Machine Learning for Advanced Additive Manufacturing

Pegg

Advanced Intelligent Systems

et al. 2021

Self Cite

In recent years, the intersection of 3D printing and “smart” stimuli‐responsive materials has led to the development of 4D printing, an emerging field that is a subset of current additive manufacturing research. By integrating existing printing processes with novel materials, 4D printing enables the direct fabrication of sensors, controllable structures, and other functional devices. Compared to traditional manufacturing processes for smart materials, 4D printing permits a high degree of design freedom and flexibility in terms of printable geometry. An important branch of 4D printing concerns electroactive materials, which form the backbone of printable devices with practical applications throughout biology, engineering, and chemistry. Herein, the recent progress in the 4D printing of electroactive materials using several widely studied printing processes is reviewed. In particular, constituent materials and mechanisms for their preparation and printing are discussed, and functional electroactive devices fabricated using 4D printing are highlighted. Current challenges are also described and some of the many data‐driven opportunities for advancement in this promising field are presented.

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

4D Printing of Electroactive Materials

Pegg

Advanced Intelligent Systems

et al. 2021

Self Cite

“…Key to the exploration of such intricate structures are advances in high-performance computing and simulation methods, namely finite element analysis (FEA), that have enabled the computation of many facets of mechanical performance (Bar-Sinai et al, 2020;Gao et al, 2003;Kochmann and Bertoldi, 2017). By combining FEA with optimization algorithms, approaches such as topology optimization (Barthelat and Mirkhalaf, 2013;Boddeti et al, 2018;Chen et al, 2018;Jin et al, 2020;Sigmund and Maute, 2013) have led to discovery of intriguing hierarchical structures and composites.…”

Section: Introductionmentioning

confidence: 99%

Using simulation to accelerate autonomous experimentation: A case study using mechanics

et al. 2021

Advanced Intelligent Systems

“…Interested readers can learn more about these techniques from relevant reviews. [19][20][21] We will focus on data-driven methods that are most applicable to AM of metallic products, while drawing parallels to developments in the field of polymer AM when such methods discussed are yet to be implemented in metallic AM. A detailed review is available elsewhere on the state-of-the-art techniques in data-driven AM that are more specific to polymeric materials.…”

Section: Introductionmentioning

confidence: 99%

“…A detailed review is available elsewhere on the state-of-the-art techniques in data-driven AM that are more specific to polymeric materials. [19] The data-driven methods discussed in our Review include various digital methods of high-throughput simulation and optimization, ML, neural networks, and genetic algorithms (GA). For a more detailed review on current developments in ML specifically for metallic AM, we refer the readers to another excellent review article.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

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.