Functional Generative Design: An Evolutionary Approach to 3D-Printing

Consumer-grade printers are widely available, but their ability to print complex objects is limited. Therefore, new designs need to be discovered that serve the same function, but are printable. A representative such problem is to produce a working, reliable mechanical spring. The proposed methodology for discovering solutions to this problem consists of three components: First, an effective search space is learned through a variational autoencoder (VAE); second, a surrogate model for functional designs is built; and third, a genetic algorithm is used to simultaneously update the hyperparameters of the surrogate and to optimize the designs using the updated surrogate. Using a car-launcher mechanism as a test domain, spring designs were 3D-printed and evaluated to update the surrogate model. Two experiments were then performed: First, the initial set of designs for the surrogate-based optimizer was selected randomly from the training set that was used for training the VAE model, which resulted in an exploitative search behavior. On the other hand, in the second experiment, the initial set was composed of more uniformly selected designs from the same training set and a more explorative search behavior was observed. Both of the experiments showed that the methodology generates interesting, successful, and reliable spring geometries robust to the noise inherent in the 3D printing process. The methodology can be generalized to other functional design problems, thus making consumer-grade 3D printing more versatile.Comment: 8 pages, 12 figures, GECCO'1

Efficient Sampling for Design Optimization of an SLS Product

In this work an efficient constrained surrogate-based sampling algorithm is implemented to optimize Selective Laser Sintering (SLS) process parameters for maximizing the tensile strength of a tensile specimen. Two variations of the algorithm have been implemented and tested on a Farsoon HT251P machine using (polyamid) PA3300 polymer powder. The algorithm is based on building a statistical predictive model of the objective response (i.e., maximization of tensile strength), aggregating the constraint function (i.e., limited amount of warping), in an iterative manner by simultaneously improving the accuracy of the predictive model as well as searching for the optimum set of process parameters. The difference in two algorithmic variations is the number of samples to update at each iteration. While the first method is based on a single sample update, the latter searches for multiple simultaneous updates to let the manufacturer try several potentially good sets of parameters in the same machine to eventually speed up the experimental evaluation procedure.Mechanical Engineerin

Constrained Efficient Global Optimization for Pultrusion Process

Composite materials, as the name indicates, are composed of different materials that yield superior performance as compared to individual components. Pultrusion is one of the most cost-effective manufacturing techniques for producing fiber-reinforced composites with constant cross-sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters. Validated computer simulations cost less as compared to physical experiments, therefore this makes them an efficient tool for numerical optimization. However, the complexity of the numerical models can still be “expensive” and forces us to use them sparingly. These relatively more complex models can be replaced with “surrogates,” which are less complex and are therefore faster to evaluate representative models. In this article, a previously validated thermochemical simulation of the pultrusion process has shortly been presented. Following this, a new constrained optimization methodology based on a well-known surrogate method, i.e., Kriging, is introduced. Next, a validation case is presented to clarify the working principles of the implementation, which also supports the upcoming main optimization test cases. This design problem involves the design of the heating die with one, two, and three heaters together with the pulling speed. The results show that the proposed methodology is very efficient in finding the optimal process and design parameter

Probabilistic analysis of a thermosetting pultrusion process

In the present study, the effects of uncertainties in the material properties of the processing composite material and the resin kinetic parameters, as well as process parameters such as pulling speed and inlet temperature, on product quality (exit degree of cure) are investigated for a pultrusion process. A new application for the probabilistic analysis of the pultrusion process is introduced using the response surface method (RSM). The results obtained from the RSM are validated by employing the Monte Carlo simulation (MCS) with Latin hypercube sampling technique. According to the results obtained from both methods, the variations in the activation energy as well as the density of the resin are found to have a relatively stronger influence on the centerline degree of cure at the exit. Moreover, different execution strategies are examined for the MCS to investigate their effects on the accuracy of the random output paramete