how to create barcode in vb.net 2008 Quantifying the Quality of a Workpiece in Software

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Quantifying the Quality of a Workpiece
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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 operating 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 part of the regular operation of the flexible manufacturing 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.
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Evaluation of an Existing Flexible Manufacturing Cell Using a Sensing Network
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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 (FMS). In considering the approach to setting up diagnostics for
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an FMS, it was decided to divide development of the diagnostics program into three major blocks (Fig. 4.2): Analyzing the existing design Setting up diagnostics that are machine-based Choosing important points in the flexible manufacturing cells where critical failure can occur and where sensors are mounted Setting up a diagnostic decision system for the hardware system Establishing a workpiece-based diagnostic system that is actually part of quality control The flexible manufacturing cells were divided into control volumes (as shown in Fig. 4.3 for the turning cell). For the turning cell,
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FIGURE 4.2 Diagnostics for an FMS.
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FIGURE 4.3 Hardware control volumes in a manufacturing cell.
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S/W control S/W control S/W control S/W control volume volume volume volume p4 p3 p2 p1
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Control volume identification
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Networking of Sensors and Control Systems in Manufacturing
for example, the hardware control volumes were denoted a, b, c, d, and e, and software control volumes p1, p2, p3, and p4. The failures within each of these control volumes were further categorized as: Fault detected by an existing sensor Fault that could have been detected if a sensor had been present Operator learning phase problem Failure due to manufacturer problem Software logic problem Repeat of a problem System down for an extended period
Software Problems
It is often assumed that disturbances in the cell software control system can be detected and evaluated relatively easily. Software diagnostics are common in most turnkey operations; however, it has been shown that software diagnostics are far from perfect. Indeed, software problems are of particular concern when a revised program is introduced into a cell that has been running smoothly (existing bugs having been ironed out). The availability of the cell plummets in these situations, with considerable loss of production capabilities and a concomitant higher cost. The frequency of software faults increases dramatically when revised packages are introduced to upgrade the system (Fig. 4.4). This is mainly due to human error on the part of either the vendor or the operator. Two possible approaches to software defect prevention and detection are: Investigate software methodologies and procedures and recommend alternative languages or more tools as defect prevention measurements. This is tedious and uses ambiguous results because such investigations are not based on data. Analyze the problems that result from the current design and develop a solution for each class of problem. This produces less ambiguous solutions and is typically used to solve only immediate problems, thereby producing only short-term solutions. To identify the types of faults that occur in programs, it is necessary to know what caused the problem and what remedial actions were taken. Program faults can be subdivided into the categories shown next and restated in Fig. 4.5. Matching faults: Wrong names of global variables or constants Wrong type of structure or module arguments
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