Welcome everyone to the next session of CCM Connect, known as Research Review. Previous research review we had for many years. Today we have three speakers like always, all of them are on the second year of PC program. One from material department to from mechanical guess. They are very exciting talks, but we start with the first speaker, Hammad, second year PE student from Material Science department working with Dr. he's going to talk about the sensing sensing of resin flow during the manufacturing of companies fires. Can you hide the boxes and the medial? Okay. Hello everyone. Good morning, this is Mama me, a second year field student at the Department of Micro Science and Engineering. I'm working under Professor Eric Christensen, and today I'm going to present my research work. And as you can see, the research topic is tensing continuous fibers manufacturing. No? Okay, so these are the outlines that I'm going to cover today which contains introduction and motivation, Carbon native functionalization and deposition, response to reason, infusion and curing, and finally, conclusions and future work. Here we can see the introduction and motivation. On the left side picture, we can see the application of carbon fiber composite in multiple fields like aerospace, marine, military industrial, equipment, sporting goods and automobile. On this picture, we can see the application of manufacturing automotive. Actually, this research is part of a common project of which the main goal is to make a composite by continuous fiber Tive manufacturing for the automotive application as an integral part. This research focuses to monitor the insitu resin flow and cure during composite manufacturing. Here we see the functional coatings for sensing. On this picture, we can see the cylindrical, hexagonal shape of carbonenotube, which is cross linked with polythenemine. This compound is basically used for making sensors out of different fibro substrates such as carbon glass fiber. On the latent side, we can see the chemical structure of Epic, which is a commercial in agent, which is basically dial plenamen. This is the chemical structure of a commercial epoxy based regine, which is called a 862. This is basically a Digctamyler phenyl F. In my research I used this sin and curing is in combination. On the left bottom picture, we can see a cross sectional view of a carbon nanotyetate fiber. The slides represents the sensing strategy actually during sin fluid fur, the tunneling resistance of synset changes. Actually the tunneling effect is such phenomenon where the charged particle, such as electrons can't pass through, pass through a barrier which has higher potential energy than the charged particle itself. This is how actually by the tunneling effect, we can sense the electrical resistance of different Tyco substance while in phasing the region sight. On the left picture, we can see the tunneling effect between two adjacent carbon nanotube networks. This tunneling based sensing can be utilized to monitor different parameters like strain cracking, Alma expunction, temperature, polio mobility, hearing and manual. Now I'm moving into carbon, not continalation, and deposition. Here we can see the carbon native processing. In this research, 1 gram per liter concentrated carbon nanotube dispersion was made to make this dispersion. 2 grams of carbon native powder was weighted and dispersed into two liter of ultrapure water. Then the dispersion was ponicated by ten gas for 10 hours to oxidize the carbon nenative dispersion. Then the dispersion sonicated by polythlinmin for additional 4 hours. Actually, the polythlinm protonates the surface of the carbonenet, which actually helps the carbon nanotube to remain well dispersed and in the dispersion being positively charged. Here we can see the final structure of carbon nontube and polycyliion M. This is how a carbon nenative dispersion is made from the carbon nenative powder. Here we can see the electroptic deposition mechanism. Actually in this mechanism, as we can see here, the fibrous substrate is placed on a charged electrode, which is placed across another oppositely charged electrode that is powered by electric electric supply. This is the basic mechanism of electropolytic deposition. This deposition is done in a medium, as I mentioned earlier, due to poly, polythlenmsonication, the carbon tube gets positively charged. As you can see, that the surface is positively charged. The carbon nenative dispersion is also positively charged. And the other electro which is placed across, which is negatively charged when we connect with the power supply, then the positively charged carbon nenative dispersion moves towards the negative charge. And actually the deposition happens. Here we can see the chemical structure of polytheneming. This is the chemical structure of carbon nenative grafted with polythene. Here we can see the sample preparation by electrolytic deposition. As we can see, the carbon fiber toes are attached on one electrode and it is placed across another electrode, which is attached with the hem perfect clan. In this figure, we can see the experimental set up of electroportic deposition and the electric power supply. This figure shows the micrographs of unpoted and carbon nanotube pot carbon fiber to. On this picture we can see the well deposition of carbon nanotube on the surface of the carbon fiber which actually indicates the efficacy of electrophertic deposition. Now let's move on to response to resin infusion and fine. This figure represents the schematic of the experimental set up to measure the electrical resistance of a single filament, multiple filaments and T of M seven carbon fiber. Actually, in this research, M 712 carbon fiber filament is being used to make composite by continuous seretic manufacturing. In this experiment, actually all the samples were placed on a glass light, and the resin was dropped in the middle of the sample at elevated temperature. And the electrical response was measured utilizing eight measurement system. Actually, this is the actual experimental set up to measure the electrical response of single filament, multiple filaments, and alto of carbon fiber. After the sample preparation, all the specimens were placed on a hot plate and the electrical response was measured while increasing the resin and curing. Here we can see thermo resistive response of carbon fiber. On the left hand side, we can see the thermal thermal effect on the electrical response of carbon fiber. Actually in this figure, the red line represents the temperature and the green and the blue line represents the resistance change percentage of carbon fiber. As we can see, as the temperature increases, the resistance decreases as a function of time. And, and at certain point when the temperature again decreases, the resistance increases. This could be due to the effect of temperature, as when we apply the heat on any specimen, it agitates the molecular atoms of the molecule that may be plays a role to increase the resistance change due to the enhanced mobility of the electrons inside the molecule. In this figure, we can see the hysteresis loop of the recent change of carbon fiber as a function of temperature. Here we can see the resistant change of single filaments and multiple filaments. On the left hand side, we can see the electrical resistant response of single filament. We didn't notice mentionable resistant change in case of single filament, which makes sense because single filament is broad like structure and as carbon fiber is inherently hydrophobic in nature, that resin cannot penetrate into the infla, direction of the fiber. I think the rest doesn't have any mentionable impact on the resistant change, but in case of multiple fibers, we observe seven to 8% resistant change as a function of time, which could be because this is multiple fibers, there is space between individual filaments. When the resin infusion, it basically makes the filaments apart from each other. Between those spaces the resin can infuse, it can impact the electrical response of the multiple filaments. Here we can see the bundle resistance change with carbon nanotube coating. In case of carbon not carbon fiber, we observed 26% restant change. Another thing we observed in this experiment that right after resin drop, the restant change increased up to 26% which could be due to the tunneling effect between the individual filaments present in the which is 12 carbon fiber, uh filament. Another thing is noticeable here, we see as the uniform line compared to the graph which represent the stent change of dry to the uniform line represents a better dimensional stability of carbon ninitive cott compared to the dry carbon fibto. To further analyze the efficacy of carbon nenative coating, we measure the electrical response of carbon nenative coated glass fiber. This figure represents the uncoated glass fiber. In the middle picture is the glass fit. This is the experimental set up to measure the electrical response of glass. Actually, in this experiment, all the specimens were cut in 2.5 inch in long. And a test on the glass light, and the electrode depression was done for 15 minutes in this case. And the electrical response was measured by utilizing K four were resistance measurement system. Here we can see the resistance response of the state glass fiber. As we know that glass fiber is basically used as an electrical insulatory in different electronics. But in this experiment you observed that occurs after carbon ninitings, non conductive glass fiber became electrically conductive. In this figure, we see that the glass cyber showed up to 30% stan change, which further indicates the efficacy of carbon nerati coating to make any non conductive fibrous substrate to convert it to a elliptically conductive substrate. Now conclusions and future work to conclude in this research, 1 gram polluter concentrated carbonative dispersion was made and was successfully deposited onto glass and carbon fiber substrates. And temperature had a distinct impact on the electric response of the fiber. We observed that both carbonative coated glass and carbon fiber showed mentionable sensitivity to resin in fusion and hearing. And right now, the development of resin pure kinetic model and mechanical characterization of sensors are going on. As a future work. The deputting also will be experimented for the comparative analysis with other coating mechanisms. Thank you very much for listening. Any questions for what's the benefit of using ozone during the dispersion phase of the carbon tubes? Okay, that's a very good question. Actually, sonication is basically used to oxidize the surface of the carbon nanotube contains oxygen. It basically oxidized the surface of the carbon tube and it reacts with the carbon nanotube so that it can create different functional groups like hydrosup and carboxylic which can further interact with the Pb substrate. Follow up on that question. Yeah, I don't even know how much ozone can damage your, your repair latitude If you have over exposure to actually to make carbon narative dispersion using the ozone gas is established method, that's a good thing to analyze whether it can damage the surface of the carbon narative. But so far I know that it's kind of established and maybe it doesn't have that detrimental effect on the structure of the carbon. Then initially developing this process P, you're measuring the Os in experience for different levels of exposure, showing that Bob zone for 16 hours or 10 hours. Discussed 10 hours and hours. So initially and the process for being established for I think every hour measured the open and then came up with that. You're working with an optimized process, right? Somebody. So that's an important thing for you to understand? Yeah. All right. Yeah. So the answer is it's obviously somebody optimized this. We checked the kinetics and we understand where this is and we've dialed in the right. Yeah. That's correct. Yeah. Thank you. Yes. Can you go back to your Figure 12? Figure two? That's possible work and it has a moment. The one, the figure on the right? Yeah, That is classical and thermal analysis. Yeah, That hysteresis loop is because your sample temperature measurement is not close enough to the sample. Actually, I did this experiment inside, inside the oven in our lab. Actually, what we did, I raise the temperature from the room temperature up to 105 degrees centigrade. And then I lowered the temperature from that point to the room temperature. Then what I observed that as we increase the temperature, the stance decreases. Yeah, that's not what I'm getting at. Okay. So the temperature you're measuring is the temperature of the furnace, not the temperature of the sample? No, actually in the sample I used thermal sensor actually that is embedded with the black box that we use to measure the resin systems. So I'm ensuring that the temperature is of the sample temperature. Okay. If you're measuring the sample temperature accurately, then that change is coming from something is changing the material. Yeah, that might be if I have a sample here or temperature here. Yeah. I have a big blue. Yeah. Yeah. I call it a cat side. I bring the thermocouples touches. The sample is a straight line back and forth. Yeah, that's all. What are the blue and green lines in this and the following two different samples? No, same sample just cycle one and cycle two. I measured actually four samples together. And just show the two samples, the two different samples, the blue line and the Yeah. Yeah, two different samples of the same carbon five. Dr. List just one question. So this sensor is sensitive to multiple mechanisms such as show temperature and I assume it's also dependent on the degree of cure of the resin we resin in between your two as well as mechanical stream. If you were to use this in an added manufacturing, temperature cure and residual stress in stream going on concurrently, how would you relate the sensor output to the specific actually professor in this search, my ultimate focus is to monitor the resin infusion and curing. But as a fundamental analysis, I measured the effect on the sensing property of cable fiber. Actually the sensor that we develop. It basically works based on the tunneling effect of the substance that we are trying to measure. By measuring the sensor response, you show you get a response if you isolate cure at constant temperature. Yeah, it works. Mechanical strain at constant temperature. Yeah. And to rescued. But in the real process and the temperature is the byproduct that reoccur connected thermic reaction. Yeah. In the real process. All that's happening at the same time and you get one measurement from the sensor. Yeah. How would you know the temperature or degree cure mechanical strength? Yeah, there's a very good fin, actually Professor Bets by now I'm trying to figure out kinetic model so that I can correlate my resistance response with the kinetic model. I mean, at what temperature it's getting Ed and at what temperature the dilation is being happened, so that I can correlate the resist response with the cure kinetic. At that point, I think I would be able to apaldate my resistance response data with the actual incites in flu and Q and that's question. You have the cure kinetics model, predicted degree of cure interpretre center. You already have the degree of cure data you're trying to send. Yeah. So one quick comment stream and temperature in the response, the slopes are quite different and for some of the previous work we just input a temperature calibration and stuff like that out of what we're trying to measure. In this case, for example, is flowing, that is storing, it is going to be challenging to segregate what is coming in. That's a classic problem. I can measure the output of the center, But inverting that, related to the things that the center is sent to, can be complicated. There's multiple combinations that could be the same. Century say that is not unique, unique inverse. The last question, just one quick comment. Jack said, Steve's question, understanding the, you know, the physics of why you're seeing that history in any resistivity is kind of a critical that leads to Jackson. Okay. So Okay. I go to I will focus on that. Yeah. Right. All. Thank you very much. Next presenter is working the posing effect of fiber aspect ratio on mechanical properties of a. Good morning everyone. My name is Dan. I'm from mechanical engineering. The topic that I'm going to present today is the role of fiber aspect ratio on the mechanical performance of highly aligned shot fiber composites process through top process. Here's a quick overview of the things that I'm going to cover today. I'm going to go through like a quick background and the motivation of the work that we're doing. And then the objectives and the outcomes from what we're doing. The experimental approach that is designed to approach the problem. And then a quick discussion into the analysis and the result. And then concluding remarks with the feature works and the conclusions from the work done. And finally, the acknowledgement. The background and motivation for this work is from the sustainability aspects of composite processing. In simple terms, the recycling approach where we introduce a step into the composite processing, where we introduce the recovered or recycled carbon fibers from the composite scrap. And eventually trying to close that loop to enable a subtler economy and a closed loop composite processing. However, there are certain challenges that show up when we try to do this. So the first one is going to be the fiber lens reduction. The composites from the composite crap are like chopped down to smaller segments, which eventually introduces an inconsistency in the fiber lens when we try to process them into the short fiber composites. The second one, the next one is the fiber strength and the interface quality, which is an artifact of the pyrolysis or whatever the recycling process itself. Where there are like some resin residues that are left over on the surfaces of the recovered fibers which compromises the bonding between the matrix and the fiber in the final recycled composite. If we try to reduce that by introducing some oxidation step, by burning off that, residues eventually degrades the fiber strength in the first place. And the reminding. The process related challenges like having a high alignment and orientation in the process shot fiber composite is highly challenging. And also having a higher fivel infraction. The processing challenges are approached using the tough process which showed a very high potential in achieving those two. The topic that I'm going to cover today is mostly related to that length. Hereafter I'm going to has aspect ration of the fiber and process related challenges. Here in the bag you can see this like the graph adapted from several literature reviews where we have a demonstrated property translation as a function of aspect ratio from several analytical models. Experimentally, getting this data is really a tricky and challenging test. So these are, as I said from analytical models, like models from carbon site equipment, and impact from opere model. As you can see, as I said experimentally demonstrating demonstrating this is highly challenging from the processability perspective, as the fiber length keeps on increasing, the processability is highly challenging because of alimentic clustering of the fibers and also the breakdown of the fibers to shortly. The whole objective of my studies is going to be on demonstrating the experimentally like the performance as a function of the aspect ratio and looking into the processing quality and eventually comparing the experimental data generated with preexisting models. Here is like a quick flow chart that explains the approach that we followed. Here we chose two different model systems like the material systems, one is the IE glass fiber and the Atx, where the fibers are in two different formats, which is continuous and discontinuous. And the discontinuous are in three different aspect ratios like as shown here. The resin is in the format of a resin film, and initially we demonstrated the strength of that specific individual components to see everything in the Expected thing. And eventually, we tried to process the composite panels, like initially doing the preform processing, using the tough process, and then repreging them and eventually processing them into composite. And doing the Tenzl testing and eventually comparing them with the models. Here is the pictures of how we process the materials like the preforms, like the continuous one is already in a fabric format with the unit directional fabric. The reminds are like the shot fiber composites which are processed using the tough process and they're like initially prepared using the resin film infusion technique where the preform is, uh, sandwiched in between the resin films and they're under vacuum for specific time to infuse the preform and eventually they're transferred to the autoclave to cure them under higher pressures, to avoid any voids or any internal repair. Here are the results that are the visual results from what we got from the processed composites, the one on the top of the scans, which usually shows that there is less variability in our short composite panel, the top panels. However, in the continuous panel, there is some defect, which is an artifact that the fabric itself came in a stitch format. There is a high chance that there are resinlocations which is also evident from the scopy that we can see here. These are like having a lot of resin location exactly to the locations where we have that stitching. And as you can see in the microscopic, microscopic images from the tough samples, it is highly consistent and there are not any significant presence of any voids or ferocity. The next thing we demonstrated is the orientation studies, which is also one of the key aspects in property translation. As you can see from the plot, the tough samples, the shot fiber composite samples, they're always having the tight tolerance with the orientation within that plus minus five degrees. However, in the continuous there is like a widespread of that orientation which is like due to certain regions like handling issues which is as we said from the stitching and the processing issues, maybe due to the resent locations, there is a high chance that the fibers have freedom to reorient themselves. And that's the data taken from several locations. So there is a huge variability from location to location. However, in tough samples that we have got, it is like a high degree of alignment accuracy. Which is like a good sign that the data that we're can rely on the other crossing challenges like the orientation and the microstructure quality. Here are the mechanic, the zyl results from the samples tested from those panels. If you look at the one on the plot on the right side, it is the modulus as a function of aspect ratio, which is compared to the continuous composite, along with two different models, like the micro mechanical models as you can see. In this case, the modulus is always consistent irrespective of the aspect ratio, which is significant because it's normalized to certain five volume fraction. And when the length is typically beyond the critical length, I mean, you would expect that the orientation and the fiber volume fraction plays a dominant role in translating the property. However, in this trend, there is an initial degradation at the lowest aspect ratio there. And eventually it climbs up to the point where we had that, where there is a complete property translation with respect to the continuous, what we have tested like the continuous baseline data. Here, there are certain reasons that we can interpret this. From the issues with the issues with the bonding like from the short fibers to the adhesion between the matrix and the fiber plays a key role in this scenario. And we also conclude that that played a major role in degradation of the property from what we have seen from the failure modes here as you can see, there is a longitudinal splitting which is highly dominant in this case. Which is the key sign of having a interface failure and also evidence from our stress strain curve. This is like a representative stress strain curve of one sample from each set. A number of like around five to six samples are from each set a representative. So as you can see from the stress strain lot, there is like multiple stress drops which would explain the page that interface or like the debonding surface which would be explained by that. Since we are sure that the modulus had a real translation with whatever the models that we tried to compare, and also with the continuous, we tried to compare the results that we got from the strength from the micro mechanical models, especially from three different models that we're showing here from cock, shear leg and the atom and the helping side matter. So as you can see, there is like a significant drop in the property. So that's going to be the topmost property that we can achieve if everything went well, like having a good idition, having a good load transfer, and no other internal defects that should have been somewhere there. However, there is a degradation not only just for the shock fiber composite but also for the continuous composites. Because of several factors the models show here doesn't predict any internal palos or any other issues that are arise during the crossing like the interface bonding or any other stuff. So which is the reason that we can say that we can rely on the property that we got from the continuous rather than on the models. Finally, the conclusions. Here we try to demonstrate the property scope as we have shown in the initial motivation slide. We try to exponmentally demonstrate that properties as a function of aspect ratio and eventually have a mix of aspect ratios, like having a broader aspect ratio within the composite. Which is more realistic for a recycled composite to see where the property slide. And also try to compare with the model comparisons and see which one is playing a dominant effect. Like which parameter is playing a dominant effect. Eventually the future work, we'll be looking at that aspect, like trying to improve the IFass, like the bonding strength. And also looking at mix of that multiple aspect ratio to demonstrate the effect on the degradation. And the product is generated so far on like the topmost region. So we wanted to go as low as possible with the aspect ratio to generate that whole product and eventually then have that. With that, I think sorry, I would like to acknowledge my supervisors and for the support and also Department of Energy for their funding. And also Chris from Composition Automation for Tough processing. And Nick for scan data acquisition. And Ed for the support. Thank you. Will you use the stitched uni directional as you're based on the compared finite aspect composites. But you showed started out showing that the continuous had a larger variation in Fibal orientation distributions as well as resin rich pockets. Object. Could you first comment on it? Do they have equivalent fiber? One fraction. The top composites. And do you think that's a valid baseline in the normal microstructure associated with age? The fiber volume fractions that we calculated from the continuous composite is from several locations, so it has a lot of prison age pockets. So we took several locations and average it up, and the volume fraction we achieved is not the same as tough, tough. We got an average of around 59% but in this we got only 51% We normalize the value to 59% to make a comparison. So as you mentioned, strength is dependent on the microstructural features of the average microstructure? Yes. Resinch regions, you would think that would have an effect if the fiber orientation distribution is twice as broad as the cuffxss Fibers can also have a significant effect on the strength. So you're kind of showing the normalized value that the cup is astoding to the continuous. I'm saying if you had a continuous that didn't have the net structural anomalies, it might be much higher than the strength that you were measuring. Saying the problem that we had in the continuous, like somehow degrading Axiom Prepate, perfect uniform material without stitches or other anomalous resins region. I'm suggesting the strength of that material will be higher then the stitch value. Yes, it should be higher because it doesn't have any issues within the microstructure. That means your aspect ratio range is falling short of that as you were to get here in them. Yeah, but we're not stopping at this point so this is like the higher at the major aspect ratio that we tested so far. But what we thought is if we did, if this bar is like going somewhere higher, we would have gone somewhere towards this side too. You're saying that dash was another 20% higher than you would be 20% lower than the continuous fire results. You would say, I need to go to even longer aspect ratios. Yeah, because I mean, in that case, I mean, yes, because if we say that this body is like maybe 20, 20% higher, then we don't have enough load transfer here. Like composite is failing for some whatever the reason maybe like interface failure or reasons why it fall short. But you know, you also have limits on how long the fire review line in the process. Uh, so what was your length? Longest th again, it's 7.2 millimeter. Yeah. So you're getting up there to the limits of looks? Yes. Yeah. I mean, eventually that's going to be a problem because it's going to have that issues with alignment and also maybe curling of the fibers or like maybe bending over the fibers. These are all the cases that breaking, breaking of the fibers. Yeah. You have mentioned a couple times interface stuff. What was your interface property? You went through that slide, what did you measure for your interface? It was around Tai make a Asco which is like very less for the agente. That would be consistent for all of these three or four options? Yeah. So the Fib material that we used here is from the same supplier and the same machinial. Everything is tested for everything. Why do you think you interface property as well? So the resin system I was using is having a different composition than the regular epoxy system. I think it is an hydride based or something. I'm not yeah, so that's the reason I was expecting there is a drop in the interface property. It eventually has these issues moving forward, you have plans to look at it with epoxy system which will be Yes, I do. So as you see here, the interface that we as with the resin system, we process this panel, they don't ty megapas, we are thinking to change the resin system to the N epoxy system to see if this has raised. And then we'll just go forward. The first step would be to check the interface properties for the immune system. We is that in progress? Not yet, but I'm going to start soon. Okay. Okay, that would be my first suggestion. And you're very quick to say interface property, the reason why you're seeing the aspect ratio change. Right? But you would also expect a change in the strings. I mean, what aspect ratio? Simply because of So how do you know who did? Yeah, Would you would you understand what I'm saying? You kind of contradict yourself. Why red, you know, premises and based on the curve you showed at the beginning, right? You would expect an S curve associated with the change in the aspect ratio. All right? And one of two things could be the case. If the interface is driving all this, then, you know, you put this, you improve your interface and then there won't be any change because you're looking at the wrong part of the aspect, right? The other thing is the interface is kind of a wash. Everything is up or down and you are seeing a changes due to where you are on the esker. So that's kind of where I'm real. How do you know what part of the S curve you sample with that fairly narrow range of aspect? So I mean, when I say the degradation strength is not just, I mean a result of just one factor like interface, but we also have the other things like the defects within the fiber itself. So it's not, I mean the fiber strength is not same across the whole length, like it has some defects. So that could be one of the reasons why there is a degradation in. Correct. So what you're telling me, there are a bunch of different reasons that you can't decouple. Yeah. So why are you showing us this data? The data that I'm showing here is like the average value that I tested for the fiber strength. So if you look at the plot here, if I try to take the fiber strength value to be like on its lower bound, I mean this bar should actually come down right, like issues with the volume fraction. I mean, we're just taking the average volume fraction. If you just try to change those two values, this plot isn't exactly because that depending on the inputs that I'm try to change them. I mean that should definitely come down. You measured in response to the resin strain, the failure. So for the reason we haven't done it, I mean, for the reason I was using the values from the data sheet was the data sheet say 90 megabases? 90 megabases for the metric strange failure was, I don't remember on the top of my head but it was greater than that of the fiber? Yes, it is greater than the file. Do you have plans to measure the resin system rest tensile or are you going to look at it? I think we're planning to do it with compression on the tension. Why probably the sampling issue? I don't think it's easier to test Tens. Talk about that a little bit. Tensile testing application, it's very sensitive to flaws on the surface, and you may fail prematurely, whereas you do compression, you can get the whole. Okay. Yeah. Did you have to look at your bail surface to see what it looked like? Bail specials, They are like some pullouts but I think I didn't get it here. But they are like pull out for the shot fiber component. Were you asking for a pull up test jack or at the at the actual deposit? Failed. Did you do the SEMs with the failed? Certain. No. No. Not sure. Might be work. You still have samples? Yes. I o that's the right answer s I don't know Jack correctly. But you fail the black fiber systems, fail the explosive pad that we see for the present. Yeah. I mean, more strain the O system that failure, the more exposure that makes it harder to actually depending on what a realistic failure mode is. And that is not affected by the rapid track propagation of explosion. But at 59% of your fibers are getting close bets, 1% And the stress concentration fiber ending is dramatically increased, The distance between the fibers decreases. You either get interfacial failure to the high shear, interfacial shear stress of the fiber end or you'll get a matrix crack if the resin has a low strength failure to old. Thank you so much. And the last speaker sails second year CD. Talking with Dr. Advani, like he's talking about the fiber plustering effect on void formation during the idea manufacturing of continuous fiber composites. Hello everyone, I'm sharks. I'm a second year Phd student in the mechanical department. Today I'll be talking about one of the research work that I'm doing right now which is modeling the effect of fiber clustering and resin biding on the wide formation during IT manufacturing process of continuous composites. And then at the beginning, first I'll be discussing the painting process that we are following. We are basically using dry carbon fibers and we are doing in epoxy impregnation, and then we are doing dual cladding. Finally, the ubiquing is done so that we can maintain the shape of the final painted products. Here we want the final printed parts to be void free. What we're trying to do is like looking at it formation at various stages. At the beginning the past stage was after the first epoch impregnation and then stage two was after the compression roller pressure is applied. And finally, at the final painting, when the painting is done, what we did, we actually cured those samples and finally analyzed through the microsit. We can actually find out the wide percentage at different stages. Basically what we are finding that at different stages the amount of void was different. Our project objective was like identify the results for void formation and develop the models to identify important material and process parameters. First model that we worked on is like understanding the fiber clustering effect on It formation. And second, the model two was resin bleeding effect on void formation. Finally, we tried to do some experiment to validate the models that we worked on. Before start talking about the modeling, we used limbs here for doing the modeling part. We actually have to do some part design and machine. We used soil works and other machine software for this. Then we have to import those data into the Lem Y and then we can actually get the materials and process parameters and then we can actually run the stimulation and get the post process From that you can find out the filling patterns, pressure at different stages and also the field pat, the simulation itself. First, before doing the simulation, we try to come up with a process number. This is basically derived from the Daric law. Then what this process number is, basically giving us an idea about different material properties and how it affects the impegnation. We can see that higher permeability, high pressure, and more time actually reduces the void content because of like having more proper impregnation. Now I'll be discussing about the first model which is fiber clustering effect. When we're doing like pulling the fiber to, through the whole system for different pulling speed or the tension applied on the fiber. There might be some clustering happening inside the fiber to due to the effect like there are some dual non uniformity in the geometry. Then we try to model this inside the and see how it affects the void formation. Our first model was to look at how the clustering happens here. The darker resumes actually shows that this is a cluster resume and it has lower permeability than the bulk permeability on the other parts of the fiber. To then we simulated. We can see that there are some void trapped after the simulation is done. Then we also try to look at how the clustering location effects on the void formation. And also look at how multiple clustering actually affects the void formation. How their location actually affects the whole. Here I'll be discussing the results at the beginning. Before we have done the simulation, we tried to look at like if we have variability in permeability at each of the, at different locations. We tried to do that here you can see at different colors, actually there are different permeability rhythms at different elements. And then we saw that if we have uniform permeability and the process number of one, in that case we'll be getting void because of the proper ipagnationf different permeability values that varies. In this case, we'll be trapping some voids at the end. And there was like a significant amount of it was in prepped. We can see that more variability actually leading to higher void content. Then from the model, we try to look at how the different clustering length and also the clustering location actually affects the void formation. As you can see from the results here, if we have the clustering length to be higher, in that case the void formation was more. Also if the clustering was closer to the injection point, which in our case at the bottom, we can trap more void like at the end of the impregnation. For validating this model, we try to look at the confocal image and look at the pier volume fraction. And from that we actually calculated the prime v values at different location and try to see if it matches the result that we have got. Here, We actually showed a different clustering area. We are actually having different volume fraction variants. Finally, we try to import those parameters that we got from the experiment and see how much gold we are getting actually inside the model. These two a differs a little bit because in the experiment we are basically doing the experiment for de composite, but here we are in the model, we are not actually using any Q. Second model was basically about the sin bleeding. This happens at different stages after the impregnation is done after stage one. We can see after stage one we can actually have some unfilled resumes, which after the compression and completion is done, we can actually fill some of the resumes there, or maybe some of the sin can lead out from those. That depends on the amount of the completion stage, actually depends on the amount of ubiquuring that happens on the outside. The first model here, what we try to do, we try to see if the whole top surface is covered or the whole surface is covered with UB. In that case, what will happen and also if there are like the ubiquing different amount how the bleeding will happen. We have also implemented some one day elements so that we can actually captured the Ted resin. Here we have passed before going into the simulation of track to do the mass balance check when we're applying the compression. Each node here will be having some flows. You are trying to see if the final matches at a 0.43 second. We are supposed to have like 43% So as you can see we have that finally we. Did the balance check and also we actually saw if the initial ions are filling up or those ions are actually moving around inside the fiber. Here I'm basically talking about the two models with 100% of UB covers at the top and also at 75% coverage. Here you can see when there was like the poll covers, 43% was supposed to be filled and it was filling 43% but if we have like less U covers in that case some of the resin can bleed outside and it was like 10% of the initial T regions. We also captured those, check those phrasing bleeded resins with one elements. Here we try to look at if we have 75% and 50% of U cover, how much bleeding we can get. We also looked at the size at basically different amount of different size. Oily taking different time, higher void taking more time to fill up. Also we also looked at the aspect ratio of the void, higher ratio of vida, higher time to fill up. These are the basic key takeaway from the model. Larger viability in permeability, clustering length, and also the clustering location is important. And those large guides, basically the large amount of size voids are taking more time to filling up. Also, pulling speed is important because at different speeds the tension might be varying. And high spatial, lower time to fill for the future work. We are trying to validate the raising biting model and also work a little bit more on the pierclustering models we've been working on. Thank you. Any questions? So we talk about private clustering. Do you have any statistics? Any idea how those because you said that you kind of verify that model. So how do you describe the size of those clusters in real and experimental data? In numerical model? It's quite obvious, but yeah, we actually try to here, we did some confocal image, we took some cross section and tried to find out the clustering location. What we did, we take different sizes, basically 20% of the total area. Here you can see the clustering location was 100% basically the whole. In that case, there will be no variation inside the volume fraction. But if we divide it into two, basically just divide it into two in such a way. In that case we can have a amount of fiber volume fraction variation. But in that way, if we keep increasing the window area or the clustering area, in that case we'll be having like the different fiber volume fraction at different locations. I tried to what I did, try to take 25% of clustering area and try to see what is the maximum fiber volume fraction we can get and also the minimum higher volume fraction. And from that, I actually try to look at a quick question. How do you define the clustering area? How do you define the cluster here? As you can see, I can take different clustering area here, so I can even take 5% and see how much variation I can get between the minimum and the maximum fire volume fraction. And then I can use the to verify the models. I'm not sure I understand what you're talking about. Varying your sample area making measure. Yeah. So I can take different areas. Although window is measuring maximum and minimum volume praction depending on the area, it's not a size of area over which you can expect. Those are the side of the windows. Yeah. All right. So I guess like he defines some windows for image processing and at each window that he measures the fiber volume fraction. That's the race. That's what I was trying to do. I understand that. Right. We've got a really low window size. As you get changes, we get up to about, let's say 40% of your area. Things kind of plateau out and constant. So how do you use that? I'm, I guess how do you use that information to validate your model? So from here in our model, we have to define a certain area of the question. Also you have to input, there are some inputs about what will be the low permeability and also the permeability. Here, what I'm trying to find out in the actual fiber to how much will be the variation in the fiber volume fraction. If I take, let's say 25% of the clustering area. In that case, what will happen? There will be a variation between the fiber volume fraction in the maximum and the minimum. I'm using this information to find out the permeability and also the variation in the permeability u, your volume fraction is the average volume fraction have a volumetion rate. They just, if you just look into those clustering area that's digital to very small, one will come to T volume fraction. And the question for the size would be how many of those really small clustering areas are next to each other. That may give you some idea about how fast is some sort of a side because it's, it's not going to be very smooth function, but it's going to be set. Correlation in one place is high volume fraction. The next one is probably more likely to volume fraction than age. I guess I'm still struggling with this. If I make my sample area three by three microns, I have areas that are 100% fiber volere that are 100% average it. At the end, what you could do is to select a window. And using the window going point by point, plot volume fraction, which is averaging the window over. This shows you a peak in volume fraction. You might figure out how high, how large it, but if of course will depend on how small your window will be set right. So for example, if your clustering area would be like one pixel, probably like the red color, the red box you show must be 100% and the VP mean would be 0% right if the size of your window is on pixel. Another approach that you could look at, we're talking about understanding poster size of fibers. You can go in and you can say, okay, I put my pin on one fiber, what is the number of fibers I see in that area? And then I go across my entire area and see how that varies, right? To have a reference point. And then, and then you can like expanding out that way, right? Sampling at basically correlation function might be a little more. The only question has, the question I had was about like in your modeling, how do you decide the location of there was a graph, I guess early on that you showed the look at this one. Yeah, like the one, right? So you just randomly decide how do you, so change de, terminability of the elements. So here, right? So here we actually try to look at like I tried to change the primary, this location here by, so we can actually vary this different location. And I tried different location and average value here. So All right, this model take care of the musk observation in the sense that the overall volume fraction would be always same. In this model, you are just the volume fraction. Inclusion. Yeah, The overall volume fraction the same. All right. Thank you so much everyone.
February Session - CCM CONNECTS: Pioneering-Innovation-Excellence / Research Series
From Kristen Scully March 05, 2024
8 plays
8
0 comments
0
You unliked the media.
Paul Samuel, (Ph.D.M.E.)
Graduate Student
Prof. John W. Gillespie, Jr.
Experimental Measurement of Mode-I Fracture Toughness and Traction Laws for Delamination in Plain Weave Composites
Seyda Naz Alasahin, (Ph.D.E.C.E)
Graduate Student
Prof. Erik Thostenson and Prof. Mark Mirotznik
Utilization Of Carbon Nanotube Based Sensors To Monitor Resin Flow In Resin Transfer Molding
Hasan Ulus
Visiting Scholar
Prof. Monique Head and Prof. Suresh G. Advani
Effect of Particle Size and Replacement Ration on Mechanical Performance of Concrete Containing Ground-Recycled ABS Waste Plastics
Zoom Recording ID: 96297013600 UUID: GK+AxDIcTnmzXHM4lE3N3A== Meeting Time: 2024-03-05 03:36:40pmGMT
Graduate Student
Prof. John W. Gillespie, Jr.
Experimental Measurement of Mode-I Fracture Toughness and Traction Laws for Delamination in Plain Weave Composites
Seyda Naz Alasahin, (Ph.D.E.C.E)
Graduate Student
Prof. Erik Thostenson and Prof. Mark Mirotznik
Utilization Of Carbon Nanotube Based Sensors To Monitor Resin Flow In Resin Transfer Molding
Hasan Ulus
Visiting Scholar
Prof. Monique Head and Prof. Suresh G. Advani
Effect of Particle Size and Replacement Ration on Mechanical Performance of Concrete Containing Ground-Recycled ABS Waste Plastics
Zoom Recording ID: 96297013600 UUID: GK+AxDIcTnmzXHM4lE3N3A== Meeting Time: 2024-03-05 03:36:40pmGMT
- Tags
- Department Name
- Center for Composite Materials
- Department Division
- Date Established
- February 07, 2024
- Appears In
Link to Media Page
Loading
Add a comment