Injection loading of submunitions for smart weapons is a novel automated processing technique that can benefit from adaptive process control. This paper describes how the quality of submunitions could be controlled by using a neural network code in real time. Future work is planned to demonstrate fewer rejects and pollution reduction during submunition manufacturing

Hinde, R.F. Jr.

Newman, K.E.

Parkinson, W.J.

Smith, R.E.

Wantuck, P.J.

UNT Digital Library

I LXUR- 98-2012 v@ forpub!ic.release; % P  stributim is unlimited. Title: Aut hor(s): Submitted to Los Alamos N A T I O N A L  L A B O R A T O R Y  NEURAL NETWOJX FOR QUALITY CONTROL OF SUBMUNITIONS PRODUCED BY INJECTION LOADING Ronald E. Smith, William S.  Parkinson, Ralph F. Hinde, Paul J. Wantuck, Kirk E. Newman 2nd Int’l conf. Engineering Design & Automation Maui, HI August 9-12, 1998 Los Alamos National Laboratory, an affirmative actioNequal -unity employer. is operated by the University of California for the U.S. Department of Energy under contract W-7405-ENGa. By acceptance of this article, the publisher TBcognlzes that the U.S. Government retains a nonexclusive, myalty-free license to publish or reproduce the published fm of this contribution, or to allow othen to do so, for U.S. Gavemment purposes. L a  Alamos National Labwat0t-y requests that the publisher identify this article as wwk petfomed under the auspices of the US. Department of Energy. The Los Alamos National Laboratory strongly supports academic freedom and a researcher’s light to publish; as an institution. bwver, the Laboratory does not endorse the viewpdnt of a publication or guarantee its technical correctness. Form 836 (1W96) DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof. nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or use- fulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any spe- cific commercial product, proctss, or ~ M c e  by trade name, trademark, manufac- turer, or otherwise does not necessarily constitute or imply its endorsement, m m -  mendation. or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect thosc of the United States Government or any agency thereof. DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. NEURAL NETWORK FOR QUALITY CONTROL OF SUBMUNITIONS PRODUCED BY INJECTION LOADING Ronald E. Smith', William J. Parkinson', Ralph F. Hinde, Jr. Kirk E. Newman', and Paul J. Wantuck' Engineering Sciences and Applications Division' Los Alamos National Laboratory Los Alamos, NM 87545 Energetic Materials Research and Technology Department Naval Surface Warfare Center, Indian Head Division Yorktown, VA 23691 ABSTRACT Injection loading of submunitions for smart weapons is a novel automated processing technique that can benefit from adaptive process control. This paper describes how the quality of submunitions could be controlled by using a neural network code in real time. Future work is planned to demonstrate fewer rejects and pollution reduction during submunition manufacturing. KEYWORDS Neural Networks; Smart Weapons; Submunitions; Pattern Recognition; Process Control; Injection Loading 1. Introduction The injection loading device discussed in this paper [ 11 was developed in order to produce submunitions filled with highly viscous material. Each m fills multiple submunitions serially, with high viscosity plastic bonded explosive (PBX) material. Even though most loads are acceptable and meet specifications, the rejection rate is often excessive. The loads that are not acceptable present a severe waste disposal problem. The current technology requires an X-ray inspection after the submunitions have been loaded with PBX. Loads that have voids, or that have density gradients will be rejected. Since operating conditions leading to rejectable products may occur at the beginning of an injection cycle, aliquots of ten pounds of PBX and the corresponding submunitions loaded may also be rejected. Therefore, it is imperative that the process control algorithm recognize disturbances in the injection loading parameters as soon as possible so that corrective action can be taken to resolve the upset before the PBX loading is completed. Each load requires about 30 seconds. In an effort to determine submunition quality on-line, the device was instrumented. Ram displacement, cavity pressure, and hydraulic pressure were measured at two second intervals for each load. Ram velocity, shear rate, shear stress, and viscosity were calculated at each time interval. The data were then compared with the post-mortem X-ray results to determine if the load was a pass or a fail. To the naked eye, no patterns emerged in the injection loading data that correlated specific processing parameters to the submunition pass/ fail criteria. Several neural network models were applied before we found one that worked. A backpropagation model using two inputs, cavity pressure and viscosity, and three hidden nodes finally cracked the code. This model, even with a limited number of training sets, was able to predict bad or good loads loo%, after only four sensings, eight seconds I c . -  u into a run. A 96% correct prediction rate can be obtained from a similar neural network model that uses only the first four seconds worth of data. This information, when implemented on the injection loading device, will provide on-line information as to whether a reject load is being processed. We intend to incorporate the model into a control system and try to make on-line corrections to possibly produce 100% good loads (or “zero defects”). If our on-line corrections do not work, we can at least interrupt the device and prevent the production of hazardous waste. 2. System The injection loader system is a unit operation that simultaneously degasses and injects viscous PBX into small submunitions or through narrow flow channels without the PBX ever turning a corner. This apparatus has two identical chambers for degassing and injecting PBX. See Figure 1. The PBX is fist  pumped through a splitter plate and degassed into the f is t  chamber. Then the upper part of the machine rotates 180 degrees to align the degassed PBX with the inject ram. The second chamber is now available for degassing the next aliquot of PBX, while the f i s t  chamber is ready for injection loading. Meanwhile, the first submunition has been lifted into a sealed vacuum shroud and evacuated. A programmable logic controller (PLC) provides the signal to inject the correct PBX increment into each submunition serially. One submunition is loaded in each 30 second cycle. The PLC will allow about eight submunitions to be loaded from an aliquot of 10 pounds of PBX. Figure 1. Injection loader apparatus at NSWCIHD Yorktown, VA The supervisory process control software is run on a Pentium personal computer (PC), and monitors processing parameters necessary to track mass transfer and momentum transport phenomena. We track vacuum level in the degassing chamber, vacuum level in the shroud where the submunition is evacuated, temperature of the PBX in the injection chamber, ram displacement, hydraulic pressure driving the ram, cavity pressure of the PBX entering the submunition, and time. We calculate ram velocity, shear rate, shear stress, and PBX viscosity. The data available for analysis consisted of 45 sets of injection loading time history data and a determination of whether the loading passed or failed inspection. Of the 45 runs of data, 29 passed inspection and 16 failed. Figure 2 shows the time history data for cavity pressure and ram velocity for ten (five pass, five fail) selected loadings. As is evident from this figure, it is difficult to discern passing loadings from failing loading by visual inspection of the time history data. 1 2  3 4 5 6 7 8 9 10 I 2  3 4 5 6 7 8 9 IO Time Step Time Step Figure 2. Cavity pressure and ram velocity time history for ten selected runs 3. Neural Network Model Standard backpropagation neural networks [2] were used to solve this pattern recognition problem. The inputs to the network were the first N points of the time history data for one or more variables (explained in more detail later). The output of the network was a single value in the range 0 to 1, with 0 representing a failed loading and 1 representing a passed loading. A value of 0.5 was used as a cut-off for the padfail choice. By analyzing the underlying equations relating the process variables and looking at correlations between the actual data, three significant variables emerge. These are cavity pressure (CP), ram velocity (RV), and viscosity (VIS). The 45 data runs were divided into a training set and a test set. The training set (34 runs) was randomly selected from the entire set so that it contained 75% of both the pass and fail subsets. The remaining 11 data runs were used as the test set. A schematic of one of the networks used is shown in Figure 3. Oulput Node A ot all connections shown) Input Nodes CPI cP2 CP3 Vis1 Vis2 Vis3 Figure 3, Neural network framework * ** * i . 4. Results Initial attempts to design a neural network to recognize passifail patterns from the time history data focused on using the entire time history of CP and RV. However, there was simply not enough training data available to train neural networks of this size. Later attempts focused on designing smaller networks that only looked at the first few time history data points to differentiate between passifail. Networks that used the first four time steps of data for a single variable were designed and trained. The number of hidden nodes was varied from two to five. The best networks had a 95% successful prediction rate using VIS, 90% using CP, and 75% using RV. Networks that used two of the significant variables were then tried. The networks using CPRV and RV/VIS yielded 98% prediction rates while the CPNIS network yielded a 100% prediction rate. Further efforts focused on using this set of inputs. The minimum number of hidden nodes that yield a 100% prediction rate was found to be three. The number of time steps used was then reduced to three and then to two. The results of the best networks are summarized in Table 1.  The column labeled “Correlation” is a measure of how well the output of the network (a number between 0 and 1) correlated with the actual result (fail=O, pass=l). Table 1. Results from CPNIS neural networks of various sizes These results indicate that an adaptive process control system using this neural network has the potential to identify process disturbances early in the injection cycle time. More work needs to be done, however, to identify appropriate manipulative variables that can be assigned corrective actions to preemptively resolve these upsets before the PBX loading cycle is completed. Future work includes a plan to implement this neural network into process control system for injection loading submunitions. The hope is that we can demonstrate the utility of an adaptive process control system to inject several different kinds of submunitions at reduced cost and also producing significantly less pollution. REFERENCES 1. 2. Mahoney, T.I., Newman, K.E., Gusack, J.A. and Sallade, G.J. (1995). U.S. Patent 5387095, Apparatus for injection molding high-viscosity materials. Hertz, J., Krogh, A. and Palmer, R.G. Introduction to the Theory of Neural Computation. Addison-Wesley, New York, 1991 

Neural network for quality control of submunitions produced by injection loading

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Neural network for quality control of submunitions produced by injection loading

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