Understanding And Applying Machine Vision Part 3 potx

Figure 4.23 Early system from Gould Electronics providing two-dimensional vision to guide robot for assembly operation. Figure 4.24 Early system from Machine Vision International provides correction to six degrees of freedom associated with position of car as it installs windshield. Page 61 TABLE 4.6 Machine Vision, Robotic Related Manipulation of Separated Workplaces on Conveyors Bin Picking Manipulation of Manufacturing Process Assembly Workplaces lying Stably on belt Workplaces hung on hooks partially constrained Workpieces completely random spatial organization Workplaces highly organized and separated Workplaces partially organized spatially and unseparated Finishing, sealing, deburring, cutting, process control, flash removal, liquid gasketing In-process Inspection Fastening, spot welding, riveting, arc Welding, bolting, screwing, nailing, gluing, stapling Fitting, parts presentation Mating of parts Several analyses of applications that have been conducted suggest that approximately 42% of the applications relate to inspection (gaging, cosmetic, and verification), 45% to visual servoing location analysis of which robot guidance is only one application, and 13% to part identification and recognition. Significantly, robot vision applications often require systems capable of inspection in addition to guidance. 4.4— Overview of Generic Machine Vision Benefits and Justification The opportunities for machine vision are largely in inspection and assembly operations. Even in the latter case, many of the applications will involve inspection (e.g., of tasks), verification, flaw detection, and so on. In conjunction with such tasks, people are only 70–85% effective, especially when dealing with repetitive tasks. According to researchers at the University of Iowa, people asked to perform the visual sorting task of picking out a minority of black Ping-Pong balls from a production line of white ones allowed 15% of the black balls to escape. Even two operators together were only about 95% effective. Page 62 People have a limited attention span, which makes them susceptible to distractions. People are also inconsistent. Individuals themselves often exhibit different sensitivities during the course of a day or from day to day. Similarly, there are inconsistencies from person to person, from shift to shift, and so on. The eye's response may also be a performance limiter. However, people offer some advantages over machine vision. People are more flexible and can be trained for many tasks. People can make adjustments to compensate for certain conditions that should be ignored. For example, a label inspection system would have to be tolerant of the range of blue saturation that may be permissible. A person can accept anything between pastel yellow and virtually orange if that much of a variance is acceptable. On the other hand, to be tolerant of such a variance, a machine vision system may require its threshold sensitivity be set such that it then accepts labels that are torn. People are also quite capable of interpreting the true nature of a condition and, when trained, can take routine action to correct for a pending process failure. The justification for machine vision need not be based solely on labor displacement. A 1984 Booz-Allen Hamilton study (Duncan and Bowen) cited two elements in the cost of quality: the cost of control and the cost of failure. The essence of the study suggests that one must consider the savings stemming from the cost of failure in any justification equation. The cost of control is generally easy to quantify and includes the prevention and appraisal measures employed in a factory to find defects before products are shipped to customer-inspection and quality control labor costs and inspection equipment. The cost of failure is much more difficult to quantify and includes internal failures resulting in materials scrap and rework and external failures that result in warranty claims, liability, and recall orders as well as the hidden costs (e.g., the loss of customers). Machine vision should be considered wherever the prevention of failure or the reduction of the cost of failure is a priority, which should be throughout manufacturing industries. Machine vision can be the primary means to avoid internal and external failures. For example, use of a machine vision system in a manufacturing process can avoid the production of scrap. Unlike a human inspector who will only detect a reject condition, a machine vision system can spot trends, - trends indicative of incipient conditions that will lead to the production of scrap. Laser gauges, as well as linear array sensors, are available that can make measurements right on or immediately after a machine tool. The dimensional or surface finish data gathered by such systems are used as a guide for readjusting the machine tool or replacing the cutting tool before the machine produces scrap. Many industries have jumped on the statistical process control (SPC) philosophy bandwagon. Trend analysis, frequency distribution, and histogram formats for each of the sensors in a system are used to interpret data and report changes in production quality levels. In many such cases, this kind of data is Page 63 available only the first time from systems that perform 100% inspection. Assessment of the data and its interpretation in the light of corrective action to take to prevent out-of-specification conditions is a process made possible because of the machine vision equipment. Both process control and quality control are possible with machine vision systems. Significantly, avoiding deviations in quality can impact on downstream operations such as assembly. By guaranteeing that every piece is in an acceptable condition, one can avoid schedule upsets or the need to reschedule an operation because only defective parts are available. Among others, the result of process monitoring and trend analysis could be increased machine uptime or improved capital productivity, that is, increased production capacity without additional equipment and associated floor space. Despite the amount of data now available for processing, an ancillary benefit is reduced paperwork since record keeping is automated. Data transfer between a hierarchy of controllers and computers is easily possible. In those cases where rejects are not prevented, machine vision system can possibly be used to detect conditions before value is added. A good example of this can be found in the electronics industry (Figure 4.25). It has been estimated that a fault found on a bare printed circuit board immediately after fabrication only costs $0.25 to repair. Once the board is fully loaded with components, the cost to repair that same bare-board reject condition is estimated at $40 before installation in a piece of equipment or shipment. As can be appreciated, the costs become commensurately higher to effect that same repair with each value-adding step in manufacturing. Figure 4.25 Printed circuit board inspection in electronics industry offers many opportunities for detection of reject conditions before adding value (courtesy of Teradyne/Control Automation). Page 64 Similarly, where rejects are not preventable, separating scrap into that which can be reclaimed from that which cannot is possible with machine vision. In the case of thick-film circuits, for example, the detection of the reject before firing permits the reuse of the substrate. In the case of machined parts, parts that have dimensions that exceed the maximum tolerance limit can generally be reworked, while those that exceed the minimum tolerances cannot. Machine vision systems designed to make measurements on parts can be used to make the distinction, both on-line, as with the previously mentioned laser gage types, and off-line, with television optical comparator analogs. Real-time machine vision techniques can flag conditions and indicate the need for corrective action before a process goes out of specification or at the very least after only a few rejects are experienced. Significant reductions in scrap and rework costs can be achieved from the consistency of flexible automation such as machine vision (Figure 4.26). Figure 4.26 Inex Vision Systems/BWI monitors positioning of labels at rate of 1200 per minute. Page 65 Figure 4.27 Early RVSI/Automatic vision system for weld seam guidance to relax requirements of fixturing. Clearly, machine vision has value although the tangible costs of scrap and rework are often hidden in manufacturing overheads, thus making it difficult to expose the true cost associated with producing a bad product; for example, a percentage of work-in-process inventory might be held pending scrap or rework decisions. Rework, similar to inventory, is subject to shrinkage and to annual carrying costs. Unfortunately, it is difficult to quantify the savings that result from making the product right the first time. Another area to investigate that represents an opportunity for machine vision is one where expensive hard tooling is required to hold a part for an operation (Figure 4.27). This may be avoidable totally or is at least a cheaper flexible fixturing substituted if a machine vision system is used. In this case the system can Page 66 provide location analysis, that is, so-called software fixturing. A key to this requirement is where setup time is lengthy and the amount of time a part is actually being operated on is very small relative to the total cycle time associated with an operation. Significantly, machine vision may offer increased flexibility, especially in assembly operations, that is, flexibility to produce a wider variety of parts with shorter lead times, better response times to changes in designs, and so on. Another area for the use of machine vision is where a high incidence of equipment breakdown (Figure 4.28) is experienced because of such problems as over- and under-size or misshapened and/or warped parts or misoriented parts. A machine vision system upstream of the feeder mechanism can reduce or even eliminate downtime. A situation that definitely warrants a machine vision system is one that involves the inventory of parts because inspection may result in the rejection of a complete batch based on statistical sampling techniques. A 100% inspection assures every part is good so ''just-in-time" inventory can be a by-product, with a corresponding reduction in the material handling and damage experienced by handling. Similarly, machine vision opportunities exist where inspection is a production bottleneck. Figure 4.28 Early Opcon system verifies that label is properly positioned. Labels applied inadvertently to area where flash exists gum up blades of deflashing unit, resulting in equipment downtime. Page 67 As with the justification for robots, one can look for applications related to unhealthy or hazardous environments. The Occupational Safety and Health Administration (OSHA) has expressed concerns about the operator's well being: the noise level is too high, the temperature is too hot, the products are too heavy, and so on. It may be that the environment includes contaminants (metal dust or vapors) that can be injurious to a person. The converse may also be a justification. People may bring contaminants into the environment that can damage the product, for example, dust, causing damage to polished surfaces. Where operation experiences errors due to operator judgment, fatigue, inattentiveness, or oversight brought about because of the dullness of the job, machine vision opportunities exist (Figures 4.29, 4.30, 4.31 and 4.32). Certainly, when an operation is experiencing a capital expansion mode, machine vision should be considered in lieu of alternative, less effective, more costly methods. Where automation is contemplated as a substitute for people, it should be understood that as people are removed, so are their senses, especially sight. When contemplating automation, an analysis is necessary to assure that loss of sight will not affect the production process. Figure 4.29 ORS Automation system inspects magnetic and signature strips of credit cards for blemishes at rate of 400 per minute. Page 68 Figure 4.30 Early system from Vanzetti Vision Systems performs "stranger elimination" function to guarantee all capsules are right ones based on color at rates up to 3600 per minute. Table 4.7 summarizes how to identify potential applications for machine vision. Unquestionably, any operation can identify opportunities for machine vision by performing an introspective examination of its operations. Table 4.8 summarizes the benefits that can accrue; these benefits can be the basis for justifying the purchase of machine vision. The adoption of this technology, with the result of the objective 100% inspection of products, will cut costs, improve quality, reduce warranty repairs, reduce liability claims, and improve consumer satisfaction - all components in an improved profit picture. Table 4.7 Identifying Applications 1. Lowest value added 2. Process control 3. Separate scrap that can be reworked 4. Avoid expensive hard tooling 5. Avoid equipment breakdown 6. Avoid excess inventory 7. Hazardous environment 8. Operator limitations essential Page 69 Figure 4.31 Vision system from Systronic performs on-line inspection of diapers to verify presence of all features. Page 70 Figure 4.32 Vision system from Avalon Imaging verifying empty state of plastic injection molding. Page 71 Table 4.8 Machine Vision Benefits and Justification Summary Economic Motivations To reduce costs of goods manufactured by: (a) Detecting reject state at point of lowest value added (b) Automating to reduce work-in-process inventory (c) Saving on tooling and fixturing costs (d) Being able to separate scrap than can be reclaimed from that which cannot (e) Providing early warning to detect incipient reject state to reduce scrap (f) Reducing scrap and reworking inventory costs (g) Reducing in warranty repairs, both in the field and returned goods (h) Reducing service parts distribution costs (i) Reducing liability costs (j) Reducing liability insurance (k) Improving production yield (l) Reducing direct and indirect labor and burden rate (m) Increasing equipment utilization (n) Reducing setup time (o) Reducing material handling cost and damage (p) Reducing inventory (q) Reducing paper (r) Eliminating schedule upsets Quality Motivations To improve quality by: (a) Conducting 100% inspection versus sample inspection (b) Improving effectiveness of quality check to improve goods shipped and thereby improving customer satisfaction (c) Providing predictability of quality People Motivations (a) Satisfy OSHA (b) Remove from hazardous, unhealthy environment (c) Avoid contaminants in clean room (d) Avoid strenuous task (e) Avoid labor turnover and training costs (f) Avoid need to hire for seasonal work (g) Eliminate monotonous and repetitive job (h) Expedite inspection task that is production bottleneck (i) Reduce need for skilled people (j) Avoid errors due to operator judgment, operator fatigue, operator inattentiveness, and operator oversight (k) Improve skill levels of workers Miscellaneous Motivations (a) (b) (c) (d) (e) (f) (g) Substitute capital for labor in expansion mode Automate record keeping and capture statistics quicker Feedback signals based on trend analysis to control manufacturing process Function as "eyes" for automation Enhance reputation as quality leader Accelerate response to design changes Get new technology into business Page 72 References Birnbaum, J., "Toward the Domestication of Microelectronics," Computer, November 1985. Duncan, L. S., and Bowen, G. L., "Boosting Product Quality for Profit Improvement, "Manufacturing Engineering, Society of Mechanical Engineers, April 1984. Gevarter, W. B., "Machine Vision: A Report on the State of the Art," Computers in Mechanical Engineering, April 1983. Kanade, T., "Visual Sensing and Interpretation: The Image Understanding Point of View," Computers in Mechanical Engineering, April 1983. Lerner, E. J., "Computer Vision Research Looks to the Brain," High Technology, May 1980. Lowe, D. G., "Perceptual Organization and Visual Recognition," National Technical Instrumentation Service Document AD A-150826. [...]... shape and/ or profile of an object or one of its parts, and its distribution in class groups 3 The particular location or orientation of a part in an assembly 4 The determination of the color and/ or shade of an object and/ or of some of its parts 5 The determination of surface conditions of an object, such as finish, polish, texture, dust These are usually unwanted attributes in unpredictable and random... defective conditions internally The glass container industry uses machine vision widely to inspect for sidewall defects, mouth defects and empty bottle states as well as dimensions and shapes In these cases, vision techniques have proven to be able to handle 1800 to 2000 objects per minute 5.2— Deploying Machine Vision How do I know what machine vision techniques are most suitable for my application? A studied... This could be a technique to handle parts that might be overlapping and still be able to make certain decisions associated with those parts even though one can not see them entirely As you can see, there are many vision tools that are available and the specific tools that one requires are applicationdependent 5.1— Who Is Using Machine Vision Today one can find machine- vision- type technology in virtually... reflection and sharp shadows 6.2.4— Practical Sources Sources of light for machine vision vary from the common incandescent lamp to sophisticated lasers One source of light almost never used is ambient light The vagaries of ambient light are typically beyond the capability of state-ofthe-art machine vision systems to handle 6.2.4.1— Incandescent Light Bulb Light (and heat) is obtained from an incandescent... plastic, and textile industries, machine vision techniques are being used to perform an inspection of the integrity of the product being produced Where coatings are applied to such products, machine vision is also being used to guarantee the coverage and quality of coverage In the printing industry, one finds machine vision being used in conjunction with registration Page 79 The food industry finds machine. .. used to analyze the image and make a decision on the basis of the analyzed image and perform an operation accordingly What is typically referred to as the vision engine part of the machine vision system is the combination of image processing, analysis and decision-making techniques that are embodied in the computer A good analogy can be made to a toolbox Virtually all machine vision systems today include... similar to yours and seemed to have been able to handle similar complexities So, for example, if the application is flaw detection - are the type and size flaws similar? Are the part, size and geometric complexity and material similar? Is part positioning similar, etc.? This survey should narrow the number of companies to be solicited to four to six The solicitation package should demand a certain proposal... the machine vision system If a system is needed to locate a part in X and Y to within one inch, the system resolution needs to be less than one inch Unlike contrast, infinite resolution is not always desired An example is measuring the gap of a spark plug Too much resolution would result in an image of the electrode surface appearing mountainous, uneven and pitted, not smooth and flat Lighting and. ..Page 73 5— Machine Vision: Introductory Concepts Machine vision all begin with an image - a picture In many ways the issues associated with a quality image in machine vision are similar to the issues associated with obtaining a quality image in a photograph In the first place, quality lighting... These include: 1 Recruiting 2 Training 3 Scrap rework created while learning a new job 4 Average workers compensation paid for injuries 5 Average educational grant per employee 6 Personnel/payroll department costs per employee Overall the deployment of machine vision will result in improved and predictable quality This in turn will yield improved customer satisfaction and an opportunity to increase market . dullness of the job, machine vision opportunities exist (Figures 4.29, 4 .30 , 4 .31 and 4 .32 ). Certainly, when an operation is experiencing a capital expansion mode, machine vision should be considered. or a part of an assembly. Page 86 2. The general shape and/ or profile of an object or one of its parts, and its distribution in class groups. 3. The particular location or orientation of a part. part is good so ''just-in-time" inventory can be a by-product, with a corresponding reduction in the material handling and damage experienced by handling. Similarly, machine vision