Current manufacturing strategy defines manufacturing systems in terms of sensors, actuators, effectors, controllers, and control loops. Sensors provide a means for gathering information on manufacturing operations and processes being performed. In many instances, sensors are used to transform a physical stimulus into an electrical signal that may be analyzed by the manufacturing system and used for making decisions about the operations being conducted. Actuators convert an electrical signal into a mechanical motion. An actuator acts on the product and equipment through an effector. Effectors serve as the "hand" that achieves the desired mechanical action. Controllers are computers of some type that receive information from sensors and from internal programming, and use this information to operate the manufacturing equipment (to the extent available, depending on the degree of automation and control). Controllers provide electronic commands that convert an electrical signal into a mechanical action. Sensors, actuators, effectors, and controllers are linked to a control loop.
In limited-capability control loops, little information is gathered, little decision making can take place, and limited action results. In other settings, "smart" manufacturing equipment with a wide range of sensor types can apply numerous actuators and effectors to achieve a wide range of automated actions.
The purpose of sensors is to inspect work in progress, to monitor the work-in-progress interface with the manufacturing equipment, and to allow self-monitoring of manufacturing by the manufacturing system's own computer. The purpose of the actuator and effector is to transform the work in progress according to the defined processes of the manufacturing system. The function of the controller is to allow for varying degrees of manual, semi-automated, or fully automated control over the processes. In a fully automated case, such as in computer-integrated manufacturing, the controller is completely adaptive and functions in a closed-loop manner to produce automatic system operation. In other cases, human activity is involved in the control loop.
In order to understand the ways in which the physical properties of a manufacturing system affect the functional parameters associated with the manufacturing system, and so as to determine the types of physical manufacturing system properties necessary to implement the various desired functional parameters, it is necessary to understand the technologies available for manufacturing systems that use automation and integration to varying degrees.
The least automated equipment makes use of detailed operator control over all equipment functions. Further, each action performed by the equipment is individually directed by the operator. Manual equipment thus makes the maximum use of human capability and adaptability. Visual observations can be enhanced by the use of microscopes and cameras, and the actions undertaken can be improved through the use of simple effectors. The linkages between the sensory information (from microscopes or through cameras) and the resulting actions are obtained by placing the operator in the loop.
This type of system is clearly limited by the kinds of sensors used and their relationship to the human operator, the types of effectors that can be employed in conjunction with the human operator, and the capabilities of the operator. The manufacturing equipment that is designed for a manual strategy must be matched to human capabilities. The human-manufacturing equipment interface is extremely important in many manufacturing applications. Unfortunately, equipment design is often not optimized as a sensor-operator-actuator/effector control loop.
A manufacturing system may be semi-automated, with some portion of the control loop replaced by a computer. This approach will serve the new demands on manufacturing system design requirements. Specifically, sensors now must provide continuous input data for both the operator and computer. The appropriate types of data must be provided in a timely manner to each of these control loops. Semi-automated manufacturing systems must have the capability for a limited degree of self-monitoring and control associated with the computer portion of the decision-making loop. An obvious difficulty in designing such equipment is to manage the computer- and operator-controlled activities in an optimum manner. The computer must be able to recognize when it needs operator support, and the operator must be able to recognize which functions may appropriately be left to computer control. A continuing machine-operator interaction is part of normal operations.
Another manufacturing concept involves fully automated manufacturing systems. The processing within the manufacturing system itself is fully computer-controlled. Closed-loop operations must exist between sensors and actuators/effectors in the manufacturing system. The manufacturing system must be able to monitor its own performance and decision making for all required operations. For effective automated operation, the mean time between operator interventions must be large when compared with the times between manufacturing setups.
The processes in use must rarely fail; the operator will intervene only when such failures occur. In such a setting, the operator's function is to ensure the adequate flow of work in progress and respond to system failure.
Several types of work cells are designed according to the concept of total manufacturing integration. The most sophisticated cell design involves fully automated processing and materials handling. Computers control the feeding of work in progress, the performance of the manufacturing process, and the removal of the work in progress. Manufacturing systems of this type provide the opportunity for the most advanced automated and integrated operations. The manufacturing system must be modified to achieve closed-loop operations for all of these functions.
Most manufacturing systems in use today are not very resourceful. They do not make use of external sensors that enable them to monitor their own performance. Rather, they depend on internal conditioning sensors to feed back (to the control system) information regarding manipulator positions and actions. To be effective, this type of manufacturing system must have a rigid structure and be able to determine its own position based on internal data (largely independent of the load that is applied). This leads to large, heavy, and rigid structures.
The more intelligent manufacturing systems use sensors that enable them to observe work in progress and a control loop that allows corrective action to be taken. Thus, such manufacturing systems do not have to be as rigid because they can adapt.
The evolution toward more intelligent and adaptive manufacturing systems has been slow, partly because the required technologies have evolved only in recent years and partly because it is difficult to design work cells that effectively use the adaptive capabilities. Enterprises are not sure whether such features are cost-effective and wonder how to integrate smart manufacturing systems into the overall strategy.
The emphasis must be on the building-block elements necessary for many types of processing. If the most advanced sensors are combined with the most advanced manufacturing systems, concepts, and state-of-the-art controllers and control loops, very sophisticated manufacturing systems can result. On the other hand, much more rudimentary sensors, effectors, and controllers can produce simple types of actions.
In many instances today, sensors are analog (they involve a continuously changing output property), and control loops make use of digital computers. Therefore, an analog-to-digital converter between the preprocessor and the digital control loop is often required.
The sensor may operate either passively or actively. In the passive case, the physical stimulus is available in the environment and does not have to be provided. For an active case, the particular physical stimulus must be provided. Machine vision and color identification sensors are an active means of sensing, because visible light must be used to illuminate the object before a physical stimulus can be received by the sensor. Laser sensors are also active-type sensors. Passive sensors include infrared devices (the physical stimulus being generated from infrared radiation that is associated with the temperature of a body) and sensors to measure pressure, flow, temperature, displacement, proximity, humidity, and other physical parameters.
SENSORS IN MANUFACTURING
Many types of sensors have been developed during the past several years, especially those for industrial process control, military uses, medicine, automotive applications, and avionics. Several types of sensors are already being manufactured by commercial companies.
Process control sensors in manufacturing will play a significant role in improving productivity, qualitatively and quantitatively, throughout the coming decades. The main parameters to be measured and controlled in industrial plants are temperature, displacement, force, pressure, fluid level, and flow. In addition, detectors for leakage of explosives or combustible gases and oils are important for accident prevention.
Optical-fiber sensors may be conveniently divided into two groups: (1) intrinsic sensors and (2) extrinsic sensors.
Although intrinsic sensors have, in many cases, an advantage of higher sensitivity, almost all sensors used in process control at present belong to the extrinsic type. Extrinsic type sensors employ light sources such as LEDs, which have higher reliability, longer life, and lower cost than semiconductor lasers. They also are compatible with multimode fibers, which provide higher efficiency when coupled to light sources and are less sensitive to external mechanical and thermal disturbances.
As described in Chap. 1, objects can be detected by interrupting the sensor beam. Optical-fiber interrupters are sensors for which the principal function is the detection of moving objects. They may be classified into two types: reflection and transmission.
In the reflection-type sensor, the light beam emitted from the fiber is reflected back into the same fiber if the object is situated in front of the sensor.
In the transmission-type sensor, the emitted light from the input fiber is interrupted by the object, resulting in no received light in the output fiber located at the opposite side. Typical obstacle interrupters employ low-cost large-core plastic fibers because of the short transmission distance. The minimum detectable size of the object is typically limited to 1 mm by the fiber core diameter and the optical beam. The operating temperature range of commercially available sensors is typically -40 to +70°C. Optical-fiber sensors have been utilized in industry in many ways, such as:
• Detection of lot number and expiration dates (for example, in the pharmaceutical and food industries)
• Color difference recognition (for example, colored objects on a conveyer)
• Defect detection (for example, missing wire leads in electronic components)
• Counting discrete components (for example, bottles or cans)
• Detecting the absence or presence of labels (for example, packaging in the pharmaceutical and food industries)
Fiber-optic sensors for monitoring process variables such as temperature, pressure, flow, and liquid level are also classified into two types: (1) the normally OFF type in which the shutter is inserted between the fibers in the unactivated state-thus, this type of sensor provides high and low levels as the light output corresponds to ON and OFF states, respectively; and (2) the normally ON type, where the shutter is retracted from the gap in the unactivated state.
In both types, the shutter is adjusted so it does not intercept the light beam completely but allows a small amount of light to be transmitted, even when fully closed. This transmitted light is used to monitor the cable (fiber) continuity for faults and provides an intermediate state. Commercially available sensors employ fibers of 200-_m core diameter. The typical differential attenuation that determines the ON-OFF contrast ratio is about 20 dB. According to manufacturers' specifications, these sensors operate well over the temperature range -40 to +80°C with a 2-dB variation in light output.
About the Author
Sabrie Soloman, Ph.D, is the Founder, Chairman and CEO of American SensoRx, Inc.
Excerpted from Sensors Handbook, 2nd Edition by Sabrie Soloman (McGraw-Hill; 2010) with permission by McGraw-Hill.
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