Additive Manufacturing Technologies

Gibson, Ian; Rosen, David W.; Stucker, Brent; Khorasani, Mahyar

doi:10.1007/978-3-030-56127-7

Cited by 594 publications

(342 citation statements)

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“…The AM processes are evaluated using many parameters such as fabrication speed, part strength, resolution, built volume, cost, quality, and surface finish. They are continuously evolving to make bigger and more complex products in the more flexible and economic way [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. However, among these, the most critical parameters are the fabrication speed and the resolution.…”

Section: Introductionmentioning

confidence: 99%

Additive Manufacturing of Polymer Materials: Progress, Promise and Challenges

et al. 2021

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The use of additive manufacturing (AM) has moved well beyond prototyping and has been established as a highly versatile manufacturing method with demonstrated potential to completely transform traditional manufacturing in the future. In this paper, a comprehensive review and critical analyses of the recent advances and achievements in the field of different AM processes for polymers, their composites and nanocomposites, elastomers and multi materials, shape memory polymers and thermo-responsive materials are presented. Moreover, their applications in different fields such as bio-medical, electronics, textiles, and aerospace industries are also discussed. We conclude the article with an account of further research needs and future perspectives of AM process with polymeric materials.

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Section: Introductionmentioning

confidence: 99%

Additive Manufacturing of Polymer Materials: Progress, Promise and Challenges

et al. 2021

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“…AM enables various advantages, particularly when compared with traditional manufacturing techniques, the enablement of mass part customisation and greater part complexity on the macro-, meso-, and micro-scales [3]. Other advantages include not requiring any hard tooling and enablement of on-demand manufacturing [4]. Despite these benefits, drawbacks include a lack of inherent repeatability [5] which has led to difficulty in gaining certification in some sectors [3].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…While, to some extent, applicable to all major industries, AM development has been largely driven by the aerospace, automotive and medical sectors [4]. The major driver in the aerospace and automotive sectors is to reduce component mass whilst not hindering performance [4]. A wider range of motivations is seen for medical applications of AM, although patient customisation, improved biocompatibility and performance is a common theme [4].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Additive Manufacturing

2021

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Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

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“…In additive manufacturing (AM), a three-dimensional geometry is decomposed in bi-dimensional shapes and used to create solid parts that are built layer upon layer [1]. This basic workflow is common to different manufacturing processes, like material jetting (MJT), material extrusion (MEX), and powder bed fusion (PBF) [2]. The application of AM has evolved from prototypes to small-batches, but the objective of reaching mass production capabilities would require filling the gap between AM specification standards and the industrial needs [3].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Improvement of the Achievable Tolerance Interval in Material Extrusion Additive Manufacturing through a Multi-State Machine Performance Perspective

et al. 2021

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Dimensional quality is still a major concern in additive manufacturing (AM) processes and its improvement is key to closing the gap between prototype manufacturing and industrialized production. Mass production requires the full working space of the machine to be used, although this arrangement could lead to location-related differences in part quality. The present work proposes the application of a multi-state machine performance perspective to reduce the achievable tolerance intervals of features of linear size in material extrusion (MEX) processes. Considering aspecific dimensional parameter, the dispersion and location of the distribution of measured values between different states are analyzed to determine whether the production should be treated as single-state or multi-state. A design for additive manufacturing strategy then applies global or local size compensations to modify the 3D design file and reduce deviations between manufactured values and theoretical values. The variation in the achievable tolerance range before and after the optimization of design is evaluated by establishing a target machine performance index. This strategy has been applied to an external MEX-manufactured cylindrical surface in a case study. The results show that the multi-state perspective provides a better understanding of the sources of quality variability and allows for a significant reduction in the achievable tolerance interval. The proposed strategy could help to accelerate the industrial adoption of AM process by reducing differences in quality with respect to conventional processes.

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Additive Manufacturing Technologies

Cited by 594 publications

References 0 publications

Additive Manufacturing of Polymer Materials: Progress, Promise and Challenges

Additive Manufacturing of Polymer Materials: Progress, Promise and Challenges

Machine Learning for Additive Manufacturing

Estimation and Improvement of the Achievable Tolerance Interval in Material Extrusion Additive Manufacturing through a Multi-State Machine Performance Perspective

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