Simulation has nearly limitless applications and has revolutionized engineering developments in manufacturing.
The idea of trials and errors in real life is from the past and could not be legitimized to today’s management considering the massive cost and loss of competitiveness that would result. However, simulation, which allows engineers to experiment with a variety of scenarios within a simulated computer environment, has almost limitless applications and has revolutionized engineering developments in manufacturing across all industries. Simulation occurs at several steps in the manufacturing process, from mining to the assembly line.
Engineers must learn from experience, which is usually gained on the job. With the workforce facing massive disruptions because of retirements, however, the question becomes: How do companies get new engineers up to speed quickly while allowing them to gain as much experience as needed to do their job efficiently? The answer is simulation.
Simulation software provides the best compromise of speed learning and cost efficiency. Engineers can test out ideas at almost no cost (other than their time and the timeshare of the license cost), create their own database of cases to reference later and gain experience and confidence in their judgment. Hopefully, some overlap with experienced engineers is ensured before retirement so that they can learn from decades of experience on the ground.
This article proposes a case study for large ingot transformation using software from the Transvalor Software Suite: THERCAST for ingot casting simulation and FORGE for solid metal transformation.
The Case Study
This case study was developed in our lab. While designs and parameters are similar to industry standards, the data is not coming from an industrial case. We suggest investigating two methods of ingot transformation and analyzing the quality of the semi-product after transformation for each method.
After initial ingot filling and solidification, the ingot is reheated in a furnace and submitted to a cogging process. The cogging process is a set of manipulators (holding the ingot) and two tools applying the deformation to the ingot. The goal of this transformation is usually to obtain a semi-product to be shipped to a customer for further transformations, such as closed-die forging. Cogging can also be used for large finish products, such as turbine large shafts.
This study focuses on the shape of the cogging tools. The goal here is to transform an ingot into a round product. We explore a set of tools in a V-shape and a flat-surface shape. Using the same amount of deformation for each tool design, we want to figure out which shape provides the best product quality. The product quality in this case is the reduction of the porosity in the centerline of the ingot, which is the typical defect from casting that engineers try to minimize during this operation.
Another analysis engineers and metallurgists are interested in is the grain growth and size after trans-formation. While this analysis is available in Transvalor Software Suite, we will not focus on this analysis in this article.
Description of Project
The first step is the filling and solidification of the ingot. The ingot chosen weighs 1.6 tons and the material used is 40CMD8. Figure 1 shows the setup with all the concerned parts of the casting: riser, mold, hot top, refractory and exothermic powder.
Fig. 1. This ingot casting setup shows all the concerned parts of the casting: riser, mold, hot top, refractory and exothermic powder.
Once the ingot is solidified, it is reheated in a gas furnace before being carried out by a manipulator to the cogging operation. Figures 2 and 3 show the description of the V-shape and flat-shape tools used in this study.
Fig. 2. This figure provides an example of the flat-shape tooling measurements used in this study.
A number of blows occur during the cogging process. A blow is the full cycle of the top tool coming down to reach a specified height and moving back up to its original position. In order to transform a tapered, hexagonal-shaped ingot into a round section bar, a number of rotations and displacements must occur during the full process. Cogging is also described by a number of passes. In this study, a pass is the full operation to go from one end of the ingot to the other. Both setups were created to be comparable. The parameters used in both cases are described below:
- Number of blows (eight for a full rotation)
- Rotation angles (22.5 degrees) and direction
- Amount of deformation
- Displacement between blows
- Number of passes
- Friction (no lubrication)
- Initial temperature set to 2282°F (1250°C).
Fig. 3. Shown here are the V-shape tooling measurements used in this simulation study.
We ran two passes for this case study, going back and forth. In addition, we set some sensors (point tracking) in the areas of interest, namely the centerline. Figure 4 shows the position of the group of sensors. The sensors will follow the material deformation and record all the results provided by the simulation, allowing for a thorough analysis of the process.
Fig. 4. For the study, point-tracking sensors were set in the areas of interest, namely the centerline. This figure shows the position of the group of sensors.
As expected, the results of the casting show porosities in the centerline. The porosities in this case study are detected using the Niyama criterion, which is a very common criterion used in the industry. Since the top and bottom of the ingot are discarded for the final product, we focus our study on the centerline to make sure we apply enough strain in the center to reduce the amount of porosity.
Fig. 5. Shown here are the initial setup configurations with V-shape on the left and flat-shape on the right.
Figure 6 shows the liquid fraction at the end of the filling. The dark blue represents the solidified metal, the red color is the liquid and any other colors are the mushy zone. The mushy zone is a mix of solid and liquid, starting with the first dendrite formation and ending with the fully solidified zone. It demonstrates that the solidification starts well before the end of the filling and that the solidification phase is indeed a continuity of the filling phase. The solidification is a continuous process starting as soon as the liquid is poured in the mold. It is essential for the simulation software to consider it to avoid discrepancy between simulation and reality.
Fig. 6. The results of the casting show porosities in the centerline. This figure shows the liquid fraction at the end of the filling phase.
The V-shape method shows a good amount of strain in the center and a smooth outer-diameter sur-face. Figure 8 shows a longitudinal cross section of the ingot after two passes with the strain distribution. The dark blue represents low or no strain, and the red color shows the highest amount of strain. Figure 9 provides information on the strain level and evolution over time for each sensor positioned on the centerline of the ingot.
Fig. 7. The porosities in this case study are detected using the Niyama criterion, a very com-mon criterion used in the industry. Shown here are the porosities analysis for this study.
The V-shape presents good-quality results regarding the goal of our study, which is to diminish the level of porosities in the centerline. The level of porosity is evaluated by a model considering the existing porosity and the level of strain received during the cogging transformation process. Figure 10 shows the porosity assessment after two passes in the longitudinal cross section.
The flat-shape method shows a somewhat more localized amount of strain on the surface of the transformed ingot. The surface is also smooth on the outer diameter. Figure 11 shows a longitudinal cross section of the ingot after two passes with the strain distribution. The dark blue represents a value of 0 strain (no deformation occurred in these areas), and the red color shows values of strain around 10. Figure 12 is a chart of the strain evolution over time in the centerline where our sensors were originally positioned.
The flat-shape tooling seems to suggest that less strain is applied to the center of the ingot. Since the goal of this process is to reduce the amount of porosity in the center of the transformed ingot, it looks like the flat-shape tooling is not ideal. Figure 13 provides more information on this matter with the direct information of the level of porosity reduction. The dark blue (0) shows no porosity detected in these areas, while the red color (1) shows a non-reduced porosity, as initially detected after the casting. Any color in between blue and red suggests some level of reduction.
Fig. 8. The V-shape method shows a good amount of strain in the center and a smooth out-er-diameter surface. This figure shows a longitudinal cross section of the ingot after two passes with the strain distribution.
Fig. 9. This figure shows a recording of the sensors positioned on the centerline of the ingot and provides information of the strain level and evolution over time for each sensor posi-tioned on the centerline of the ingot.
Fig. 10. The level of porosity is evaluated by a model considering the existing porosity and the level of strain received during the cogging transformation process. Here we see the po-rosity assessment after two passes in the longitudinal cross section.
Fig. 11. The flat-shape method shows a somewhat more localized amount of strain on the surface of the transformed ingot. The surface is also smooth on the outer diameter. This fig-ure shows a longitudinal cross section of the ingot after two passes with the strain distribu-tion.
Fig. 12. This chart records the strain evolution over time in the centerline of the ingot where sensors were originally positioned.
Fig. 13. Shown here is the porosity assessment in longitudinal cross section.
Fig. 14. Since it is complicated to compare all sensor results at once in the same chart, we have extracted one sensor located in the middle of the centerline and plotted both sensors in the same chart. The comparison shows that the sensor from the V-shape (light-blue curve) provides a better porosity reduction than the flat-shape tooling (orange curve).
After assessing each method individually, it is time perform a side-by-side comparison of the results and decide which one would best achieve our goal. Since it is complicated to compare all sensor results at once in the same chart, we have extracted one sensor located in the middle of the centerline and plotted both sensors in the same chart. The comparison shows that the sensor from the V-shape (light-blue curve) provides a better porosity reduction than the flat-shape tooling (orange curve).
The V-shape tooling achieved a better movement of material (strain) in the centerline of the ingot. This is important because it is the main objective for porosity reduction. Figures 10 and 13 show that it translates into a better porosity reduction for the V-shape tooling. While the flat shape can certainly get to the same level of reduction as the V-shape, it will most likely require more passes than the V-shape to accomplish the same results. The addition of more passes has a significant cost for the company – such as a longer forging cycle to accomplish the same result, higher energy consumption and additional wear on the tooling. This translates into more frequent replacement of the tooling, which ultimately adds to the overall cost of transforming the same ingot.
The analysis shows that the V-shape is a better fit. However, it must be considered that a V-shape tooling may require a company to order a new set of tools while flat-shape tooling might be available. Factoring all this information, the company may decide that the cost involved in the cutting of new tools is not worth it for a specific job. If the company is looking for a long-term investment strategy with multiple jobs in the pipe, however, a V-shape tooling makes more sense because it will be more cost effective in the long run.
The goal of this article was to demonstrate the benefit of using simulation in the engineering department. The case used here is leaning toward decision making for process development and investment, but this analysis can be done at multiple levels. Most companies use the simulation to answer re-quests for quotation and ensure that their bid is the best possible considering the company’s capabilities and resources. Development time has been dramatically shortened since the introduction of simulation software, and any companies that are not yet equipped with simulation are losing bids for pro-jects, putting their businesses in jeopardy.
This article provides a snapshot of the potential of simulation. Grain size and tool analysis, such as stresses and temperature, are other important results to consider and are available through simulation. Such analysis would be nearly impossible to do through experiments because of the cost and practicality of such investigation.
Simulation software must be a priority for companies. Software must be calibrated to the company’s processes but come with ready-to-go parameters for an immediate start. Companies not yet equipped should urgently investigate what simulation software would work best for them and make sure to pro-vide enough training to their staff so they can use the simulation in the best conditions and get the most value.
For more information: Nicolas Poulain is Director of Sales and Technology at Transvalor Americas Corp. (www.transvalorusa.com). He can be reached at 312-219-6029 or email@example.com.
All images provied by the author, except where noted.