Testing & AnalysIs

Using Ultrasonic Sensors for Powder Injection Molding

Derrick D. Hongerholt, Joseph L. Rose, and Randall M. German

Previous research efforts concentrating on proof-of-principle studies have shown that ultrasonic nondestructive evaluation can be applied to the process monitoring of a pow­der-injection-molding system. This research has resulted in the successful detection of several injection-molding problems, such as flashing, short shot, sticking, and sink marks. Past techniques, however, have re­quired that the sensors be positioned so that the ultrasonic wave (bulk or guided) directly interacts with the defect. This article reports results from a technique that uses only one sensor for detecting several common injec­tion-molding defects and does not require that the wave interact directly with the defect.

Table I. The Defect Types Used for Training and Testing the Neural Network

Type

No Defect Weld Line Flash Flash with

Sticking Short Shot

24

Defect Class

Number 1 2 3

4 5

No. of Specimens Molded

12 12 9

9 12

INTRODUCTION

In the past, ultrasonic inspection techniques for detecting defects during powder­injection molding have required that the transducer be placed in a manner that focuses the ultrasonic wave, either bulk or guided, to the defective area.1,2 One major drawback with this method, especially when using bulk waves, is the necessity of multiple transducers if several defects are to be detected in different locations in the specimen. The work described in this article, however, shows that a variety of defects can be detected inside the mold cavity for certain products with only one transducer operat­ing in a pulse/receive mode.

The physical reasoning of this approach is based on the assumption that the presence of defects in the specimen will change the boundary conditions at the mold insert/ specimen interface. The fact that the ultrasonic wave transmission and reflection at the mold insert/ specimen interface is sensitive to these boundary conditions is used. For defect detection, normalized amplitude-based feature profiles are used that are computed from the time-dependent, ultrasonic signal data collected during the molding cycle.

Ultrasonic reflection and transmission factors have been used in the past for a variety of applications, including adhesive-bond inspection,3 monitoring composite cure,4 and monitoring plastic-injection-molding processes.s The benefits of this tech­nique are increased sensitivity and a reduction in the dimensionality of the data set. The normalized amplitude profiles can be generated on-line with an in-house­developed data acquisition system for direct analysis during molding by an operator or for use in a feedback control system. The data are then used to train a neural network for defect classification purposes.

DATA ANALYSIS

The ultrasonic signals collected during the injection-molding process, as described in the sidebar, are used to generate the normalized amplitude profiles. The ultrasonic signals collected at different times during the molding process for a defect-free specimen are shown in Figure 1. The ultrasonic signal collected immediately after the mold closes (just prior to the beginning of the injection cycle) is shown in Figure 1a. The ultrasonic signal collected 11.4 seconds after the mold closes is shown in Figure lb. This signal was collected after the injection-velocity controlled cycle when the mold cavity was almost full.

The difference in amplitude between the ultrasonic waveforms in Figure 1a and 1b results from transmission of some of the ultrasonic energy across the mold insert/ specimen interface. Prior to injection, this interface does not exist because the mold cavity is filled with air; hence, all of the ultrasonic energy is reflected back toward the transducer. As soon as the injection control switches to hydraulic pressure, more material is pushed into the mold and the specimen cavity fills. When the material fills the mold and the packing cycle is completed (approximately 12 seconds after injection beginS) the normalized amplitude increases (Figure lc). Again, this is a result of a change in the boundary conditions at the mold insert/ specimen interface. The ultra­sonic waveform in Figure Id was collected after the mold opens. The contact between the mold insert and the specimen is broken and, again, all of the energy is reflected back toward the transducer, except for a small portion lost due to the residue left on the mold-insert surface.

The same phenomena explained above can be seen on a continuous timescale, referenced to the time that injection begins. The time-dependent amplitude profiles collected from specimens with different defects are shown in Figure 2. Analysis of the data in this manner provides for greater ease in interpretation of the results. To generate the amplitude profiles, the maximum amplitude of the backwall echo is computed for the signal prior to when injection begins. The second backwall echo was monitored in an effort to reduce the noise attributed to the interaction with the first backwall and the signals from the reflections within the transducer itself. This amplitude is considered to be the reference amplitude AR• The normalized amplitudes at the various positions in time (RiT) are then computed by finding the maximum amplitude of the backwall echo for each point in time (NT) and dividing this by the reference amplitude

JOM • September 1996

Normalized amplitude profiles using a pulse-echo configuration are sensitive to conditions within the mold. A major benefit of this technique is that only one transducer must be used in a pulse-echo configuration. The normalized amplitude of the wave reflected from the mold insert/specimen interface is sensitive to defects within the mold cavity. The changes in the normalized amplitude profile result from different boundary conditions at the interface due to the presence of defects. Figure 2 includes plots of the normalized amplitude versus time curves collected during the molding cycle. The data shown in the figures were collected using a 12.7 mm diameter transducer with a center frequency of approximately 2.25 MHz.

The normalized amplitude profiles from specimens with no defects are shown in Figure 2a. The normalized amplitude rises near the end when the mold opens and the specimen is ejected. At approximately 12 seconds after the injection cycle begins, the

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d Figure 1. The ultrasonic signals collected at (a) zero seconds, (b) 11.4 seconds after injection began, (c) 22.8 seconds after injection began, and (d) 57.0 seconds after injection began in molding a defect-free specimen.

1996 September • JOM

Table II. Results from the Neural Network Classification Studies

Class No.

1 2 3 4 5

No. of Patterns

6 6 5 4 6

Patterns Classified Correctly

6 6 5 4 5

25

Equipment The data acquisition and analysis system used in the

experiments is based on a LabWindows (National In­struments) programming environment. LabWindows provides graphical user interface (GUI) capabilities as well as access to basic signal proceSSing routines, instrument control functions, and a C-programming language interface. GUI modules can be integrated with routines for instrument control, data acquisition, feature extraction, and neural-network analysis. The data ac­quisition and analysis system developed here com­bines the hardware control, data collection, and analy­sis procedures into a GUI environment. Continued work in implementing the data from a trained neural network into the data collection system is underway; when this work has been finished, H will be possible to classify defects in-situ.

The hardware selected for digitizing the ultrasonic signatures is a 100 MHz analog-to-digitaf (AID) con­verter (GAGE, Applied Science). This board plugs into a Pentium personal computer and allows collection and analysis of the ultrasonic data in a real-time fashion. The board is easily controlled within the Lab Windows envi­ronment due to the availability of device drivers from GAGE. The ultrasonic signals are collected at a sam­pling frequency of 100 MHz and have a total length of 2,048 points. The total time length of the each signal is 20.48 ~sec.

A flaw detector (Krautkramer Branson) is used to excite the ultrasonic transducer with a shock pulse. This type of excitation allows the piezoelectric element in the

amplitude rises slightly. This rise in amplitude is a result of the end of the packing cycle. The forces pushing the specimen against the mold wall are released; therefore, the boundary conditions governing the reflection from the mold surface / specimen interface are influenced. Short shot (Figure 2c) is indicated by a sharp increase in the normalized amplitude, usually within ten seconds after the injection cycle begins. Low-cavity pressure reduces the contact forces between the specimen and the mold wall, thereby, increasing the amplitude of the reflected wave. Weld-line defects (Figure 2b) are indica ted by a rise in the normalized amplitude later on in time. A larger weld line results in a higher amplitude profile after the packing cycle ends. Prior to the end of the packing cycle, the profiles are very similar. This phenomena was also

EXPERIMENTAL SETUP

transducer to vibrate at its resonant frequency. The received pulse is directed to the receiver of an eight­channel multiplexer (Stavely Instruments). With this receiving unit, extemal gain or damping can be applied to the received Signal to match the signal voltage with the voltage levels required by the AID card. In addition, multiple receivers and/or senders can be plugged into the multiplexer and monitored during the molding pro­cess. The use of multiple receivers and/or senders can improve detection reliabilily and sensitivity.

Mold Design The specimen mold used in this study was designed

by the Pennsylvania State Universily Powder Metal­lurgy Lab. The schematic in Figure A describes the machined side of the specimen mold; the other side is flat with the same outer dimensions. The specimen dimensions and the ultrasonic sensor position are indi­cated in the figure. The sensor is bonded with silicon adhesive to the back surface of the mold insert to insure proper coupling of the ultrasonic energy. From an inspection point of view, this geometry provides poten­tial for using both gUided-wave and bulk-wave tech­niques. Guided waves can potentially be excited in the specimen, provided the proper frequency and impinge­ment angles are selected; their benefits are increased sensitiviity to defects and global-inspection capabili­ties."

Problems encountered during molding this speci­men were short shot, flash, sticking, weld-line defects, cracking, and poor surface finish. The most difficult of these problems to overcome was elimination of the

weld-line defect in the far cor­

Material Selection

The material selected for molding the specimens that were inspected is a 60 vol.% carbonyl iron feedstock. The binder system is 40 WI.% wax, 55 WI.% polypropy­lene, and 5 WI.% stearic acid. This material was se­lected because it is representative of industrial practice.

Specimen Preparation

Defects are produced in the mold cavity by varying the molding parameters. Complete details of the mold­ing parameters are not presented here; however, a brief description of the parameter variations, referenced to the optimum molding parameters, is included.

Specimens wHh short shot were made by lowering the packing pressure to almost zero; therefore, after swHching from injection-velocity control to hydraulic­pressure control, the mold does not continue to fill. Specimens with weld-line defects were also created by lowering the packing-pressure profile; however, enough pressure was applied to continue filling the mold. In addition, the packing time was raised slightly. The combination of lower packing pressure and longer pack­ing time slowed the mOld-filling rate, allowing the mate­rial at the melt front to cool. This resulted in a weld-line inhomogeneity near the end of the mold where the melt front narrows and comes to a point. The packing­pressure profile was increased in order to produce specimens with flash. To produce specimens with flash and sticking, the hold time for the cooling cycle was decreased. This allowed the mold temperature to in­crease and caused the specimens to stick to the side of the mold insert on which the ultrasonic sensor was mounted.

104.1 .... A~ __ ------7"------...;.,. ....

ner, opposite to where the sprue is attached (see Figure A). As the mold cavity fills, the melt front cools quickly because of the large surface area and small specimen thickness. When the melt front reaches the far comer (the last area to fill on the mOld), a weld-line defect is often present. Most often, this defect is a small discontinuily caused when the edges of the cool melt front narrow to a point and join together. Because the melt front is cool when it narrows down, it does not want to bond together on contact. A high melt-tempera­ture profile and fast injection­speed profile were used to help eliminate this defect.

104.1

44.5

104.1 All Dimensions in Millimeters

Sprue Attachment

- ~--

Figure A. A schematic of the mold used in the experimental investigations. The position of the ultrasonic sensor is included. Only one side of the mold is shown-the mating side is flat with the same outer dimensions.

26

The sensor configuration used is shown in Figure B. This type of configuration is commonly referred toas a pulse-echo tech­nique. In this case, the ultra­sonic sensor serves as both the sender and receiver. The wave is transmitted into the steel via the silicon adhesive; it then re­flects from the mold insertfspeci­men interface and is received by the same sensor.

Specimen

Mold Insert

Senderl Receiver

Figure B. Schematic representations of the pulse-echo transducer configuration used in the experiments.

JOM • September 1996

l.l l.l

weld line

10.9

no defects

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~ ~ 0.8 ~ 0.8

1 0.7 g

1 0.7

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observed while molding the specimens with other types of defects, except for the short­shot specimens.

Flash is indicated by a lower overall normalized amplitude profile (Figure 2e), resulting from material being forced into the mold under higher pressures, thus, making the contact at the specimen/mold interface better. The ultrasonic profiles collected from specimens that flashed do not have a transition point at the end of the packing cycle. This results from the fact that the forces causing the material to flash were great enough to pack the material in the mold, so that even after the packing pressure is reduced to zero the material is in good contact with the mold surface. Flash with sticking has a normalized amplitude profile (Figure 2d) that is comparable to a specimen with flash early on, however, after the mold opens the amplitude remains low. This phenomena results from continuous contact between the specimen and the mold insert. To detect sticking on either side, two transducers could be used, both in pulse-echo mode. One of the profiles collected from a specimen that flashed and stuck to the mold surface did show an increase in amplitude after the mold opened; however, the amplitude did not rise to the levels that the specimens with no sticking did. This could be a result of less severe sticking. Further studies in this area need to be considered before definite conclusions can be made.

DEFECT CLASSIFICATION DURING NEURAL NETWORKS

Neural-network analysis was performed on the data collected in an attempt to correctly classify the defect type from the normalized amplitude profile. Features describing the normalized amplitude profiles were extracted from the data sets and used as input to train the neural network. The neural network simulator used is the Stuttgart Neural Network Simulator (SNNSY from the University of Stuttgart. The network (Figure 3) is a standard backpropagation network with three layers. The first layer is the input layer, the second is the hidden layer, and the third is the output layer. The first layer has four possible input nodes, one for each feature. The second layer has six nodes that are fully connected to all active input nodes and all of the output nodes. The third layer has five nodes that represent the five defect classes.

The total data set used to train the network consists of 54 patterns. Table I lists the defect classes and the number of specimens for each class. The features used to represent the normalized amplitude profiles were the area under the normalized amplitude profile (feature one); the minimum amplitude in the normalized amplitude profile (feature two); the amplitude at the end of the profile (t = 57 seconds) (feature three); and the time that the profile crosses the line y = -8.33e-3*t + 1.0 (t ~ 4 seconds) (feature four). Only the area under the normalized amplitude profile and the ampli­tude at the end of the profile were used for the final training of the network. The other two features proved to be redundant.

The patterns were separated into two fractions of 27 patterns each for training and testing the neural network. The results from training the network with fraction two and testing the trained network with the data in fraction one are included in Table II. Analysis of the data in this manner resulted in proper recognition of 26 out of 27 patterns (96.30%).

The feature spaces representing the trained network's response to each defect type are included in Figure 4. A feature space is generated by feeding into the network every combination of the training features and plotting the network's response at the output

1996 September. JOM

1.1,---------------,

1.0

10.9

~ 0.8

] 0.7 g

0.6

c

short shot

time [seconds 1

Figure 2. Specimens containing (a) no defect, (b) weld-line defect, (c) short shot, (d) flash and sticking, and (e) flash from normalized second backwall echo amplitude profiles col­lected during the injection molding cycle.

Input Layer

Hidden Layer

Output Layer

Figure 3. Architecture of the neural network used for classifying the defects. Nodes 1-4 are the input features (only 1 and 3 were used), nodes 5-10 are the hidden layer, and nodes 11-15 represent the classification out­put. The architecture is based on a Standard Back Propagation Network.

27

o Normalized Feature 1 o b

e

Normalized Feature 1

node that represents a particular defect. Results are for training the network with fraction two and testing the trained network with the data from fraction one. Each feature space represents the decision surface for a particular defect class. The white area indicates the portion of the feature space that represents the defect class.

CONCLUSIONS A thru-transrnission bulk-wave technique is suggested so that information about

the mechanical properties of the material, modulus, and density can be obtained in addition to defect detection and classification potential. 8 This will be especially useful in classifying the material quality of defect-free specimens. The use of specific guided­wave modes could also lead to a possible increase in detection sensitivity and efficiency. Guided waves have the ability to propagate in certain geometries with special wave structures that make the wave sensitive to defects located in different locations in the specimen. In addition, because guided waves propagate through the complete geometry, they can be used to scan a specimen in an efficient manner. Finally, their use may lead to increased sensitivity to the boundary conditions between the mold insert/ specimen interface; they have particle motions that are perpendicular to the wave propagation direction and have shown increased sensitivity to interface conditions. The weights from this network can be implemented into the data-collec­tion procedure to classify unknown specimens while molding. This work is underway.

ACKNOWLEDGEMENTS This work is supported by grant number DMI9408878 from the U.S. National Science

Foundation.

References 1. S.M. Menon et aI., "Ultrasonic Sensors for Powder lniection Molding," Advances in Powder Metallurgy & Particulate Materials, 4 (1994), pp. 71-84. 2.J.L. Rose et al., "An Ultrasonic Sensor Innovation for Improved Process Control in Powder Injection Molding" (Paper presented a t the ASME Control Conference, Seattle, W A, June 1995). 3. J.L. Rose e t aI., "A Model for Transverse Wave Sensitivity to Poor Adhesion in Adhesively Bonded joints," /ASA, 80 (1986), Supp. 1. 4. J.E. Eder and J.L. Rose, "Viscoelastic Wave Propagation in Anisotropic Media with Applica tion for Composite Cure Monitoring," AMD-Wave Propagation alld Emerging Technologies, vol. 188 (New York: ASME, 1994), pp. 173-186. 5. C. Thomas, j.L. Rose, and Z.K. Li, "An Ultrasonic Sensor to Monitor the Mold Cavity Conditions During lniection Molding," Review of Progress in Quantitative Nondestructive Evaluation, ed. D.O. Thompson and D.E. Chimen ti (New York: Plenum Press, 1993), pp. 2333-2340. 6. J.L. Rose, "Recent Advances in Guided Wave NDE" (Paper presented atthe IEEE International Ultrasonics Symposium, Seattle, WA, November 7-10,1995). 7. SNNS User Manual, version 4.1, University of Stuttgart, Institute for Parallel and Distributed High Performance Systems. 8. A.S. Birks, R.E. Green,jr.,and Paul Mcintire,eds., Ultrasonic Testing: Nondestructive Testing Handbook(2nd ed.), vol. 7 (Columbus, OH: American Society for Nondestructive Testing, 1991).

28

o

Figure 4, The feature space gen­erated by the neural network for each defect class. The white area indicates the region of the feature space representing a (a, class 1) no defect, (b, class 2) weld line, (c, class 3) flash, (d, class 4) flash with sticking, and (e, class 5) short shot. The fea­ture spaces shown correspond to the network that was trained with the data set labeled fraction two. The class number plotted on the curve correspond to the test data from fraction one.

ABOUT THE AUTHORS

Derrick D. Hongerholt earned his M.S. in engineering science and mechanics at Pennsylvania State University in 1995. He is currently a Ph.D. student at Pennsylvania State University.

Joseph L. Rose earned his Ph.D. in applied mechanics at Drexel University in 1970. He is currently the Paul Morrow Professor in Engi­neering Design and Manufacturing at Penn­sylvania State University.

Randall M. German earned his Ph.D. in materials science at the University of Califor­nia-Davis in 1975. He is currently Brush Chair Professor in Materials at Pennsylvania State University. Dr. German is also a member of TMS.

For more information, contact D.O. Hongerholt, Pennsylvania State University, 114 Hallowell Building, University Park, Pennsylvania 16802; (814) 863-8163; fax (814) 863-8164; e-mail weasel @j1r.esm.psu.edu.

JOM • September 1996