Integrated sensors and control systems are the way of the future. In times of disaster, even the most isolated outposts can be linked directly into the public telephone network by por- table versions of satellite earth stations called very small aperture terminals (VSATs). They play a vital role in relief efforts such as those for the eruption of Mount Pinatubo in the Philippines, the massive oil spill in Valdez, Alaska, the 90,000-acre fire in the Idaho forest, and Hurricane Andrew’s destruction in south Florida and the coast of Louisiana.
LIDAR (light detection and ranging) is an optical remote sensing technology that measures properties of scattered light to find range and/or other information of a distant target. The prevalent method to determine distance to an object or surface is to use laser pulses. Like the similar radar technology, which uses radio waves instead of light, the range to an object is determined by measuring the time delay between transmission of a pulse and detection of the reflected signal. LIDAR technology has application in archaeology, geog- raphy, geology, geomorphology, seismology, remote sensing, and atmospheric physics.
VSATs are unique types of sensors and control systems. They can be shipped and assembled quickly and facilitate communications by using more powerful antennas that are much smaller than conventional satellite dishes. These types of sensors and control systems provide excellent alternatives to complicated conventional communication systems, which in disasters often experience serious degradation because of damage or overload.
Multispectral sensors and control systems will play an expanding role to help offset the increasing congestion on America’s roads by creating “smart” highways. At a moment’s notice, they can gather data to help police, tow trucks, and ambulances respond to emer- gency crises. Understanding flow patterns and traffic composition would also help traffic engineers plan future traffic control strategies. The result of less congestion will be billions of vehicle hours saved each year.
In Fig. I.1, the Magellan spacecraft is close to completing its third cycle of mapping the surface of planet Venus. The key to gathering data is the development of a synthetic aperture radar as a sensor and information-gathering control system, the sole scientific instrument aboard Magellan. Even before the first cycle ended, in mid-1991, Magellan had mapped 84 percent of Venus’ surface, returning more digital data than all previous U.S. planetary missions combined, with resolutions ten times better than those provided by earlier mis- sions. To optimize radar performance, a unique and simple computer software program was developed, capable of handling nearly 950 commands per cycle. Each cycle takes a Venusian day, the equivalent of 243 Earth days.
Manufacturing organizations in the United States are under intense competitive pres- sure. Major changes are being experienced with respect to resources, markets, manufactur- ing processes, and product strategies. As a result of international competition, only the most productive and cost-effective industries will survive.
Today’s sensors, remote sensors, and control systems have explosively expanded beyond their traditional production base into far-ranging commercial ventures. They will play an important role in the survival of innovative industries. Their role in information
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assimilation, and control of operations to maintain an error-free production environment, will help enterprises to stay effective on their competitive course.
ESTABLISHING AN AUTOMATION PROGRAM
Manufacturers and vendors have learned the hard way that technology alone does not solve problems. A prime example is the gap between the information and the control worlds, which caused production planners to set their goals according to dubious assumptions concerning plant-floor activities, preventing plant supervisors from isolating production problems until well after they had arisen.
The problem of creating effective automation for an error-free production environment has drawn a long list of solutions. Some are as old as the term computer-integrated
FIGURE I.1 The Magellan Spacecraft. (Courtesy Hughes Corp.)
manufacturing (CIM) itself. However, in many cases, the problem turned out not to be tech- nology but the ability to integrate equipment, information, and people.
The debate over the value of agile manufacturing and computer-integrated manufac- turing technology has been put to rest, although executives at every level in almost every industry are still questioning the cost of implementing CIM solutions. Recent economic belt tightening has forced industry to justify every capital expense, and CIM has drawn fire from budget-bound business people in all fields.
Too often, the implementations of CIM have created a compatibility nightmare in today’s multivendor factory-floor environments. Too many end users have been forced to discard previous automation investments and/or spend huge sums on new equipment, hardware, software, and networks in order to effectively link together data from distinctly dissimilar sources. The expense of compatible equipment and the associated labor cost for elaborate networking are often prohibitive.
The claims of CIM open systems are often misleading. This is largely due to proprietary concerns, a limited-access database, and operating system compatibility restrictions. The systems fail to provide the transparent integration of process data and plant business infor- mation that makes CIM work.
In order to solve this problem, it is necessary to establish a clearly defined automation program. A common approach is to limit the problem description to a workable scope, eliminating the features that are not amenable to consideration. The problem is examined in terms of a simpler workable model. A solution can then be based on model predictions.
The danger associated with this strategy is obvious: If the simplified model is not a good approximation of the actual problem, the solution will be inappropriate and may even worsen the problem.
Robust automation programs can be a valuable asset in deciding how to solve produc- tion problems. Advances in sensor technology have provided the means to make rapid large-scale improvements in problem solving and have contributed in essential ways to today’s manufacturing technology.
The infrastructure of an automation program must be closely linked with the use and implementation of sensors and control systems within the framework of the organization.
The problem becomes more difficult whenever it is extended to include the organizational setting. Organization theory is based on a fragmented and partially developed body of knowledge, and can provide only limited guidance in the formation of problem models.
Managers commonly use their experience and instinct in dealing with complex production problems that include organizational aspects. As a result, creating a competitive manufactur- ing enterprise—one involving advanced automation technology utilizing sensors and control systems and organizational aspects—is a task that requires an understanding of both how to establish an automation program and how to integrate it with a dynamic organization.
In order to meet the goals of integrated sensory and control systems, an automated manu- facturing system has to be built from compatible and intelligent subsystems. Ideally, a manu- facturing system should be computer-controlled and should communicate with controllers and materials-handling systems at higher levels of the hierarchy, as shown in Fig. I.2.
UNDERSTANDING FLEXIBLE WORKSTATIONS, FLEXIBLE WORK CELLS, AND FLEXIBLE WORK CENTERS
Flexible workstations, flexible work cells, and flexible work centers represent a coordinated cluster of a production system. A production machine with several processes is considered a workstation. A machine tool is also considered a workstation. Integrated workstations form
a work cell. Several complementary workstations may be grouped together to construct a work cell. Similarly, integrated work cells may form a work center. This structure is the basic concept in modeling a flexible manufacturing system. The flexible manufacturing sys- tem is also the cornerstone of the computer-integrated manufacturing strategy (Fig. I.3).
The goal is to provide the management and project development team with an overview of major tasks to be solved during the planning, design, implementation, and operation phases of computer-integrated machining, inspection, and assembly systems. Financial and technical disasters can be avoided if a clear understanding of the role of sensors and control systems in the computer-integrated manufacturing strategy is asserted.
Sensors are largely applied within the workstations, and are the only practical means of operating a manufacturing system and tracking its performance continuously.
Sensors and control systems in manufacturing provide the means of integrating differ- ent, properly defined processes as input to create the expected output. Input may be raw material and/or data that have to be processed by various auxiliary components such as tools, fixtures, and clamping devices. Sensors provide the feedback data to describe the sta- tus of each process. The output may also be data and/or materials, which can be processed by further cells of the manufacturing system. A flexible manufacturing system that contains workstations, work cells, and work centers and is equipped with appropriate sensors and control systems is a distributed management information system, linking together subsys- tems of machining, packaging, welding, painting, flame cutting, sheet-metal manufactur- ing, inspection, and assembly with material-handling and storage processes.
In designing various workstations, work cells, and work centers in a flexible manufac- turing system within the computer-integrated manufacturing strategy, the basic task is to create a variety of sensors interconnecting different material-handling systems, such as robots, automated guided-vehicle systems, conveyers, and pallet loading and unloading carts, to allow them to communicate with data processing networks for successful integra- tion with the system.
FIGURE I.2 Computer-controlled manufacturing system.
Figure I.4 illustrates a cell consisting of several workstations with its input and output, and indicates its basic functions in performing the conversion process, storing workpieces, linking material-handling systems to other cells, and providing data communication to the control system.
The data processing links enable communication with the databases containing part programs, inspection programs, robot programs, packaging programs, machining data, and real-time control data through suitable sensors. The data processing links also enable com- munication of the feedback data to the upper level of the control hierarchy. Accordingly, the entire work-cell facility is equipped with current data for real-time analysis and for fault recovery.
A cluster of manufacturing cells grouped together for particular production operations is called a work center. Various work centers can be linked together via satellite communica- tion links irrespective of the location of each center. Manufacturing centers can be located several hundred feet apart or several thousand miles apart. Adequate sensors and control systems together with effective communication links will provide practical real-time data analysis for further determination.
The output of the flexible cell is the product of the module of the flexible manufacturing system. It consists of a finished or semifinished part as well as data in a computer-readable format that will instruct the next cell on how to achieve its output requirement. The data are conveyed through the distributed communication networks. If, for example, a part is required to be surfaced to a specific datum in a particular cell, sensors will be adjusted to read the required acceptable datum during the surfacing process. Once the operation is successfully completed, the part must once again be transferred to another cell for further machining or inspection processes. The next cell is not necessarily physically adjacent; it may be the previous cell, for instance, as programmed for the required conversion process.
The primary reason for the emphasis on integrating sensors and control systems into every manufacturing operation is the worldwide exponentially increasing demand for
FIGURE I.3 Workstation, work cell, and work center.
FIGURE I.4 Conversion process in a manufacturing cell.
error-free production operations. Sensors and control technology can achieve impressive results only if effectively integrated with corporate manufacturing strategy.
The following benefits can be achieved:
• Productivity. A greater output and a lower unit cost.
• Quality. Product is more uniform and consistent.
• Production reliability. The intelligent self-correcting sensory and feedback system increases the overall reliability of production.
• Lead time. Parts can be randomly produced in batches of one or in reasonably high num- bers, and the lead time can be reduced by 50 to 75 percent.
• Expenses.Overall capital expenses are 5 to 10 percent lower. The cost of integrating sensors and feedback control systems into the manufacturing source is less than that of stand-alone sensors and feedback systems.
• Greater utilization. Integration is the only available technology with which a machine tool can be utilized as much as 85 percent of the time—and the time spent cutting can also be over 90 percent.
In contrast, a part, from stock to finished item, spends only 5 percent of its time on the machine tool, and actual productive work takes only 30 percent of this 5 percent. The time for useful work on stand-alone machines without integrated sensory and control systems is as little as 1 to 1.5 percent of the time available (see Tables I.1 and I.2).
To achieve the impressive results indicated in Table I.1, the integrated manufacturing system carrying the sensory and control feedback systems must maintain a high degree of flexibility. If any cell breaks down for any reason, the production planning and control system can reroute and reschedule the production or, in other words, reassign the system
TABLE I.1 Time Utilization of Integrated Manufacturing Center Carrying Sensory and Control Systems
Active, % Idle, % Tool positioning and tool changing 25
Machining process 5
Loading and inspection 15
Maintenance 20 Setup 15
Idle time 15
Total 85 15
TABLE I.2 Productivity Losses of Stand-alone Manufacturing Center Excluding Sensory and Control Systems
Active, % Idle, %
Machine tool in wait mode 35
Labor control 35
Support services 15
Machining process 15
Total 15 85
environment. This can be achieved only if both the processes and the routing of parts are programmable. The sensory and control systems will provide instantaneous descriptions of the status of parts to the production and planning system.
If different processes are rigidly integrated into a special-purpose highly productive system, such as a transfer line for large batch production, then neither modular development nor flexible operation is possible.
However, if the cells and their communication links to the outside world are program- mable, much useful feedback data may be gained. Data on tool life, measured dimensions of machined surfaces by in-process gauging and production control, and fault recovery derived from sensors and control systems can enable the manufacturing system to increase its own productivity, learn its own limits, and inform the part programmers of them. The data may also be very useful to the flexible manufacturing system designers for further analysis. In non-real-time control systems, the data cannot usually be collected, except by manual methods, which are time-consuming and unreliable.