Automating Injection Molding Simulation using Autonomous Optimization

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1 Automating Injection Molding Simulation using Autonomous Optimization Matt Proske, Rodrigo Gutierrez, & Gabriel Geyne SIGMASOFT Virtual Molding Autonomous optimization is coupled to injection molding simulation to help molders make more informed decisions with less manual labor. The concept is to automatically create and calculate hundreds or thousands of simulations within a predetermined design space of millions of possibilities to learn about the relationship between final part quality and the material, mold, process, and molding machine. Instead of running ten simulations with an operator, the software is running hundreds or thousands of simulations on its own. Better decisions are then possible because there is significantly more information to base the decisions on. Obviously, making a decision based on five thousand simulated variations (variants) provides much less room for speculation than basing the same decision on only three or four simulations. With a larger sample size, there is a much clearer understanding of the relationship between the part/mold design and the process. This approach provides even more value to the molder because the software is completing a much larger portion of the work. It s also completed well in advance of building a mold. In a traditional simulation approach, an operator sets up a single simulation, clicks start, and then evaluates the results either while the simulation is running or after it s complete. The evaluation requires time; look at individual results, think about them, decide if improvement is required, decide what inputs to change (probably as a group), make changes, click start, and repeat. The manually iterative procedure continues possibly several times a day for several days until the group is satisfied the result is as good as it will get with the time available. Each set up might take 15 minutes and each review might take another 15 to 30 minutes. Calculation times can vary from as little at 60 seconds or as long as an entire day. Manually operating the software for ten simulation designs can require a full workday of labor, maybe more. The new, automatic approach is quite different. The software operator sets up the initial design which includes objectives, variables, and constraints. (This procedure can also be further simplified using preexisting templates.) The software automatically produces each future variant (simulated design) by changing the process and/or geometry in milliseconds, generates the mesh, queues it up for calculation, runs the simulation, and plots the results of the objectives. The results of all variants are plotted in a single chart so the operator can easily determine which of the variants produce the results which most closely meet their objectives (Figure 1). AUTONOMOUS OPTIMIZATION 1

2 Figure 1 - Autonomous Optimization Operating Space The operator time is reduced from 7.5 hours to 0.75 hours (an improvement of 10x). The decisions of the operator are based on thousands of simulations instead of only a few. This is something like a DoE (design of experiments), which is also possible. However, the main difference is that instead of simulating all of the variants, the software focusses on changes that produce results which more closely meet the objectives. The software knows what can and cannot be changed, how much it can be changed and what the objectives are to avoid creating unfeasible situations. It creates and runs multiple variants over several generations learning about which changes are most desirable. Several variants are calculated in parallel to speed the process. The faster the results are available, the faster the software learns what changes support our objectives. This is a completely different approach for solving problems or achieving new goals when compared to traditional simulation. All molders don t necessarily have the same goals, or constraints however. Some may want to find the lowest melt pressure to fit the largest part into the smallest machine while others may want to achieve the fastest cycle time. Some may have significant latitude on gate location or material selection while others may have none. Autonomous optimization may not be able to find the solution, but it can explore the complete realm of what is allowed to determine the best possible scenario. At least a molder can determine what the very best process is, how repeatable it will be, and what final part quality is attainable. Two very common problems molders encounter are; AUTONOMOUS OPTIMIZATION 2

3 1. Best gate location 2. Fastest cycle time However, there are other objectives too, all involving part quality. Parts must meet dimensional, aesthetic, and mechanical requirements. Multiple objectives are used simultaneously now to ensure molders don t end up with undesirable outcomes; - Lowest pressure to fill a cavity but creates weak weld lines, or - Least amount of trapped air but produces highest melt pressure LSR Melt Pressure, Gate Location, Air Entrapment Figure 2a & b - Melt pressure progression during fill shows trapped bubble For LSR production, cavity pressure at the parting line is critical because the material viscosity is typically very low and the material can flash the parting line relatively easily. The pressure rises as the cavity fills so gate location is directly related to the cavity pressure. Figure 2 a, b shows melt pressure rising as cavity filling progresses. The challenge for this product is to determine if there is a geometric solution for the trapped air, i.e. new gate location. The autonomous optimization begins with the identification of the objectives, variables, and constraints. Objectives are to minimize air trapped in the cavity during fill. It is described to the software as minimize: air entrapment where air entrapment is a specific simulation output. The results is based on the temperature and pressure of trapped air bubbles and is resolved against the liquid melt pressure to calculate if melt pressure rises above the air pressure, it will begin to dissolve into the flowing LSR before curing initiates. Notice the location of the air entrapment and the way in which it becomes trapped. Also notice the trapped air bubbles being squeezed out of the initial air pocket and into the melt stream (Figure 3 a, b). AUTONOMOUS OPTIMIZATION 3

4 Figure 3a & b Air pocket on actual (left) vs simulated part (right) The variables in this design will be limited to three; two geometric and one process. Geometric: vent locations, gate location Process: Filling time (6s and 8s) The two filling times are added to the optimization to make sure the best gate location can also absorb some process variation. Since LSR is susceptible to jetting (Figure 4) due to its low viscosity, the best gate location must still be valid while being subject to variations in viscosity. This objective is named minimize: free surface. Less free surface of the melt will always come from a design with less jetting. Figure 4 - Jetting effect due to LSR low viscosity AUTONOMOUS OPTIMIZATION 4

5 Changes to the vent locations must also be considered (and coupled to the gate locations). This avoids situations where the software finds potentially optimal solutions to the filling pattern but disregards them due to improper venting. The gate locations are also variable, but limited to certain areas of the part. In this case, a trajectory line (path) is extracted from the part geometry and used to keep the definition of where the gate can be, relatively straightforward (Figure 5). The gate can move anywhere along the line in 1 degree increments. Since the beginning and ending positions of the gate cover 240 degrees, there are 240 different potential gate locations which the software can use to find the optimal gate location. The vent locations are dependent on the gate locations so additional designs are not required for this variable. Two filling times are combined with 240 different gate locations for a total design space of 480 possible variants. All other inputs for this design were constrained (melt/mold temperature, gate size, material grade, etc. Figure 5- Potential Gate Locations The autonomous optimization runs multiple designs in parallel for each generation and then runs multiple generations, seeking the gate location which provides the lowest air entrapment within the 6-8s range of filling times. Figure 6 - Scatter chart showing each simulated design vs maximum air trapped pressure AUTONOMOUS OPTIMIZATION 5

6 The software has found the best gate location after creating and simulating 96 possibilities. All of the designs are shown in a scatter chart (Figure 6), plotted against the objectives of lowest air entrapment and lest amount of free surface. The best design is identified and exported. The free surface and air entrapment results from the best design and shown and compared to the original design (Figure 7 a, b). Figure 7 a & b - Cavity pressure comparison from original (left) vs optimal gate location (right) The calculation time for the autonomous optimization was 2 hours and the labor to set it up was only 30 minutes. In contrast, using a manual approach with both filling times, it would have taken at least 24 designs to be within 10 degrees of the optimal location. Even at only 20 minutes (of labor) per design, it would have required a full workday and it would still not be as good as the optimal. If it were as good as the optimal, it would have been by accident and the autonomous approach consistently finds more optimal solutions, purposefully, using larger sample sizes and basing decisions on data. The calculation time is extremely inexpensive compared to the cost of labor; the average hourly cost for software and hardware using a three year amortization schedule is only $5.00 and it works 24/7. The average hourly cost of a trained software operator is $50.00 (including salary, health insurance, retirement, unemployment, SS, etc). So, it makes complete sense to focus on reducing the human labor factor since it s 10 times greater. It also makes sense to focus on software development which computes more quickly. As more data is required for software to make decisions, more simulated variants are required to improve the accuracy of these decisions. However, this approach is statistically proven to find better solutions. Most molders use live molding trials and DoE to make these data-driven decisions already. The cost of a live DoE trial (8 hr sample including start-up and shutdown) ($1,000.00) compared to a virtual molding trial ($50.00) vary greatly and will help molders achieve levels of profitability not previously possible. AUTONOMOUS OPTIMIZATION 6