Intelligent additive manufacturing and design: state of the art and future perspectives

Xiong, Yi; Tang, Yunlong; Zhou, Qi; Ma, Yongsheng; Rosen, David W.

doi:10.1016/j.addma.2022.103139

Cited by 31 publications

(18 citation statements)

References 97 publications

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“…It will therefore require the combined efforts of scientific communities from diverse backgrounds, such as physics, chemical engineering, and materials surface science. [125][126][127][128][129] Researchers need to refine the material design theory further and build a specialized database of 3D-printable materials to enable intelligent material development and screening. By establishing connections among the composition, process, microstructure, and properties, novel surfaces can be designed with desired interfacial properties based on the characteristics of elemental materials.…”

Section: Discussionmentioning

confidence: 99%

“…Future research into droplet interfaces in AM processes will be uniquely multidisciplinary, covering a diverse range of fields encompassing both fundamental science and engineering. It will therefore require the combined efforts of scientific communities from diverse backgrounds, such as physics, chemical engineering, and materials surface science 125–129 . Researchers need to refine the material design theory further and build a specialized database of 3D‐printable materials to enable intelligent material development and screening.…”

Section: Discussionmentioning

confidence: 99%

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Droplet interface in additive manufacturing: From process to application

Sun

Deng

et al. 2023

Droplet

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Additive manufacturing (AM), which is often referred to as three-dimensional printing, revolutionizes manufacturing by providing a high degree of design freedom and customization. Several AM methods entail precise control of droplet interfacial properties to ensure the high quality of the printed products. At the same time, the rapid growth of AM technology has made it possible to prepare novel surfaces with complex structures, further expanding the range of potential applications of droplet interface. To provide a unified framework to guide the continuous development of

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Droplet interface in additive manufacturing: From process to application

Sun

Deng

et al. 2023

Droplet

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“…At the micro-scale, it is also possible to create functionally graded materials via in-situ alloying [13,14]. Foams, lattice and chain-mail structures [15,16] can be produced at a meso-scale, and certain geometric features that are not feasible via conventional processes can be manufactured at a macro-scale. This capability of AM allows load-adapted re-designed components to be optimized and light-weighted [17][18][19].…”

Section: Additive Manufacturing and Re-designing Through Topological ...mentioning

confidence: 99%

“…More than other processes, AM is deep-rooted in computeraided procedures, since several tasks that are inherent to design and manufacturing mostly occur in a digital environment. Such characteristics may foster the development of intelligent manufacturing, in which products and production are optimized making full use of the involved manufacturing technologies coupled with advanced information systems [16]. As a result of the opportunities of extending the lifespan of products, coupled with high resource efficiency and low waste generation [35], AM has been identified as suitable to enable the execution of Circular Economy (CE) principles and design strategies [37].…”

Section: Sustainability Implications Of Re-designing Via Ammentioning

confidence: 99%

Additive manufacturing for the automotive industry: on the life-cycle environmental implications of material substitution and lightweighting through re-design

Priarone¹,

Catalano²,

Settineri³

2023

Prog Addit Manuf

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The automotive sector has recently been taking measures to reduce fuel consumption and greenhouse gas emissions for the mobility of ground vehicles. Light-weighting, via material substitution, and the re-designing of components or even a combination of the two, have been identified as a crucial solution. Additive manufacturing (AM) can be used to technologically complement or even replace conventional manufacturing in several industrial fields. The enabling of complexity-for-free (re) designs is inherent in additive manufacturing. It is expected that certain benefits can be achieved from the adoption of re-design techniques, via AM, that rely on topological optimisation, e.g., a reduced use of resources in both the material production and use phases. However, the consequent higher specific energy consumption and the higher embodied impact of feedstock materials could result in unsustainable environmental costs. This paper investigates the case of the light-weighting of an automobile component to quantify the outcomes of the systematic integration of re-designing and material substitution. A bracket, originally cast in iron, has been manufactured by means of a powder bed-based AM technique in AlSi10Mg through an optimized topology. Both manufacturing routes have been evaluated through a comparative Life Cycle Assessment (LCA) within cradle-to-grave boundaries. A 69%-lightweighting has been achieved, and the carbon dioxide emissions and energy demands of both scenarios have been compared. Besides the use-phase-related savings in terms of both energy and carbon footprint due to the lightweighting, the results highlight the environmental trade-offs and prompt the consideration of such a manufacturing process as an integral part of sustainable product development.

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“…Over the past several decades, additive manufacturing (AM) based technologies have shown their potential to revolutionize many research fields ranging from aerospace technology to biomedical applications [1,2]. This impact is clearly observed by the quick, on-demand, and free-form fabrication of complex architectures made using AM.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of evolutionary machine learning approaches to simulate the rheological characteristics of polybutylene succinate (PBS) utilized for fused deposition modeling (FDM)

Taylan¹,

Abdullah²,

Baik³

et al. 2023

Preprint

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Polymer filament and its printability, which is strongly influenced by the rheological behavior, can represent a significant hurdle in translating fused deposition modeling (FDM) from the lab to the industrial or clinical settings. The aim of this study is to demonstrate the potential of machine learning (ML) approaches to speed up the development of polymer filaments for FDM. Four types of ML methods; artificial neural network, support vector regression, polynomial chaos expansion (PCE), and response surface model were used to predict the rheological behaivior of polybutylene succinate. In general, all four approaches presented significantly high correlation values with respect to the training and testing data stages. Remarkably, the PCE algorithm repeatedly provided the highest correlation for each response variable in both the training and testing stages. Noteworthy, variation differs between response variables rather than between algorithms. Taken together, these modeling approaches could be used to optimize filament extrusion processes.

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Intelligent additive manufacturing and design: state of the art and future perspectives

Cited by 31 publications

References 97 publications

Droplet interface in additive manufacturing: From process to application

Droplet interface in additive manufacturing: From process to application

Additive manufacturing for the automotive industry: on the life-cycle environmental implications of material substitution and lightweighting through re-design

Comparative study of evolutionary machine learning approaches to simulate the rheological characteristics of polybutylene succinate (PBS) utilized for fused deposition modeling (FDM)

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