INTRODUCTION
Central to the development of any computer-integrated manufacturing facility is the selec- tion of the appropriate automated manufacturing system and the sensors and control sys- tems to implement it. The degree to which a CIM (computer-integrated manufacturing) configuration can be realized depends on the capabilities and cost of available equipment and the simplicity of information flow.
When designing an error-free manufacturing system, the manufacturing design group must have an appreciation for the functional limits of the automated manufacturing equipment of interest and the ability of the sensors to provide effective information flow, since these param- eters will constrain the range of possible design configurations. Obviously, it is not useful to design a manufacturing facility that cannot be implemented because it exceeds the equipment’s capabilities. It is desirable to match automated manufacturing equipment to the application.
Although sensors and control systems are—by far—less costly than the automated manufac- turing equipment, it is neither useful nor cost-effective to apply the most sophisticated sensors and controls, with the highest performance, to every possible application. Rather, it is impor- tant that the design process determines the preferred parameter values.
The preferred values must be compatible with available equipment and sensors and control systems, and should be those appropriate for the particular factory. The parameters associated with the available equipment and sensors and control systems drive a func- tional process of modeling the manufacturing operation and facility. The parameters deter- mine how the real-world equipment constraints will be incorporated into the functional design process. In turn, as many different functional configurations are considered, the cost-benefit relations of these alternatives can be evaluated and preferred parameter values determined. So long as these preferred values are within the limits of available automated manufacturing equipment and sensory and control systems, the design group is assured that the automated manufacturing equipment can meet its requirements. To the degree that optimum design configurations exceed present equipment capabilities, original equipment manufacturers (OEMs) are motivated to develop new equipment designs and advanced sensors and control systems.
Sensors and control systems, actuators/effectors, controllers, and control loops must be considered in order to appreciate the fundamental limitations associated with manufactur- ing equipment for error-free manufacturing. Many levels of factory automation are associ- ated with manufacturing equipment; the objective at all times should be to choose the levels
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of automation and information flow appropriate for the facility being designed, as revealed through cost-benefit studies. Manufacturing facilities can be designed by describing each manufacturing system—and the sensors and controls to be used in it—by a set of functional parameters. These parameters are:
• The number of product categories for which the automated manufacturing equipment, sen- sors, and control systems can be used (with software downloaded for each product type)
• The mean time between operator interventions (MTOI)
• The mean time of intervention (MTI)
• The percentage yield of product of acceptable quality
• The mean processing time per product
Anideal equipment unit would be infinitely flexible so it could handle any number of cat- egories desired, would require no operator intervention between setup times, would produce only product of acceptable quality, and would have unbounded production capabilities.
The degree to which real equipment containing sensors and control systems can approach this ideal depends on the physical constraints associated with the design and operation of the equipment and the ability to obtain instantaneous information about equipment perfor- mance through sensors and control systems. The performance of the equipment in each of the five parameters stated earlier is related to the details of the equipment’s operation in an error-free environment. Relationships must be developed between the physical description of the equipment’s operation and the functional parameters that will be associated with this operation. The objective is to link together the physical design of the equipment and its functional performance through sensory and control systems in the factory setting.
This concept provides insight into an area in which future manufacturing system improvements would be advantageous, and also suggests the magnitude of the cost-benefit payoffs that might be associated with various equipment designs. It also reveals the opera- tional efficiency of such systems.
An understanding of the relationships between the equipment characteristics and the performance parameters based on sensors and control systems can be used to select the best equipment for the parameter requirements associated with a given factory configuration.
In this way, the manufacturing design team can survey alternative types of available equip- ment and select the units most appropriate for each potential configuration.
NUMBER OF PRODUCTS IN A FLEXIBLE SYSTEM
The first parameter listed earlier—the number of product categories for which the manu- facturing system can be used—represents the key concern in flexible manufacturing. A unit of automated manufacturing equipment is described in terms of the number of product categories for which it can be used with only a software download to distinguish among product types. A completely fixed automated manufacturing system that cannot respond to computer control might be able to accommodate only one product category without a manual setup. On the other hand, a very flexible manufacturing system would be able to accommodate a wide range of product categories with the aid of effective sensors and con- trol systems. This parameter will thus be defined by the breadth of the processes that can be performed by an automated manufacturing equipment unit and the ability of the unit to respond to external control data to shift among these operations.
The most effective solution will depend on the factory configuration that is of interest.
Thus, OEMs are always concerned with anticipating future types of factories in order to ensure that their equipment will be an optimum match to the intended configuration. This
will also ensure that the concept of error-free manufacturing can be implemented with a high degree of spontaneity. There is a continual trade-off between flexibility and cost. In general, more flexible and “smarter” manufacturing equipment will cost more. Therefore, the objective in a particular setting will be to achieve just the required amount of flexibility, without any extra capability built into the equipment unit.
SENSORS TRACKING THE MEAN TIME BETWEEN OPERATOR INTERVENTIONS
The MTOI value should be matched to the factory configuration in use. In a highly manual operation, it may be acceptable to have an operator intervene frequently. On the other hand, if the objective is to achieve operator-independent manufacturing between manual setups, then the equipment must be designed so the MTOI is longer than the planned duration between manual setups. The manufacturer of automated equipment with adequate sensors and control systems must try to assess the ways in which factories will be configured and produce equipment that can satisfy manufacturing needs without incurring any extra cost due to needed features.
SENSORS TRACKING THE MEAN TIME OF INTERVENTION
Each time an intervention is required, it is desirable to compare the intervention interval with that for which the system was designed. If the intervention time becomes large with respect to the planned mean time between operator interventions, then the efficiency of the automated manufacturing equipment drops rapidly in terms of the fraction of time it is available to manufacture the desired product.
SENSORS TRACKING YIELD
In a competitive environment, it is essential that all automated manufacturing equipment emphasize the production of quality product. If the automated manufacturing equipment produces a large quantity of product that must be either discarded or reworked, then the operation of the factory is strongly affected, and costs will increase rapidly. The objective, then, is to determine the product yields required for given configurations and to design auto- mated manufacturing equipment containing sensors and control systems that can achieve these levels of yield. Achieving higher yield levels will, in general, require additional sens- ing and adaptability features for the equipment. These features will enable the equipment to adjust and monitor itself and, if it gets out of alignment, to discontinue operation.
SENSORS TRACKING THE MEAN PROCESSING TIME
If more product units can be completed in a given time, the cost of automated manufac- turing equipment with sensors and control systems can be more widely amortized. As the mean processing time is reduced, the equipment can produce more product units in a given time, reducing the manufacturing cost per unit. Again, automated manufacturing
equipment containing sensory and control systems generally becomes more expensive as the processing time is reduced. Tradeoffs are generally necessary among improve- ments in the five parameters and the cost of equipment. If high-performance equipment is to be employed, the factory configuration must make effective use of the equipment’s capabilities to justify its higher cost. On the other hand, if the factory configuration does not require the highest parameters, then it is far more cost-effective to choose equipment units that are less sophisticated but adequate for the purposes of the facility. This interplay between parameter values and equipment design and cost is an essential aspect of system design.
Table 3.1 illustrates the difference between available parameter values and optimum parameter values, where the subscripts for equipment E represent increasing levels of com- plexity. The table shows the type of data that can be collected to evaluate cost and benefits.
These data have a significant impact on system design and performance which, in turn, has a direct impact on product cost. Given the type of information in Table 3.1, system designers can evaluate the effects of utilizing various levels of sensors and control systems on new equipment and whether they improve performance enough to be worth the research and development and production investment.
One of the difficulties associated with manufacturing strategies in the United States is that many companies procure manufacturing equipment only from commercial vendors and do not consider modifying it to suit their own needs. Custom modification can produce pivotal manufacturing advantage, but also requires the company to expand both its plan- ning scope and product development skills. The type of analysis indicated in Table 3.1 may enable an organization to determine the value and return on investment of custom- izing manufacturing equipment to incorporate advanced sensors and control systems.
Alternatively, enterprises with limited research and development resources may decide to contract for development of the optimum equipment in such a way that the sponsor retains proprietary rights for a period of time.
TABLE 3.1 Values of Available Parameters
R&D Equipment
expense, cost,
MTOI, MTI, Process, thousands thousands
Equipment min min Yield, % min of dollars of dollars Production function A
E1 0.1 0.1 90 12 50
E2 1.0 0.1 85 8 75
E3 10 1.0 80 10 85
E4 18 1.0 90 8 280 155
Production function B
E1 1.0 0.1 95 10 150
E2 10 0.5 90 2 300
Production function C
E1 0.1 0.1 98 3 125
E2 5.0 1.0 98 2 250
E3 8.0 2.0 96 1 300
E4 20 2.0 96 1 540 400
NETWORK OF SENSORS DETECTING MACHINERY FAULTS
A comprehensive detection system for automated manufacturing equipment must be seri- ously considered as part of the manufacturing strategy. A major component of any effort to develop an intelligent and flexible automatic manufacturing system is the concurrent devel- opment of automated diagnostic systems, with a network of sensors, to handle machinery maintenance and process control functions. This will undoubtedly lead to significant gains in productivity and product quality. Sensors and control systems are one of the enabling technologies for the “lights-out” factory of the future.
A flexible manufacturing system often contains a variety of manufacturing work cells.
Each work cell in turn consists of various workstations. The flexible manufacturing cell may consist of a CNC lathe or mill whose capabilities are extended by a robotic handling device, thus creating a highly flexible machining cell whose functions are coordinated by its own computer. In most cases, the cell robot exchanges workpieces, tools (including chucks), and even its own gripping jaws in the cell (Fig. 3.1).
Diagnostic Systems
A diagnostic system generally relies on copious amounts of a priori and a posteriori information.A priori information is any previously established fact or relationship that the system can exploit in making a diagnosis. A posteriori information is the information concerning the problem at hand for which the diagnosis will be made. The first step in collecting data is to use sensors and transducers to convert physical states into electrical signals. After processing, a signal will be in an appropriate form for analysis (perhaps as
FIGURE 3.1 Flexible machining cell.
a table of values, a time-domain waveform, or a frequency spectrum). Then the analysis, including correlations with other data and trending, can proceed.
After the data have been distilled into information, the deductive process begins, lead- ing finally to the fault diagnosis. Expert systems have been used effectively for diagnostic efforts, with the diagnostic system presenting either a single diagnosis or a set of possibili- ties with their respective probabilities, based on the a priori and a posteriori information.
Resonance and Vibration Analysis
Resonance and vibration analysis is a proven method for diagnosing deteriorating machine elements in steady-state process equipment such as turbomachinery and fans. The effec- tiveness of resonance and vibration analysis in diagnosing faults in machinery operating at variable speed is not proved, but additional study has indicated good potential for its application in robots. One difficulty with resonance and vibration analysis is the attenua- tion of the signal as it travels through a structure on the way to the sensors and transducers.
Moreover, all motion of the machine contributes to the motion measured by the sensors and transducers, so sensors and transducers must be located as close as possible to the compo- nent of concern to maximize the signal-to-noise ratio.
Sensing Motor Current for Signature Analysis
Electric motors generate back electromotive force (emf ) when subjected to mechanical load. This property makes a motor a transducer for measuring load vibrations via current fluctuations. Motor current signature analysis uses many of the same techniques as vibra- tion analysis for interpreting the signals. But motor current signature analysis is nonin- trusive because motor current can be measured anywhere along the motor power cables, whereas a vibration sensor or transducer must be mounted close to the machine element of interest. The limited bandwidth of the signals associated with motor drive signature analy- sis, however, may restrict its applicability.
Acoustics
A good operator can tell from the noise that a machine makes whether a fault is devel- oping or not. It is natural to extend this concept to automatic diagnosis. The operator, obviously, has access to subtle, innate pattern recognition techniques, and thus is able to discern sounds within myriad background noises. Any diagnostic system based on sound would have to be able to identify damage-related sounds and separate the information from the ambient noise. Acoustic sensing (looking for sounds that indicate faults) is a nonlocal noncontact inspection method. Any acoustic technique is subject to outside disturbances, but is potentially a very powerful tool, provided that operating conditions are acoustically repeatable and that the diagnostic system can effectively recognize acoustic patterns.
Temperature
Using temperature as a measurement parameter is common, particularly for equipment run- ning at high speed, where faults cause enough waste heat to raise temperature significantly.
This method is generally best for indicating that a fault has occurred, rather than the precise nature of the fault.
Sensors for Diagnostic Systems
Assuming an automated diagnostic system is required, the necessary sensors are normally mounted permanently at their monitoring sites. This works well if data are required con- tinuously, or if there are only a few monitoring locations. However, for those cases where many sites must be monitored and the data need not be continuously received during opera- tion of the flexible manufacturing cell, it may be possible to use the same sensor or trans- ducer, sequentially, in the many locations.
The robot is well-suited to gather data at multiple points with a limited number of sen- sors and transducers. This would extend the mandate of the robot from simply moving workpieces and tools within the flexible manufacturing cell (for production) to include moving sensors (for diagnostic. inspection).
Within the flexible manufacturing cell, a robot can make measurements at sites inside its work space by taking a sensor or transducer from a tool magazine, delivering the sensor or transducer to a sensing location, detaching it during data collection, and then retrieving it before moving to the next sensing position.
Sensor mobility does, however, add some problems. First, the robot will not be able to reach all points within the flexible manufacturing cell because its work space is only a subspace of the volume taken up by the flexible manufacturing cell. The manipulator may be unable to assume an orientation desired for a measurement even inside its work space.
Also, the inspection procedure must limit the robot’s influence on the measurement as much as possible. Finally, sensors require connectors on the robot end effectors for signals and power. The end effector would have to be able to accommodate all the types of sensors to be mounted on it.
Quantifying the Quality of a Workpiece
If workpiece quality can be quantified, then quality can become a process variable. Any system using product quality as a measure of its performance needs tight error checks so as not to discard product unnecessarily while the flexible manufacturing cell adjusts its operat- ing parameters. Such a system would depend heavily, at first, on the continued supervision of an operator who remains in the loop to assess product quality. Since it is forbidden for the operator to influence the process while it is under automatic control, it is more realistic for the operator to look for damage to product after each stage of manufacture within the cell.
In that way, the flexible manufacturing cell receives diagnostic information about product deficiencies close to the time that improper manufacture occurred.
In the future, these quality assessments will be handled by the flexible manufacturing cell itself, using sensors and diagnostic information for process control. Robots, too, will be used for maintenance and physical inspection as a part of the regular operation of the flexible manu- facturing cell. In the near term, the flexible manufacturing cell robot may be used as a sensor- transfer device, replacing inspectors who would otherwise apply sensors to collect data.
Evaluation of an Existing Flexible Manufacturing Cell Using a Sensing Network
A study was conducted at the Mi-TNO in the Netherlands of flexible manufacturing cells for low-volume orders (often called job production, ranging from 1 to 100 parts per order).
The automated manufacturing equipment used in the study consisted of two free-standing flexible manufacturing cells. The first cell was a turning-machine cell; the second, a milling- machine cell. The turning cell contained two armed gantry robots for material handling. The study was mainly conducted to assess the diagnostics for flexible manufacturing systems