Machine Learning in Additive Manufacturing: A Review

Meng, Lingbin; McWilliams, Brandon; Jarosinski, William; Park, Hye-Yeong; Jung, Yeon‐Gil; Lee, Je-Hyun; Zhang, Jing

doi:10.1007/s11837-020-04155-y

Cited by 312 publications

(111 citation statements)

References 54 publications

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“…Therefore, the experimental workload based on conventional trialand-error testing methods and simple orthogonal experiments may substantially increase, undoubtedly substantially increasing the time devoted to research and economic costs. The statistical design of experiments and artificial intelligence prediction methods can be useful in deriving the best process parameters and reducing the volume of actual physical experiments (Goh, Sing, and Yeong 2021;Johnson et al 2020;Meng et al 2020). Rankouhi et al reported the use of machine learning to optimise the processing parameters of L-PBF of the 316L-Cu composite (Rankouhi et al 2021).…”

Section: Challenges In Experimental Methodsmentioning

confidence: 99%

Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion

Wei¹,

Li²

2021

Virtual and Physical Prototyping

156

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Multi-material additive manufacturing provides a new route for fabricating components with tailored physical properties. Laser-based powder bed fusion (L-PBF), also known as selective laser melting, is a powder bed-based additive manufacturing technology. This technology affords the advantage of manufacturing metallic and non-metallic materials with high geometrical resolution. An emerging field relevant to the foregoing is multi-material L-PBF. This paper reviews the latest progress in this field including multiple material powder deposition mechanisms, molten pool behaviour, process characteristics of printing metal-metal, metal-ceramic, and metal-polymer multiple material components, and potential applications. Finally, scientific and technological challenges are presented.

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Section: Challenges In Experimental Methodsmentioning

confidence: 99%

Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion

Wei¹,

Li²

2021

Virtual and Physical Prototyping

156

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“…An analytical model was used by Croccolo et al to predict tensile strength based upon build orientation and number of solid shells [18]. A wide range of machine learning techniques have been used in AM [28]. Artificial neural networks (ANNs) were used by Sood et al [12] to predict compressive strength based upon layer thickness, build orientation, raster angle, raster width and air gap.…”

Section: Capability Profilingmentioning

confidence: 99%

The creation of a neural network based capability profile to enable generative design and the manufacture of functional FDM parts

Goudswaard

Hicks

Nassehi

2021

Int J Adv Manuf Technol

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In order to manufacture functional parts using filament deposition modelling (FDM), an understanding of the machine’s capabilities is necessary. Eliciting this understanding poses a significant challenge due to a lack of knowledge relating manufacturing process parameters to mechanical properties of the manufactured part. Prior work has proposed that this could be overcome through the creation of capability profiles for FDM machines. However, such an approach has yet to be implemented and incorporated into the overall design process. Correspondingly, the aim of this paper is two-fold and includes the creation of a comprehensive capability profile for FDM and the implementation of the profile and evaluation of its utility within a generative design methodology. To provide the foundations for the capability profile, this paper first reports an experimental testing programme to characterise the influence of five manufacturing parameters on a part’s ultimate tensile strength (UTS) and tensile modulus (E). This characterisation is used to train an artificial neural network (ANN). This ANN forms the basis of a capability profile that is shown to be able to represent the mechanical properties with RMSEP of 1.95 MPa for UTS and 0.82 GPa for E. To validate the capability profile, it is incorporated into a generative design methodology enabling its application to the design and manufacture of functional parts. The resulting methodology is used to create two load bearing components where it is shown to be able to generate parts with satisfactory performance in only a couple of iterations. The novelty of the reported work lies in demonstrating the practical application of capability profiles in the FDM design process and how, when combined with generative approaches, they can make effective design decisions in place of the user.

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“…Above are example methods in ML. In fact, learning is quite a broad topic and several techniques have already been used in 3D printing in general[ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

An¹,

Chua²,

Mironov³

2024

IJB

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The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

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Machine Learning in Additive Manufacturing: A Review

Cited by 312 publications

References 54 publications

Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion

Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion

The creation of a neural network based capability profile to enable generative design and the manufacture of functional FDM parts

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

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