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Few things damage the financial stability of a manufacturing facility more than unexpected downtime. Predictive maintenance aims to get the maximum life out of\u00a0equipment while minimizing the risk of failure. It ensures that parts are used to the end of their natural life, but risks downtime due to delays in delivery of replacements or additional costs due to higher stock levels.<\/p>\n
On average, manufacturers suffer around 30 percent downtime during their scheduled production. In some industries, such as automotive assembly, downtime can cost up to $20,000 per minute (a mind-blowing $1.2 million per hour).<\/p>\n
Traditionally, maintenance has been carried out according to a fixed schedule, replacing items at constant intervals. But for all kinds of reasons, parts wear out at different rates, so constant intervals do not take into consideration the varying circumstances of the machine being maintained. Therefore, although general purpose schedules are easy to manage by arranging for spares to be delivered in a predictable manner, they risk wasting money by replacing parts that are still operational.<\/p>\n
Prospector can help speed along your research with technical datasheets and access to global equipment suppliers.<\/p>\n
Extrusion is a “black-box” process, so monitoring what is happening inside the extruder depends on instruments. Typical extrusion problems fall into a few main categories: aesthetic flaws (pits, black specs, pinholes, drag marks, die lines, sink marks); size variance; and dimensional variations. Table 1<\/a> lists common faults and possible causes.<\/p>\n Usually tooling, once “tuned-in” and properly maintained, will not be the root of the problem. Extruder screws and barrels will wear over time, and it depends on many factors, so they can last for several years or only several weeks. It is important to measure the internal dimensions on a regular basis, so that the life of the screw and barrel can be predicted.<\/p>\n Key process parameters which can be monitored include:<\/p>\n Instruments look for variances in:<\/p>\n Other process variables may be monitored upstream on devices such as dryers, blenders, conveyors, and feeders, and on downstream devices, such as gear pumps, screen changers, calibrators, water troughs, laser gauges, pullers, and winders.<\/p>\n This makes for a perfect arena in which to apply condition monitoring and predictive maintenance principles. The more data obtained over time, the more confidence there can be in the predictions made. The instruments used to monitor all these parameters should also be calibrated regularly so readings do not drift over time.<\/p>\n Of course, there is no substitute for people who understand the process, so training courses covering material characteristics and machinery features are invaluable (even though many operators rely solely on on-the-job training).<\/p>\n In addition to monitoring parameters, there are singular events which might have caused a problem, such as a power outage, change of screw or new resin batch. Some potentially important events are less obvious, such as activity in that area of the plant, changes in materials handling methods, changes to the water system, replacement of lubricating oil, or the start of a new operator. In addition, not all events have an immediate effect and become noticeable over time.<\/p>\n The performance of an extruder is determined as much by the characteristics of the feedstock as by the characteristics of the machine. If a material problem is suspected, the engineer should first look for any record of a change in feedstock properties. Often, the only QC test on incoming material is a melt index (MI) test. This detects only a very limited number of material-related extrusion problems. In many cases, more extensive material testing may be required. Feedstock properties that affect the extrusion process include bulk flow properties, melt flow properties and thermal properties. See Table 2 for further information.<\/p>\n <\/p>\n In addition, resin storage needs to be in a clean, dry area, without extreme temperature variation. Because batch changes can result in undesirable variations, some processors measure the material (density, melt index, shear rate vs. viscosity data, and tensile strength). If the material is too dry, it may not melt and the resin cannot be processed. If a batch change is unavoidable, it is important to monitor machine variables more closely, in order to make adjustments as needed to maintain product quality.<\/p>\n Reference<\/strong>:\n
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Feedstock Issues<\/strong><\/h3>\n
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\n Table 2 Feedstock properties affecting extrusion process<\/strong><\/td>\n<\/tr>\n \n Polymer material data is dependent on temperature and pressure, as well as surface roughness, sliding velocity, granularity, shear rate, strain rate and time.<\/td>\n<\/tr>\n \n Bulk flow properties<\/strong><\/td>\n bulk density<\/td>\n<\/tr>\n \n <\/td>\n compressibility<\/td>\n<\/tr>\n \n <\/td>\n particle size<\/td>\n<\/tr>\n \n <\/td>\n particle shape<\/td>\n<\/tr>\n \n <\/td>\n friction<\/td>\n<\/tr>\n \n <\/td>\n tendency to agglomeration<\/td>\n<\/tr>\n \n Melt flow properties<\/strong><\/td>\n shear and elongational viscosities as a function of strain rate and temperature<\/td>\n<\/tr>\n \n Thermal properties<\/strong><\/td>\n specific heat<\/td>\n<\/tr>\n \n <\/td>\n glass transition temperature<\/td>\n<\/tr>\n \n <\/td>\n crystalline melting point<\/td>\n<\/tr>\n \n <\/td>\n latent heat of fusion<\/td>\n<\/tr>\n \n <\/td>\n thermal conductivity<\/td>\n<\/tr>\n \n <\/td>\n density<\/td>\n<\/tr>\n \n <\/td>\n degradation temperature<\/td>\n<\/tr>\n \n <\/td>\n induction time as a function of temperature<\/td>\n<\/tr>\n \n Thermal material data of polymers differ strongly depending on whether the polymer is an amorphous or a semicrystalline material.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\nExtrusion Troubleshooter<\/a>,\u00a0Rauwendaal, Chris, Rauwendaal Extrusion Engineering Inc.<\/p>\n","protected":false},"excerpt":{"rendered":"