SmartSlice™ Validation Series - Part 3 - Optimized Print Settings

One of the most powerful functions in SmartSlice™ is the Optimize tool. An enhancement to the Validate tool, which evaluates the performance of an as-printed geometry, the Optimize tool treats the major print settings as variables and uses a sophisticated optimization algorithm to iterate through many print setting configurations with the objective of meeting performance requirements while minimizing print time and material usage. In other words, the Optimize tool automates the process of finding the best print settings for your application.

This is the 3rd installment of our SmartSlice™ Validation Series where we compare SmartSlice™ to experiments. In this post, levers with print settings optimized by SmartSlice™ are 3D printed and experimentally tested, and the results are compared to predictions from SmartSlice™. The 1st study in this validation series examined raster orientations using tensile specimens and the 2nd study considered the effect of build orientation.

How We Validate SmartSlice™ with Optimized Parts

The SmartSlice™ Optimize workflow is simple and involves selecting a material, orientation, and defining a use case and requirements for the part. The material in this study is 3DXTECH CarbonX™ CF-Nylon. The use case consists of selecting surfaces that are anchored (surfaces that can not move) and surfaces that are loaded (surfaces that are being pushed or pulled) and the requirements are stiffness and strength targets for the part. In this study, the print settings for a lever are optimized based on the use case and requirements shown in Figure 1. For the use case, the 2 holes on the left are fixed and a 170 N load is applied to the notch on the right. For the requirements, the minimum factor of safety is 2.0 and the maximum displacement is 3.0mm. The factor of safety is best understood as a factor on the applied load that corresponds to the onset of material yielding at any point in the part - which can also be attributed to the amount of risk a user is willing to take. In this study, a factor of safety of 2.0 and an applied load of 170 N means that the part will not begin to yield until the load is at least 340 N (2.0 x 170 N). The maximum displacement is a metric for the stiffness of the part and is the largest deflection (movement) among all regions of the part. The requirements are targets for the SmartSlice™ optimization algorithm which means that all the print setting solutions will meet or exceed the requirements.

validation_blog_3_fig1Figure 1. Use case for the lever showing fixed surfaces (red), the loaded surface (blue), and the optimization requirements.

Based on this use cases SmartSlice™ automatically generates a list of 10 print setting configurations that meet the requirements. SmartSlice™ ranks the solutions in the list based on a combination of print time and uniqueness and in this study, the top 2 ranked solutions were 3D printed and tested. The slice profiles for these parts are shown in Figure 2 and, as is evident, the slice profiles are significantly different. In addition to vastly different wall line counts, the most notable difference is that the Rank 2 solution has local reinforcement. These internal (local) features are facilitated by modifier meshes that SmartSlice™ has automatically added. SmartSlice™ is able to detect regions which require additional stiffness and/or strength, and locally reinforce these regions and in doing so, extra material is added only where it is needed.

validation_blog_3_fig2Figure 2. Slice profiles for Rank 1 and 2 optimized parts

To generate the experimental data, the Rank 1 and 2 levers were 3D printed and experimentally tested in a load frame, and the displacements were measured using a laser extensometer. The part stiffnesses and yield loads computed by SmartSlice™ and from the experimental data are shown in Figure 3. The predicted part stiffnesses and yield loads from SmartSlice2 are within 11% of the experimental values for both print configurations. In terms of part stiffness, SmartSlice™ matches the observed trend of Rank 2 being slightly stiffer than Rank 1. For the yield load, the measured values are the same for Rank 1 and 2 while SmartSlice™ predicts a yield load for Rank 2 that is slightly larger than Rank 1.

Returning to the stiffness and strength requirements, we see that SmartSlice™ can ensure the levers meet a factor of safety that is at least 2.0 and a stiffness of at least 57 N/mm (the stiffness is computed by dividing the applied load by the maximum displacement requirement: 170 N / 3.0 mm). In this example, the optimization is strength driven - meaning that the factor of safety requirement is the limiting factor, not the maximum displacement requirement. In other words, the stiffness requirement is easily satisfied as a result of the strength requirement being met.

validation_blog_3_fig3Figure 3. Comparison of predicted and measured part stiffnesses and yield loads.

The Benefits of Optimizing

There are several lenses through which the benefits of optimizing print settings for a part can be understood. In terms of time savings, optimized parts simply print in less time compared to overdesigned parts - such as solid (100% infill) parts. This time savings can equate to increased 3D printer efficiency, less downtime when a replacement part needs to be 3D printed and decreased operator costs. In terms of material savings, optimized parts use only the amount of material that is needed. In addition to reducing hard material costs, optimized parts mean less wasted material which is important for environmental concerns. There are other applications where print setting optimization is very valuable, such as light-weighting and reducing the development cycle for a part by using simulation to reduce the print-break-iterate cycle.

To illustrate the benefits of optimization, consider the lever studied above. The table below shows the print time and part mass for a solid lever and both optimized levers. Compared to the solid lever, the optimized levers print in less than half the time and reduce the mass by 34-38%. For a single lever, these savings are significant but are even more eye-popping as the part count increases. Assuming a moderately sized production run of 50 levers, the total time savings is 3.15 days and the total material savings is 940 grams. Compound these savings over several different parts throughout the year and the savings become even more substantial.



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Experiment SmartSlice Validation Optimization