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Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing.

ABSTRACT: 3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.

SUBMITTER: Rojek I

PROVIDER: S-EPMC8707385 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFor difficult or rare procedures, simulation offers an opportunity to provide education and training. In developing an adequate model to utilize in simulation, 3D printing has emerged as a useful technology to provide detailed, accessible, and high-fidelity models. Nasal osteotomy is an essential step in many rhinoplasty surgeries, yet it can be challenging to perform and difficult to receive adequate exposure to this nuanced portion of the procedure. As it currently stands, there are limited opportunities to practice nasal osteotomy due to the reliance on cadaveric bones, which are expensive, difficult to obtain, and require appropriate facilities and personnel. While previous designs have been developed, these models leave room for improvement in printing efficiency, cost, and material performance. This manuscript aims to describe the methodology for the design of an updated nasal osteotomy training model derived from anatomic data and optimized for printability, usability, and fidelity. Additionally, an analysis of multiple commercially available 3D printing materials and technologies was conducted to determine which offered superior equivalency to bone.MethodsThis model was updated from a first-generation model previously described to include a more usable base and form, reduce irrelevant structures, and optimize geometry for 3D printing, while maintaining the nasal bones with added stabilizers essential for function and fidelity. For the material comparison, this updated model was printed in five materials: Ultimaker Polylactic Acid, 3D Printlife ALGA, 3DXTECH SimuBone, FibreTuff, and FormLabs Durable V2. Facial plastic surgeons tested the models in a blinded, randomized fashion and completed surveys assessing tactile feedback, audio feedback, material limitation, and overall value.ResultsA model optimizing printability while maintaining quality in the area of interest was developed. In the material comparison, SimuBone emerged as the top choice amongst the evaluating physicians in an experience-based subjective comparison to human bone during a simulated osteotomy procedure using the updated model.ConclusionThe updated midface model that was user-centered, low-cost, and printable was designed. In material testing, Simubone was rated above other materials to have a more realistic feel.

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Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing.

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