Herstrom Carolyn Grantham Cheryl L. Allen P. Daniel Sydow Eric G. Cooper Cuang C. Quach Marion A. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Fisher and C. Rhodes, R. Will, and M. Wu, R. Adams, and M. Albus, H. McCain, and R. The following are some recomrnertda. This principle can be realized during de- sign by combining functions within th. The use of plastic molded parts to substitute for sheet metal parts may be a way to actualize this principle. A more complex geom- etry molded into a plastic part might replace several metal parts. Although the plas- tic part may seem to be more costly, the savings in assembly time will justify the substitution in many cases.
In automated assembly, increasing the number of separate as- sembly steps accomplished by a single automated system results in a decrease in sys- tem reliability. This is demonstrated in our analysis of assembly system performance in Sections To reduce this effect Riley [13] suggests that the design of the product be modular with perhaps each module requiring a maximum of 12 or so parts to be assembled on a single assembly system.
Also, the subassembly should be designed around a base part to which other components are added [14]. The preferred prac tice in automated assembly machine design is 10 separate the operations at different stations rather than to simultaneously handle and fasten multiple components at the same workstation.
This principle simply means that the number of directions in which new components are added to the existing subassembly should be minimized. If all of the components can be added vertically from above, this is the ideal situation.
Obviously the design of the subassembly determines this. High performance of the automated assembly system requires consistently good quality of the components added at each work- station. Poor quality components cause jams in the feeding and assembly rnecha- nisms, which cause downtime in an automated system. This is a term thai Rile ' [13] uses 10 identity the case with which a given component can be fed and oriented reliably for delivery from the parts hopper to the assembly workhead.
One of the major costs in the development of an auto- mated assembly system is the engineering time to devise the means of feeding the components in the correct orientation for the assembly operation. In this section, we develop models to analyze the following issues in auto- mated assembly: 1 parts delivery system at workstations, 2 multi-station automated assembly systems, 3 single station automated assembly systems, and 4 partial automation, These parts are assumed to be randomly oriented initially, and must be presented to the selector or orientor to establish the correct orientation.
In the case of a selector, a certain proportion of the parts will be correctly ori- ented initially, and these will be allowed to pass through. The remaining proportion that is incorrectly oriented will be rejected back into the hopper. In the case of an orientor, in- correctly oriented parts will be reoriented.
In many delivery system designs, the functions of the selector and the orientor are combined. Let us define 1 to be the proportion of components that pass through the selector-orientor process and are correctly oriented for delivery into the feed track.
Hence, the effective rate of delivery of components from the hopper into the feed track is to. The remaining proportion, 1 - 0 , is recirculated back into the hopper. Obviously, the delivery rate to of components to the workhead must be sufficient to keep up with the cycle rate of the assembly machine Assuming the delivery rate of components to is greater than the cycle rate R, of the assembly machine.
Quantitative Analysis of Assembly Systems tablished. This sensor is referred to as the high teret sensor. The length of the com- ponents must be measured from a point on a given component to the corresponding poin! The value of nf2 is the capacity of the feed track Another sensor is placed along the feed track at some distance from the first sensor and is used to restart the feeding mechanism again.
Accordingly, the value of nfl must be made large enough to virtually eliminate the probability of a stock- out after the Jaw level sensor has turned on the feeder. The rate of depletion of parts in the feed track, start. Some modifications in the analysis must be made 10 account lor the fact that components are being added at the various workstations in the assembly system.
The gen- eral operation of the assembly system is pictured in Figure J9. The existing assembly consists of a base part plus the components assembled to it at previous stations. The base pari is launched onto the line either at or before the first workstation. The components that are added must be clean, uniform in size and shape, of high quality, and consistently oriented. When the feed mechanism and assembly workhead attempt to join a component that does not satisfy this technical description, the station can jam.
When a jam occurs, it results in the shutdown of the entire system until the fault is corrected. Thus, in addition to the other mechanical and electrical failures that interrupt the opera. This is the problem we propose to deal with in this Section. Defective parts occur in manu- facturing with a certain fraction defect rate, q O :s q s, 1.
In the operation of an as- sembly workstation, q can be considered to be the probability that the component to be added during the current cycle is defective.
When an attempt is made to feed and assem- ble a defective component. Consider what happens at a particular workstation, say station i, where there are three possible events that might occur when the feed mechanism attempts to feed the next component, and the assembly device attempts to join it to the existing assembly at the sta- tion.
The three events and their associated probabilities are: 1. The component is defective and causes a suuton jem, The probability of this event is the fraction defect rate of the parts at the station q.. This product is the same term p, in our previous analysis of transfer machines in Section When the station jams, the component must then be cleared and the next component be allowed to feed and be assembled. We assume thai if the next component in the feed track were defective, the operator who cleared the previous jam would notice and remove this next defect as well.
The component is defective but does not cause a station jam. This has a probability 1 - m. With this outcome, a bad part is joined to the existing assembly, perhaps rendering the entire assembly defective.
The component is not defective. This is the most desirable outcome and the most likely by far hopefully. The probability that a part added at the station is not de- fective is equal to the proportion of good parts 1 - q.. Unfortunately, the number of terms in the expansion becomes very large for a machine with more than two or three stations.
Measures of Performance. One or the characteristics of performance that we want to know is the proportion of assemblies that contain one or more defective components. Two of the three terms in Eq. The first term is lIliQ;, which indicates that a station jam has occurred, and thus a defective component has not been added to the existing assembly. The other term is 1 - qi. The sum of these two terms represents the proh- ahility that a defective component is not added at station i. Multiplying these prohabilities for all stations.
The proportion of assemblies with one or more defective components P qp must be considered a significant disadvantage of the machine's performance, Either these assem- blies must be identified through an inspection process and possibly repaired, or they will become mixed in with the good assemblies. This latter possibility leads to undesirable con- sequences when the assemblies are placed in service. Other performance measures of interest are the machine's production rate, propor- tion of uptime and downtime, and average cost per unit produced.
To calculate production rate, we must first determine the frequency of downtime occurrences per cycle F. For the case of equal m, and qj, How- ever, the operation of assembly machines is different from processing machines. Accordingly, the production rate should he corrected to give the rate of acceptable product. When all mi arc equal and all 4. Line efficiency is calculated as the ratio of ideal cycle time to average actual pro- duction time, This is the same ratio as we defined in Chapter 17 Eq.
No attempt has been made to correct line efficiency E for the yield of good as- sernblies, We are treating assembly machineefficiency and the quality of units produced on it as separateissues.
On the other hand, the cost per assembled product must take account of the output quality. Therefore, the general cost formula given in Eq. The effect of the denominator is to increase the cost per assembly: as the quality of the individual components deteriorates, the average cost per good quality assembly increases In addition to the traditional ways of indicating line performance production rate. The base part is automatically loaded prior to the first station, and components are added at each of the stations.
Other costs are ignored. SQlution: Computations similar to those in Example The effect of component quality, as indicated in the value 01 q, is predictable, As fraction defect rate increases, meaning that component quality gets worse, all measures of performance suffer. Production rate drops. The effect of m the probability that a defect will jam the workhcad and cause the as- serribly machine to stop is less obvious.
Instead of interrupting the assembly machine operation and causing downtime, all defective components pass through the assembly process to become part of the final product. Memory control is particularly appropriate for automated assembly machine operation. With memory control, the assembly machine IS provided with logic that identi- fies when a defective component is encountered but does not stop the machine. With the introduction of the variable m, we are now in a position to compare the performance of the two control types.
Memory Control Let us compare the two control modes using the same automated assembly ma- chine as in Examples As before. R, Yield c; '00 The only additional computation is cost per unit in which we include the additional cost of memory control.
In practice. With instantaneous control. These reauttes are ignored in the preceding example. We assume a single work- head with several components feeding into the station to be assembled to a base part. The ideal cycle time for the single station assembly machine is the sum of the individual element times of the assembly op- erations to be performed on the machine plus the handling time to load the base part into position and unload the completed assembly.
Many of the assembly elements involve the addition of a component to the existing subassembly. As in our analysis of multiple station assembly, each component type has a cer- tain fraction defect rate q" and there is a certain probability that a defective component will jam the workstation mj.
When a jam occurs, the assembly machine stops, and it takes an average Td to clear the jam and restart the system. The inclusion of downtime resulting from jams in the machine cycle time gives This might occur, for example, when a fastening operation is performed with no part added during element j. For the special case of equal q and equal m val- ues for all components added, Eq, The elements are listed in the table below, to- gether with the fraction defect rate q and probability of a station jam m for each of the components added NA: not applicable.
Element Operation Time sec p Add gear 0. When a jam occurs, it takes an average of 1. Accordingly, applications of the single station assembly machine an: limited to medi- um volume. For higher production rates, one of the multi-station assembly systems is generally preferred. These cases of partially automated production lines occur for two main reasons' L Automation is introduced gradually on an existing manual line.
Suppose that de- mand for the product made on a manually operated line increases, and it is desired to increase prodnction and reduce labor costs by automating some or all of the sta- tions. Certain manual operations are too difficult or roo costly to automate. Therefore, when the sequence of workstations is planned for the line, certain stations are de- signed to be automated, whereas the others are designed as manual stations.
Examples of operations that might be too difficult to automate are assembly proce- dures or processing steps involving alignment, adjustment, or fine-tuning of the work unit. Many ill- spection procedures also fall into this category. Issue 2 Micro assembly and high speed assembly. Issue 1 Flexible manufacturing and Small Batch Production. Issue 4 Electronics Industry. Issue 3 Parts feeding and parts presentation.
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