); general purpose computer technology; communication protocol (World Federation of MAP Users Group, 1987); and communications networks for manufacturing (Hughes and Dytewski, 1987). The greatest challenge in controller design is to select the appropriate computer environment for the integration and implementation of a complex machine tool environment that applies these individual technologies. This challenge cannot be met with current off-the-shelf controller technology.

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--> Open Systems for Control and Communication An open architecture for the controller, which is based on mainstream, well-established computer technology such as the workstation, is highly desirable (Proctor and Michaloski, 1993). Such an architecture avoids the difficulties of using proprietary technology and offers an efficient environment for operation and programming, offers ease of integrating various system configurations and computer products, and provides the ability to communicate more effectively with computer-aided design, computer-aided manufacturing systems and factory-wide information management systems. It would also be cost-effective given the current price-performance trends in the general purpose computer industry. The controller may be based on a high-end personal computer or workstation, with additional control processors on a shared bus; it would be using a real-time operating system. An open architecture allows for universal and modular installation, since all the components share the same high-level operating system and programming environment, communication facilities, and other computer resources. An open system would, by definition, accommodate the installation of new devices and sensors as required for a machine-specific configuration. There is a need for a real-time operating system that provides the response time and features essential for maintaining the speed, accuracy, and safety features for controlling advanced process equipment. This operating system should interface to industry standard operating systems such as Unix or OS/2 and provide high-level management, file system operations, communications, and a good programming environment. To be part of a broad, intelligent environment, the machinery must make use of communications and networks that are universally accepted in the computer culture. Standard protocols, such as the Manufacturing Automation Protocol, must be supported easily. The machine should be adaptable to the changing environment and tasks and thus be modular in terms of its controller's computer configuration and its mechanical construction. Research Opportunities Advanced control architectures, such as adaptive and robust control, should be extended for application to unit processes that have time-varying dynamics and a high level of uncertainty regarding control inputs. This research would include analysis and simulation, as well as demonstration through critical experiments. Process-level analyses are necessary to understand the influence that the introduction of a new control algorithm will have on the overall operation of

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--> a unit process. For example, a process-level study of advanced control algorithms for machine tool controllers is strongly encouraged in order to examine the issues related to the servo level, force control level, control for tool deflection, and computer numerical control algorithm and for the integration of all these technologies. Learning control can be applied to unit processes that involve the repetition of the same task or cyclical operations. Research issues include appropriate models for the design of learning-control algorithms and analytical and experimental investigation concerning the convergence characteristics of learning-control algorithms. The potential of extending the capability of equipment by smart control algorithms provides a research challenge. For example, researchers have shown that standard machining lathes can manufacture workpieces with noncircular cross-sections (e.g., an oval-shaped piston cylinder) by adding a servo axis in the direction normal to the workpiece surface. Incorporation of expert knowledge in an intelligent control system is a high priority. Fuzzy logic, neural networks, expert systems, and genetic algorithms are promising tools to capture and organize such information. Control needs of future unit processing machinery can be grouped into two central themes: a self-sustaining work environment and an open system for control and communication. The following are specific examples for R&D that will provide key elements for implementation of new control algorithms. -   the development of a real-time operating system suitable for the very high-speed control required for unit processing operations; -   the development of advanced manufacturing languages; while existing languages (such as APT and Compac for machining) will be supported, a more flexible language is needed; it should include provisions not only for real-time control but also for the operation of accessory devices in conjunction with the machining process, a more direct connection to computer-aided design and manufacturing systems, and a flexible interface for user applications; and -   experience with the integration of the Open System Machinery Controller, using an open-architecture operating system, based on readily available, well-established computer technology, such as the workstation.

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--> References Beeby, W., and P. Collier. 1986. New Directions Through CAD/CAM. Dearborn, Michigan: Society of Manufacturing Engineers. Bourne, D.A. 1986. CML: A Metta interpretor for manufacturing. Artificial Intelligence Magazine 7(4):86-96. CarrLane Manufacturing Co. 1988. Tooling Component Library in 3-D IGES Format. St. Louis, Missouri: CarrLane Manufacturing Co. Coleman, J.R. 1992. No myth high speed machining. Manufacturing Engineering 109:61-65. Considine, D.M., ed. 1993. Process/Industrial Instruments and Control Handbook. New York: McGraw-Hill. Dagli, C.H. 1994. Artificial Neural Networks for Intelligent Manufacturing. New York: Chapman and Hall. Greenfeld, I., H.B. Hansen, and P.K. Wright. 1988. Self-Sustaining Open-System Machine Tools. New York: New York University, Courant Institute of Mathematical Sciences. Hardt, D.E. 1993. Modeling and control of manufacturing processes: getting more involved. American Society of Mechanical Engineers. Transactions: Journal of Dynamic Systems, Measurement and Control 115(2(B)):291-300. Hayes, C., and P.K. Wright. 1986. Automated planning in the machining domain. Pp. 221-232 in Knowledge Based Expert Systems for Manufacturing. Dearborn, Michigan: American Society of Mechanical Engineers. Hazen, F.B., and P.K. Wright. 1988. An autonomous, self loading machine tool. Proceedings of the Manufacturing 1988 Conference. Dearborn, Michigan: American Society of Mechanical Engineers.

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--> Hughes, K., and D.A. Dytewski. 1987. Selection and implementation of a factory automation LAN, Autofact '87 Conference Proceedings held November 1987. Dearborn, Michigan: Society of Manufacturing Engineers. Nackman, L.R., M.A. Lavin, R.H. Taylor, W.C. Dietrick Jr., and D.D. Grossman . 1986. AMLIX: A Programming Language for Design and Manufacturing. Research Report 11992 No. 54006. Yorktown Heights, New York: International Business Machines Thomas J. Watson Research Center. Proctor, F.M., and J. Michaloski. 1993. Enhanced Machine Controller Architecture Overview, NISTIR 5331. Gaithersburg, Maryland: National Institute of Technology and Standards. Tlusty, J., and G.C. Andrews. 1983. A critical review of sensors for unmanned machining. Institute for Production Engineering Research Proceedings 32(2):563-575. Tomizuka, M. 1989. Design of digital tracking controllers for manufacturing applications. American Society of Mechanical Engineers. Manufacturing Review 2(2): 134-141. Ulsoy, A.G., and Y. Koren. 1993. Control of machining processes. American Society of Mechanical Engineers Transactions, Journal of Dynamic Systems, Measurement and Control 115(2(B)):301-308. Valysis Corp. 1988. Softgauge. Santa Clara, California: Valysis Corporation. World Federation of MAP Users Group. 1987. MAP Reference Specification—Manufacturing Automation Protocol (based on GM MAP 2.2 Specification). Dearborn, Michigan: Society of Manufacturing Engineers. Wright, P.K., and D.A. Bourne. 1988. Manufacturing Intelligence. Reading, Massachusetts: Addison-Wesley.