Machine learning, an advanced mathematical tool for artificial intelligence, has been starting to gain attentions in materials science recently for the accelerated discovery of new materials [31], [32], accelerated materials design [2], [33], material synthesis recommendations [34], [35], property prediction [36], [37], and manufacturing
In recent years, Additive Manufacturing (AM) technology, also known as 3D printing, rapid prototyping or freeform fabrication, has gained wide attention due to its superiority in fabricating complex components. Additive manufacturing slices 3D objects into multiple layers of two-dimension in CAD, and then deposits feedstock layer by layer.
Additive manufacturing (AM), often known as 3D printing, has revolutionized the field of manufacturing by enabling the fabrication of customized, complex 3D structures in a layer-by-layer manner. It can be categorized into seven main groups based on ISO/ASTM 52900:2021: 1) binder jetting, 2) directed energy deposition, 3) material extrusion, 4
1. Introduction. Additive manufacturing (AM), as opposed to traditional subtractive manufacturing technologies, is a promising digital approach for the modern industrial paradigm that has gained widespread interest all over the world [1], [2], [3], [4] fabricating objects layer by layer from three-dimensional (3D) computer-aided design
Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent "black box" nature of ML techniques such as those represented by artificial neural networks has often presented a challenge to interpret ML outcomes in
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Additive manufacturing requires process control for the parts fabrication. • A machine learning approach is proposed for processing parameters optimization. • The approach provides tools for in-depth uncertainty quantification.. • The method is demonstrated by modeling the amorphicity of a glass-forming alloy.
In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is
Machine learning (ML), a subset of artificial intelligence (AI), has increasingly become popular in additive manufacturing (AM) research. Additive manufacturing, also known as 3D printing or rapid prototyping (RP), is defined as a group of layer-upon-layer fabrication processes controlled by a computer-aided design (CAD) model [1, 2].
Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with
Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology.However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality recent years, machine learning
This paper focuses on recent advances in algorithm-based methods for additive manufacturing processes, especially machine learning approaches. Three main additive manufacturing stages are explored and discussed including geometrical design, process parameter configuration, and in situ anomaly detection. Discussions on current
This has drawn increasing attention towards the use of machine learning in the manufacturing industry, particularly in the field of additive manufacturing (Jiang et al. 2020; Qin et al. 2022 ). The rapid progress of machine learning in additive manufacturing has brought us to this special issue, where we hope to bring together
Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on
Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies
The use of machine learning in other additive manufacturing processes is documented extensively and can provide new avenues of advancement for extrusion-based Bio-AM (Yu & Jiang, 2020). For instance, the work of Scime & Beuth, 2019 in laser powder bed fusion showed the use of unsupervised machine learning on meltpool
3.3 Machine Learning (ML) in Additive Manufacturing Additive Manufacturing is becoming increasingly data-intensive while generating a growing amount of newly accessible data. The availability of AM data affords Design for Additive Manufacturing (DfAM) a new chance to develop AM design guidelines with a deeper
Compared to conventional fabrication methods, additive manufacturing (AM) techniques pose a significantly greater challenge [1] due to their highly-dynamic processes (e.g., non-equilibrium solidification [2], cyclic thermal profiles [3], complex melt pool behaviors [4], [5], etc.) and numerous process variables stemming from the layer
Machine learning is now a hot technology that has been used in medical diagnosis, image processing, prediction, classification, learning association, regression, etc. Currently, focuses are increasingly given to using machine learning in the manufacturing industry, including additive manufacturing.
In this paper, several algorithms, with a focus on machine learning methods, are reviewed and explored to systematically tackle the three main stages of the
Moreover, additive manufacturing processes require knowledge about how process parameters affect material properties and melt pool dynamics to produce defect-free parts. Thus, creating machine learning models in additive manufacturing is difficult, due to the lack of sufficient data and the complexity of MAM processes [3].
For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image
Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on-demand and decentralized manufacturing. However, a number of challenges stem from not only the complexity of manufacturing systems but the demand
Prior studies in metal additive manufacturing (AM) of parts have shown that various AM methods and post-AM heat treatment result in distinctly different microstructure and machining behavior when compared with conventionally manufactured parts. There is a crucial knowledge gap in understanding this process-structure-property (PSP) linkage
Machine learning in design for additive manufacturing. DfAM significantly differs from the design principles commonly practised in conventional
Machine learning (ML) has undeniably turned into a mainstream idea by enhancing any system''s throughput by allowing a more intelligent usage of materials and processes and managing their resultant properties. In industrial applications, usage of ML not only decreases the lead time of the manufacturing process involved but because of
Machine learning (ML), a subset of artificial intelligence (AI), has increasingly become popular in additive manufacturing (AM) research. Additive manufacturing, also known as 3D printing or rapid prototyping (RP), is defined as a group of layer-upon-layer fabrication processes controlled by a computer-aided design (CAD) model [1, 2].
Additive manufacturing is the process of creating an object by building it one layer at a time. It is the opposite of subtractive manufacturing, in which an object is created by cutting away at a solid block of material until the final product is complete. Technically, additive manufacturing can refer to any process where a product is created