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Manufacturer s robot cell control software installed Manufacturer s cell control software installed
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Mechanical Failure
Software Failure 1989
FIGURE 4.4 Frequency of software faults.
Wrong number of hardware units Wrong procedures for writing data to hardware Restriction faults: Omission of procedures to prevent invalid input or output of data Wrong limit value for validity check of arguments Function faults: Omission of saving data to global variables Unnecessary calling modules Wrong limit value for judging whether or not hardware is set Reference to undefined local variables Omission of loop variable incrementation Logic expressions that are always true
Networking of Sensors and Control Systems in Manufacturing
FIGURE 4.5
Program fault categories.
Programming faults: Comparison of local variables of different types Omission of comment marks This categorization provides significant insight into the location of fault conditions, the reasons for their occurrence, and their severity. If the faults are in either a hardware (mechanical) or software category, then the frequency of failure by month can be summarized as indicated in Fig. 4.4. Two major and unexpected milestones in the program represented in Fig. 4.4 are the routine introduction of revised cell control software and revised robot control software. In both cases, a substantial increase occurred in the downtime of the flexible manufacturing cell. In an industrial environment, this would have been very costly. In this study, it was found that interface faults (mismatched data transfer between modules and hardware) were the major cause of
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downtime. Also, in the new machine software, it was found faults occurred because the software had not been properly matched to the number of tool positions physically present on the tool magazine. Once, such a fault actually caused a major collision within the machining volume.
Detecting Tool Failure
An important element in automated process control is real-time detection of cutting tool failure, including both wear and fracture mechanisms. The ability to detect such failures online allows remedial action to be undertaken in a timely fashion, thus ensuring consistently high product quality and preventing potential damage to the process machinery. The preliminary results from a study to investigate the possibility of using vibration signals generated during face milling to detect both progressive (wear) and catastrophic (breakage) tool failure are discussed next.
4.7.8.2.1 Experimental Technique The experimental studies were carried out using a 3-hp vertical milling machine. The cutting tool was a 381-mm-diameter face mill employing three Carboloy TPC-322E grade 370 tungsten carbide cutting inserts. The standard workpiece was a mild steel plate with a length of 305 mm, a height of 152 mm, and a width of 13 mm. While cutting, the mill traversed the length of the workpiece, performing an interrupted symmetric cut. The sensor sensed the vibration generated during the milling process on the workpiece clamp. The vibration signals were recorded for analysis. Inserts with various magnitudes of wear and fracture (ranging from 0.13 mm to 0.78 mm) were used in the experiments. 4.7.8.2.2 The Manufacturing Status of Parts Figure 4.6 shows typical
acceleration versus time histories. Figure 4.7a is the acceleration for three sharp inserts. Note that the engagement of each insert in the workpiece is clearly evident and that all engagements share similar characteristics, although they are by no means identical. Figure 4.7b shows the acceleration for the combination of two sharp inserts and one insert with a 0.39-mm fracture. The sharp inserts produce signals consistent with those shown in Fig. 4.6, while the fractured insert produces a significantly different output. The reduced output level for the fractured insert is a result of the much smaller depth of cut associated with it. It would seem from the time-domain data that use of either an envelope detection or a threshold crossing scheme would provide the ability to automate the detection of tool fracture in a multi-insert milling operation. Figure 4.7 shows typical frequency spectra for various tool conditions. It is immediately apparent that, in general, fracture phenomena are indicated by an increase in the level of spectra components within
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