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2000 Japan-USA Symposium on Flexible Automation July 23-26, 2000, Ann Arbor, Michigan, USA

IMPLEMENTATION OF NETWORKED MACHINE TOOLS IN RECONFIGURABLE MANUFACTURING SYSTEMS

Feng-Li Lian1 , James R. Moyne2 , Dawn M. Tilbury1 Engineering Research Center for Reconfigurable Machining Systems 1. Department of Mechanical Engineering and Applied Mechanics 2. Department of Electrical Engineering and Computer Science The University of Michigan, Ann Arbor, MI 48109-21251 /21082 fengli,moyne,tilbury @umich.edu


ABSTRACT This paper discusses the hierarchical model and implementation considerations of network architectures for RMS. Using a 3-axis machine tool, system performance and reconfiguration issues are parameterized and analyzed. We utilize this machine tool to address issues associated with evaluating system performance and developing control network solutions. The implementation considerations of a networked machine tool include device and network delays, node numbering, messaging connections, conformance testing, etc. The issues addressed in the paper facilitate the design of several key components of the modularity and flexibility in networked Reconfigurable Manufacturing Systems.

INTRODUCTION Reconfigurable Manufacturing Systems (RMS) can be costeffectively reconfigured to rapidly adapt the system’s manufacturing capacity and its machine functionality in a changing marketplace (Koren, 1999). Reconfigurability and adaptation are achieved through flexible control architectures and open information exchange via networks. In achieving these goals, RMS devices must be designed to function modularly and intelligently. Modularity provides standardized units or dimensions for flexibility and variety of device operations, and intelligence enables devices to function independently and to interoperate with other devices to achieve system functionality goals. With the designated RMS devices, machines and systems can be efficiently and quickly reconfigured, both in hardware and software, to meet new task requirements. However, the successful cooperation of RMS devices also relies on the reconfigurability of the communication architecture. The traditional communication architecture for control systems on machining tools is point-to-point, which has been suc-

cessfully implemented in industry for decades. However, expanding physical setups and machining systems functionality push the limits of the point-to-point architecture. Hence, a traditional centralized point-to-point control system is no longer suitable to meet new requirements, such as modularity, decentralization of control, integrated diagnostics, quick and easy maintenance, and low cost (Eccles, 1998). Recently, network systems with common-bus architectures have been receiving significant attention. These distributed common-bus systems, called Networked Control Systems (NCSs), provide several advantages such as small volume of wiring and distributed processing, and support the implementation of RMS. Especially with respect to RMS, the NCS architecture supplies higher intelligence at nodes for modularization of functionality and standard interfaces for interchangeability and interoperability. Object based device models separate generic, device-type specific from manufacturer specific functionality, thereby allowing reconfiguration at various levels (Koren, 1999). When configuring sensors, actuators and controllers together in a reconfigurable machine tool, it is important to investigate the impact of the functionality and limitation of these devices on the capability of a network system. This is one of the issues being investigated in a research effort whose primary goal is to develop methodology and tools for assisting the implementation of a networked architecture at the machine level of an RMS. This paper focuses on the study of network systems for control system applications, and presents results on NCS design and analysis of a networked machine tool, called the “Robotool,” in the Engineering Research Center for Reconfigurable Machining Systems (ERC/RmS) at the University of Michigan. In the following sections, we will address the basics of network systems and their role in RMS, investigate the impact of network architecture on the “Robotool” machine controller design, discuss networked machining cells implementation considerations,

and end with a summary and discussion of future work.

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NETWORK AND CONTROL IN RMS Network Architecture A network architecture, with interconnected devices such as sensors, actuators and controllers, uses less wiring, and requires less maintenance than a point-to-point architecture. It also makes it possible to distribute processing functions and loads into several small units. Furthermore, the diagnosis of communication health and device condition is more easily implementable through a network hierarchy. Manufacturing systems networks have many levels of hierarchy, each one having different goals and also different required communication protocols and capabilities. Generally, communication enabling technologies at the lower levels are referred to as “sensor buses” and/or “field buses” (Schickhuber and McCarthy, 1997). The networks utilized at these levels can be divided into two categories, namely data networks and control networks, (Raji, 1994). Generally speaking, data networks use large data packets and relatively infrequent bursty transmission over a wide area with high data rates to support the transmission of large data files. Data networks do not have hard time-critical constraints. Control networks, in contrast, must shuttle countless small, but frequent packets among a relatively large set of nodes to meet the time-critical requirements. The key element that distinguishes control networks from data networks is the capability to support time-critical applications. Hence, the communication of control systems should be implemented only through control networks. Networked Control Systems Networked machine tools, by definition, are NCSs with a distributed system of controllers, actuators and sensors connected by a control network and designed for the exchange of information between these devices. Intelligence and decision making can be moved out of the central control units and distributed into controllers located near the controlled devices. Hence, the processing load on single central control unit can be assigned into several small processors which are dedicated to a part of control process of a group of actuators/sensors. These processors can communicate through the bus network and cooperate with each other to accomplish their designated tasks. Moreover, devices with intelligence can improve signal processing and communication of the measured information. For control systems, candidate control networks generally must meet two important criteria: bounded time delay and guaranteed transmission. That is, a message should be transmitted successfully within a predetermined time. Lost or excessively delayed messages from a sensor through a controller to an actuator, for example, may deteriorate system performance or re-

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Figure 1. THE CLOSED-LOOP MODEL OF NETWORK CONTROL SYSTEMS.

sult in instability. In implementing a control system with a networked architecture, a new constraint must be accommodated — the limited bandwidth of the communication medium. Different networks have different protocols to allocate the available bandwidth to the devices on the networks. For each subsystem in an NCS, the closed-loop system can be simply represented by the block diagram shown in Figure 1. As illustrated in this figure, a major drawback of an NCS when compared with a traditional control system is the networkinduced time delays between sensors/actuators and controllers. These time delays are not only variant at each sampling instant, but different among different devices in the same subsystem. For control systems, the exact values and characteristics of time delays have a large impact on system performance and stability. Hence, modeling and controller design must be first done to build the theoretical foundation before networked control systems can be successfully implemented.

IMPLEMENTATION OF NETWORKED MACHINE TOOLS Case Study: Networked “Robotool” The issues of developing, parameterizing and analyzing a reconfigurable NCS are being pursued at the University of Michigan in part through application to an industrial tool. Figure 2 illustrates this tool, which is a three-axis (X, Y and Z) machining system, called the “Robotool.” Each axis moves on a linear slide and is driven through a ball screw by a DC motor with a tachometer which provides an angular velocity measurement. Each axis also has a linear encoder with a resolution of 10 µs that provides linear position measurement. Both position and velocity signals are available and sampled every 10 ms. The motor is driven by a PWM drive with an 8-bit input, that is, between 0 and 255. In the past, several authors have used the Robotool as a testbed for research on open architecture control systems (Koren et al., 1996), supervisory control (Landers and Ulsoy, 1998), and crosscoupling control (Chen et al., 1998). All previous work used a traditional point-to-point communication architecture. Because of the hardware setup, it is very difficult and time-

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Figure 2. THE DEVICENET NETWORKED ROBOTOOL.

consuming to add/remove system components and to perform diagnosis when system functionality goals change. Moreover, the currently used sensors and actuators are passive devices and cannot operate independently. Also, the signals generated by these devices are not unified, cannot be shared by other devices, and need further processing before usage. Hence, the current architecture is not reconfigurable. We are installing a DeviceNet control network1 on the Robotool, as shown in Figure 2, to study the advantages of a distributed architecture. The first advantage of networked architecture (as shown) is the reduction of wiring. Depending on the communication protocol, this networked control system may also provide attractive features with respect to different communication models supported. These include client/server (one-to-one interaction) and publisher/subscriber (one-to-many interaction). Because all the devices, including sensors, actuators, controllers and monitoring tools, are interconnected by a common-bus communication medium, the information generated by one sensor can be conveniently accessed by several control devices (publisher/subscriber communication mode) without having to introduce any duplication of sensors and algorithms to distribute the information as in traditional systems. Therefore, the information of any device on the same network is easily shared by other devices and less hardware setup is needed than in point-to-point systems. NCS Performance Analysis Due to the limitation of the network bandwidth, devices with information must wait until the network is available before they can transmit. The waiting mechanism depends on the types of message connections (e.g., multi-cast, polling, change of state, cyclic), and the network traffic load. Hence, the signals from

1 DeviceNet

is a sensor bus communication protocol commonly used as an NCS. DeviceNet utilizes a non-destructive collision resolution scheme through message priority

sensors, controllers or actuators are delayed information and possibly sampled at different sampling instants. Furthermore, different network protocols exhibit different characteristics of time delays which are not always constant, but possibly random or bounded (Lian et al., 1999). The characteristics of time delays will affect the control performance of machine tools within one axis as well as the cooperation of different axes. Therefore, an advanced controller design should consider the impact of network protocols and parameters on the delay characteristics and system performance. For example, one important parameter affecting the control performance of an NCS is the time delay associated with sensing or actuation messages. The major components of this time delay are device delay and network delay. We have determined that typical device response time in the ranges of 155 to 677 µs based on the measurement performed at our Sensor Bus Conformance Testing Laboratory; the transmission time without any message contention ranges from 94 to 158 µs. Hence, the device delay dominates over the message time delay when the network load is medium or low. However, if the devices have faster processing ability or the network load increases, the network delay will dominate over the message time delay. For example, considering only the X-axis system, the network delays for the three devices (motor, tachometer, encoder) are 111, 222, 333 µs, respectively if the message size is one byte and the network data rate is 500K (DeviceNet). After adding two more axes, the network delays for these 9 devices vary from 111 to 999 µs. These network delays will have large influence on the control performance. It can be expected that in the future devices will have better processing ability and induce smaller time delays. Therefore, we need to further study the effect of the network delay on the system performance. A second important system design parameter, especially for the DeviceNet network, is the node numbering. Due to the node/message priority, high-priority nodes can access the network medium first than low-priority nodes. Hence, for the same system configuration, different node numbering may have different system performance. Figure 3 illustrates the performance evaluation for 6 different node numberings. The vertical axis is the performance criteria, i.e., the integral of time multiplied by absolute error (ITAE), and the horizontal axis is the message frequency (number of messages per second) which simulate different network load. Fig 3 suggests that it is possible to obtain better system performance by choosing a proper node numbering. The messaging methods utilized in the control network can also significantly affect the time delay. Practical message methods utilized in DeviceNet include multicast (strobe), poll, and change of state or cyclic. Multicast messaging induces the most message contention because all devices try to respond to the request at the same time (after they are “strobed”). On the other hand, poll messaging requests each node to respond individually and intends to reduce the possibility of message contention.

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Figure 3. PERFORMANCE (ITAE) VS MESSAGE FREQUENCY USING DIFFERENT NODE NUMBERINGS.

However, because each node requires an individual request message, this method requires almost double the number of required messages of the multicast case. Issues in Achieving Effective NCS solutions In order to achieve effective NCS solutions for RMS control systems a number of issues must be addressed in addition to the performance analysis issues outlined in the previous sections. One important issue involves the utilization of smart devices which are smart sensors, smart actuators and networked controllers. Smart sensors or actuators have three major features: intelligence, communication ability, and data acquisition or actuation, respectively, (Eccles, 1998). Besides network-capable application processors, the major functionalities of networked controllers are to analyze the sensor data, make decisions, and give commands to actuation devices. The control algorithms should handle decentralized information analysis as well as the traditional centralized cases. In order to guarantee a level of reconfigurability we must also guarantee a level of interoperability and interchangeability of devices. This is achieved through a process called conformance testing. Currently, we are conducting different levels of conformance tests for ControlNet, DeviceNet, and Modbus/TCP at the Sensor Bus Laboratory at the University of Michigan. The main conformance tests include the protocol test, the physical layer test, and the interoperability test. The purpose of these conformance tests is to guarantee the interoperability and interchangeability of networked devices.

SUMMARY AND FUTURE WORK This paper provided the study of key features in networked control systems, and the implementation consideration of a networked machine tool. We summarized a hierarchical model of network architecture in RMS with industrial networks based on their applications. The characteristics of networked control sys-

tems were addressed to provide guidelines for designing control systems using network communication. We discussed the transformation of a networked machine tool from the point-to-point to common-bus architecture. The implementation issues in terms of device and network delays, node numbering, and messaging connection were also addressed to help analyze the control performance and provide effective NCS solutions. The result of this paper will be helpful to anyone who wants to set up a networked architecture for machine tools or cells in a Reconfigurable Manufacturing System. Future work includes the characterization of network-induced time delays from different protocols and message connection types, which have large impact on system performance and stability of control systems, and the advanced controller design that can take into account different characteristics of time delays.

ACKNOWLEDGMENT This research was supported in part by the NSF Engineering Research Center for Reconfigurable Machining Systems at the University of Michigan under grant EEC95-92125. The authors would like to thank Drs. Robert Landers and Byung-Kwon Min for valuable information about the Robotool and McNaughtonMcKay Electric Company and Western Reserve Controls, Inc. for supplying DeviceNet devices.

REFERENCES Chen, B.-C., Tilbury, D., and Ulsoy, A., 1998, “Modular control for machine tools: cross-coupling control with friction compensation,” in: Proc. of the ASME DSC Division, Vol. DSCVol. 64, pp. 455–462. Eccles, L. H., 1998, “A Smart Sensor Bus for Data Acquisition,” Sensors, Vol. 15, No. 3, pp. 28–36. Koren, Y., 1999, “The Third Year Report: 1998-1999,” Tech. rep., NSF-Engineering Research Center for Reconfigurable Machining Systems, The University of Michigan. Koren, Y., Pasek, Z., Ulsoy, A., and Benchetrit, U., 1996, “Realtime Open Control Architectures and System Performance,” CIRP Annals, Vol. 45, No. 1, pp. 377–380. Landers, R. and Ulsoy, A., 1998, “Supervisory machining control: design approach and experiments,” CIRP Annals - Manufacturing Technology, Vol. 47, No. 1, pp. 301–306. Lian, F.-L., Moyne, J., and Tilbury, D., 1999, “Performance evaluation of control networks for manufacturing systems,” in: Proc. of the ASME DSC Division, Vol. DSC-Vol. 67. Raji, R., 1994, “Smart Networks for Control,” IEEE Spectrum, Vol. 31, No. 6, pp. 49–55. Schickhuber, G. and McCarthy, O., 1997, “Distributed Fieldbus and Control Network Systems,” Computing & Control Engineering, Vol. 8, No. 1, pp. 21–32.