Lightweight parametric design optimization for 4D printed parts

Abstract

4D printing is a technology that combines the capabilities of 3D printing with materials that can transform its geometry after being produced (e.g. Shape Memory Polymers). These advanced materials allow shape change by applying different stimulus such as heating. A 4D printed part will usually have 2 different shapes: a programmed shape (before the stimulus is applied), and the original shape (which is recovered once the stimulus has been applied). Lightweight parametric optimization techniques are used to find the best combination of design variables to reduce weight and lower manufacturing costs. However, current optimization techniques available in commercial 3D CAD software are not prepared for optimization of multiple shapes. The fundamental research question is how to optimize a design that will have different shapes with different boundary conditions and requirements. This paper presents a new lightweight parametric optimization method to solve this limitation. The method combines the Latin Hypercube design of experiments, Kriging metamodel and specifically designed genetic algorithms. The optimization strategy was implemented and automated using a CAD software. This method recognizes both shapes of the part as a single design and allows the lightweight parametric optimization to retain the minimum mechanical properties for both shapes.

Optimization methodology for the material assignation in bioprinted scaffolds to achieve the desired stiffness over time

“…The methodology presented in this work is based on a previous approach and inspired by the work of Heljak et al, reviewed in the previous section. The main novelty introduced is the use of metamodels to reduce the computational time needed for the evaluation of the fitness function of the individuals generated during the GA evolution.…”

“…Although this methodology is based on the proposal depicted in Paz et al, some significant modifications were implemented to be able to deal with different time points, material assignation, user‐defined objective functions, and other requirements needed for this specific application. For example, in Paz et al, the GAs were coded for continuous variables, whereas in this case, the variables can be continuous, discrete, or a combination of both. Moreover, the design variables reserved for material assignation are not associated with design variables of the CAD model, but material properties that must be applied to the solids in the FEA.…”

“…The methodology has been implemented within SolidWorks 2016 software by using the Application Programming Interface. After running the code, the main window asks for the output directory and file name, the number of design variables (“n,” which will be used to determine the sampling effort in the different stages of the algorithm), the number of discrete design variables, the number of discrete design variables to assign materials, the number of materials, the number of time points for the degradation process, the number of constraints for the optimization definition, the number of studies (to apply different analysis if needed), the number of configurations (to assess different shapes of a unique design, mainly useful for 4D printing optimization), and the number of desired runs to repeat the same optimization process many times (useful to compare the results and analyze the behavior of the optimization methodology). According to the code structure, the continuous design variables must be the first to be defined, while the discrete variables for material assignation must be the last ones.…”

Functionally graded additive manufacturing to achieve functionality specifications of osteochondral scaffolds

“…Some authors have been working in optimization of lattice structures when geometry is changing with the time. For example, Paz et al [35] optimized a 4D printing geometry, of shape memory part, for two different scenarios when an external stimuli deforms the part, based on genetic algorithm as method for optimization. Once the dynamic optimal solution is achieved, the 3D geometry is exported either to AMF or FAV and the slicer interface imports such an exchange format to define the routes, processing parameters and print heads, combining the graded materials to make the graded OC scaffold in a multi-head bioplotter.…”

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

“…Optimization techniques have been used widely for the solution of many engineering and real‐life problems such as the allocation of resources (Kyriklidis & Dounias, ), product design and development (Lostado, Fernandez, Mac Donald, & Villanueva, ; Paz, Pei, Monzón, Ortega, & Suárez, ), process planning and scheduling (X. X. Li, Li, Cai, & He, ), wind farm distribution network optimization (Cerveira, Baptista, & Solteiro Pires, ), vertical transportation optimization in skyscrapers (Koo, Hong, Yoon, & Jeong, ), railway line design and timetable optimization (Castillo, Grande, Moraga, & Sánchez‐Vizcaíno, ), freeway travel cost optimization (Shahabi, Unnikrishnan, & Boyles, ), sustainable road network design (Y. Wang & Szeto, ), bridge design optimization (Bisadi & Padgett, ), construction scheduling (Karim & Adeli, ), mountain railway alignment optimization (W. Li et al, ), road weather information system network optimization (Kwon, Fu, & Melles, ), cost optimization of concrete (Sirca & Adeli, ) and steel building structures (Tashakori & Adeli, ) and composite floors (Adeli & Kim, ), free‐form steel space‐frame roof design optimization (Kociecki & Adeli, ), freeway work zone traffic delay and cost optimization (Jiang & Adeli, ), optimal control of bridges (Adeli & Saleh, ) and buildings (Saleh & Adeli, ), and among others.…”