Machine learning generative models for automatic design of multi-material 3D printed composite solids

Xue, Tianju; Wallin, Thomas J.; Mengüç, Yiğit; Adriaenssens, Sigrid; Chiaramonte, Maurizio

doi:10.1016/j.eml.2020.100992

Cited by 63 publications

(29 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A numerical realization of a similar idea was reported in [62]. Further development of these concepts becomes possible owing to breakthroughs in deep learning methods, such as GAN and VAE, which allow variation of spatial distributions of pixels/voxels of the base materials within the unit cells [63,64]. The number of parameters that can be varied for 2D and 3D unit cells can be estimated at 10 4 and 10 6 , respectively.…”

Section: Architectured Lattice Materialsmentioning

confidence: 94%

Architecturing materials at mesoscale: some current trends

Estrin

Beygelzimer

Kulagin

et al. 2021

Materials Research Letters

View full text Add to dashboard Cite

This article overviews several areas of research into architectured materials which, in the opinion of the authors, are most topical and promising. The classes of materials considered are based on meso scale designs inspired by animate and inanimate Nature, but also on those born in the minds of scientists and engineers, without any inspiration from Nature. We present the principles governing the design of the emerging materials architectures, discuss their explored and anticipated properties, and provide an outlook on their future developments and applications. IMPACT STATEMENTThe article addresses several hot topics with a great promise for innovative materials design.

show abstract

Section: Architectured Lattice Materialsmentioning

confidence: 94%

Architecturing materials at mesoscale: some current trends

Estrin

Beygelzimer

Kulagin

et al. 2021

Materials Research Letters

View full text Add to dashboard Cite

show abstract

“…Many researchers have implemented the advanced optimization tools, artificial intelligence, and machine learning approached. Xue et al [ 27 ] developed a variational autoencoder based upon machine learning and Bayesian optimization for designing a 3D printed prototype with customized macroscopic elastic properties. Goh et al [ 28 ] implemented the neural network technique for exploring the relationship between process parameters and mechanical strength of PolyJet 3D printed parts.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling

Kumar

Chohan

Singh

et al. 2022

International Journal of Biomaterials

View full text Add to dashboard Cite

Additive manufacturing has gained popularity among material scientists, researchers, industries, and end users due to the flexible, low cost, and simple manufacturing process. Among number of techniques, fused deposition modeling (FDM) is the most recognized technology due to easy operation, lower environmental degradation, and portable apparatus. Despite numerous advantages, the limitations of this technique are poor surface finish, dimensional accuracy, and mechanical strength, which must be improved. The present study focuses on the implementation of the genetic algorithm and Taguchi techniques to achieve minimum dimensional variability of FDM parts especially for polymeric biocomposites. The output has been measured using standard testing techniques followed by Taguchi and genetic algorithm analyses. Four response variables were measured and were converted into single variable with combination of different weightages of each response. Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. The advanced optimization and production techniques have yielded promising results which have been validated by advanced algorithms. It was found that layer thickness and orientation angle were significant parameters which influenced the dimensional accuracy whereas best fitness value was 0.377.

show abstract

“…ML-aided optimization of repetitive lattice composite materials can also automatically generate structures of microscale representative volume elements for desired macroscale elastic moduli. [118] While data-driven methods significantly advanced the AM of structurally complex polymer composites, this success is not replicated in metal composites due to the challenge of preventing the constituent materials from reacting with each other under high temperatures. Nonetheless, the success of AI-aided design in AM-enabled polymer composites can still inspire the design of AM-enabled metal composite, as shown in the optimization of the constituents' staggered arrangements with generalized material properties that are applicable to both polymers and metals.…”

Section: Optimization Of Constituent Arrangement In Compositesmentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

View full text Add to dashboard Cite

The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.

show abstract

Machine learning generative models for automatic design of multi-material 3D printed composite solids

Cited by 63 publications

References 27 publications

Architecturing materials at mesoscale: some current trends

Architecturing materials at mesoscale: some current trends

Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Contact Info

Product

Resources

About