Additive Manufacturing Technologies – Rapid Prototyping to Direct Digital Manufacturing

Mueller, Bernhard

doi:10.1108/aa.2012.03332baa.010

Cited by 245 publications

(96 citation statements)

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“…According to the experimental results of our previous work mentioned above, it is very clear that by using solid-state mechanochemical method, not only the filler dispersion but also the interfacial compatibility between filler and polymer matrix can be effectively improved. 23,24 As one of the additive manufacturing technologies (3Dprinting), 26 selective laser sintering (SLS), which is based on the discrete accumulation manufacturing principle, generally uses a controlled laser beam as heat source to selectively sinter the powder materials layer by layer according to the 3D digital models and finally finishes fabrication of the end-use parts. 27 Compared with the traditional forming methods such as injection molding and compression molding, SLS technology is particularly more suitable for fabrication of the personalized parts and the ones with very complex geometry and structure such as cooling fan.…”

Section: Introductionmentioning

confidence: 99%

Solid‐state shear milling method to prepare PA12/boron nitride thermal conductive composite powders and their selective laser sintering 3D‐printing

Yang

Wang

Chen

2019

J of Applied Polymer Sci

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The selective laser sintering (SLS) is one of the most important 3D‐printing technologies. However, the challenges in SLS could be in the limited high material cost and single material performance. Development of high‐performance and multifunctional copowders suitable for SLS is of great importance. Here, polyamide 12 (PA12)/boron nitride (BN) thermal conductive copowders suitable for SLS were successfully prepared through solid state shear milling (S3M) technology in combination with cryogenic pulverization technology. The particle size, morphology, grafting reaction between PA12 and BN, rheology behavior, and coalescence behavior of the obtained PA12/BN copowders were carefully investigated. The optimal amount of silica flow additive (0.5 wt %) was determined to achieve the good powder flowability. Under the optimum 3D‐printing conditions, the fabrication of parts with high BN loading could be achieved. When BN content was at 40 wt %, the flexural strength could reach 10.6 MPa and the thermal conductivity could reach 0.55 W/m·k, 77% higher than that of pure PA12. After treated with phenolic epoxy resin, the tensile strength and flexural strength of the printed parts with 40 wt % BN loading could reach 14.2 and 25.6 MPa, which were 130 and 115% higher than those of the untreated 3D‐printed parts, respectively. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48766.

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Section: Introductionmentioning

confidence: 99%

Solid‐state shear milling method to prepare PA12/boron nitride thermal conductive composite powders and their selective laser sintering 3D‐printing

Yang

Wang

Chen

2019

J of Applied Polymer Sci

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“…AM can appreciably reduce material waste, decrease the amount of inventory, and reduce the number of distinct parts needed for an assembly [14], [15]. Further, AM can reduce the number of steps in a production process, both in the case of tool making as well as direct manufacturing, reducing the need for manual assembly [16].…”

Section: A Additive Manufacturing: Overviewmentioning

confidence: 99%

A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes

Paul¹,

Mozaffar²,

Yang³

et al. 2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computeraided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and timeconsuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees -an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https://github.com/paularindam/ml-iter-additive.

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“…A comparison between above two AM processes is summarized in Table 3 [113]. Same as EBM, SLM process is effective in reducing the waste material since the unused powder can be recycled.…”

Section: Selective Laser Meltingmentioning

confidence: 99%

State of the art of bioimplants manufacturing: part I

Kang

Fang

2018

Adv. Manuf.

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Bioimplants are becoming increasingly important in the modern society due to the fact of an aging population and associated issues of osteoporosis and osteoarthritis. The manufacturing of bioimplants involves an understanding of both mechanical engineering and biomedical science to produce biocompatible products with adequate lifespans. A suitable selection of materials is the prerequisite for a long-term and reliable service of the bioimplants, which relies highly on the comprehensive understanding of the material properties. In this paper, most biomaterials used for bioimplants are reviewed. The typical manufacturing processes are discussed in order to provide a perspective on the development of manufacturing fundamentals and latest technologies. The review also contains a discussion on the current measurement and evaluation constraints of the finished bioimplant products. Potential future research areas are presented at the end of this paper.

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Additive Manufacturing Technologies – Rapid Prototyping to Direct Digital Manufacturing

Cited by 245 publications

References 0 publications

Solid‐state shear milling method to prepare PA12/boron nitride thermal conductive composite powders and their selective laser sintering 3D‐printing

Solid‐state shear milling method to prepare PA12/boron nitride thermal conductive composite powders and their selective laser sintering 3D‐printing

A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes

State of the art of bioimplants manufacturing: part I

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