Producibility Index and Orientation Optimization for Additive Manufacturing
Product: Siemens NX, NXOpen
Additive manufacturing (AM) process is a layer-based method to fabricate a part by depositing material on top of each other. When manufacturing a part using AM, the designer has several different build orientations to choose from making the process of choosing the best or optimum build orientation difficult. And sometimes the best building orientation is not intuitive, as one building direction may reduce the cost of material consumptions but increase the total building time, while one may reduce the building time but compensate the geometry accuracy.
The Center for Global Design and Manufacturing (CGDM) at the University of Cincinnati (UC) have developed a set of pre-processing tools that can identify the best building orientation from the point of view of manufacturability based on the requirements of the designer, as shown in Figure 1 and 2. A custom designed GUI is provided for users to freely define the problem based on different manufacturability criteria based on the part geometry.
This tool can optimize the build orientation based on various Design for Additive Manufacturing (DFAM) constraints. Such DFAM features includes small openings, thin features, sharp corners, thin to thick transitions, recoater arm collisions, thin walls, cusp errors. The tools also consider various support parameters including support volume, support contact area, down facing area not requiring support, support removal accessibility, build height and build time. Producibility index (PI) combines all the design parameters that are mentioned into one single knowledge base to increase the manufacturability of the designs. It’s the measure of goodness of a design’s manufacturability in a build orientation. For each orientation, it will calculate all the parameters listed here and gives one value.
University of Cincinnati have developed an algorithm to quantify each design features with variable units and orders of magnitude into same scale. Each feature has a weight input by the user, the weight indicates the importance of the features in the final PI score. The final PI score is calculated by evaluating each feature and combining with the user input weight. The highest PI value indicates the orientation with the best manufacturability considering all the design features as shown in Figure 3. The orientation optimization algorithm will rotate the part through 360 degrees on the x and y axes in user-specified increment angles and find the best candidate orientations with highest PI scores.
First step in orientation optimization is to setup analysis parameters. A custom GUI is provided in NX to allow the designer customized the parameters for the analysis. The User can input candidate building orientations that need to be analyzed, threshold values for DFAM features, support structure parameters, machine parameters such as hatch pattern and layer thickness, and weight of each features.
Then, Using the algorithm developed by UC implement in Siemens NX with NXOpen. The tool starts to analyze each feature and returns quantified raw values for each orientation until all the candidate orientations have been analyzed. Then PI score of each candidate orientation is shown as well as recommend best ordinations.
User’s weights input plays an important role in determine the best building orientations. Depending on manufacturing requirement, the user can input different weight factors to each individual feature. As result, the best building orientation will change accordingly that could achieve optimized manufacturability.
For example, if the goal is to find the best orientation for orientation for least amount of support structure parameters and build height. Then designer may choose to give very high weight to the support volume parameter, support contact area parameter, build height and build time parameter, support accessibility parameter. However, if the designer cares more about geometry accuracy and least distortion in the part, then the higher weight value should be given to features such as small openings, thin features, thin to thick translons, sharp corners, thin walls, recoater arm collisions etc. Figures 4 and 5 show different best building orientations with different weights input.