Machine learning in additive manufacturing: State-of-the-art and perspectives
Introduction
Additive manufacturing (AM) is a disruptive digital manufacturing technology to make 3D objects, usually layer upon layer, according to computer-aided design (CAD) models. Compared to conventional manufacturing technologies, it has the advantages of fabricating intricate parts with complex geometries and designs, unique microstructures and properties, as well as reduced lead time and cost. Therefore, in recent years, AM has attracted a great deal of research interest in both academic research and industrial applications worldwide.
According to the ASTM F42, AM processes can be broadly classified into 7 categories [1]. The AM techniques involving Machine Learning (ML) in this article mainly fall under 3 classes of technology, namely powder bed fusion (PBF), directed energy deposition (DED) and material extrusion, as they are currently the mainstream AM processes that have attracted great attention in both academic research and industrial applications (see Fig. 1). Although ML has also been applied in other AM processes such as materials jetting [2] and stereolithography [3], these research works are not very relevant to the focus of this article hence they are not specifically discussed here.
Under the PBF category, a laser or electron beam is used as the energy source to selectively melt powder bed which is uniformly spread by re-coating layer by layer [[4], [5], [6]]. In the DED process, a focused laser beam melts the continuous powder stream or wire which are fed from the deposition nozzle into the melt pool in order to fabricate near-net-shape parts [7,8]. One typical material extrusion process is fused deposition modelling (FDM). The filament is fed into the liquefier head by a driver gear and is subsequently heated to the glass transition state. The semi-liquid filament material is then deposited from the extrusion nozzle to print parts layer by layer [9].
ML is an artificial intelligence (AI) technique that allows a machine or system to learn from data automatically and make decisions or predictions without being explicitly programmed. In research, ML is gaining popularity in medical diagnostics [[10], [11], [12]], material property prediction [[13], [14], [15]], smart manufacturing [[16], [17], [18]], autonomous driving [[19], [20], [21]], natural language processing [[22], [23], [24]] and object recognition [[25], [26], [27]]. ML algorithms are commonly categorized as supervised, unsupervised and reinforcement learning.
Supervised learning enables a computer programme to learn from a set of labelled data in the training set so that it can identify unlabelled data from a test set with the highest possible accuracy [28]. The datasets can be in a variety of forms including forms of images [29,30], audio clips [31,32] or text [33]. There is an objective function known as cost function, which calculates the error between the predicted output values and the actual output values. In the training process, the parameters (or weights) between neurons in adjacent layers are updated in order to reduce the cost function after each iteration (or epoch) [34]. In the testing process, the previously unseen new data, i.e. test set, is introduced to provide an unbiased evaluation of the model’s accuracy.
Unsupervised learning infers from unlabelled data [35,36]. It is a data-driven ML technique which can uncover hidden patterns or group similar data together (i.e. clustering) in a given random dataset [37]. Unsupervised learning is widely used in anomaly detection [38], recommendations systems [39], and market segmentation [40,41].
Reinforcement learning is a semi-supervised ML paradigm which allows the model to interact with the environment and learn to take the best actions that can yield the greatest rewards [42]. It requires no training dataset, and the model learns from its own actions. Reinforcement learning is popularly adopted in robotic arms [43,44], autonomous cars [45,46], and AlphaGo [[47], [48], [49]].
The terminologies of the ML algorithms that are mentioned in this overview article can be found in the Appendix. Readers are suggested to refer to the recommended textbooks or research papers in this table to gain more insights into the details of each ML algorithm.
This paper aims to provide a state-of-the-art review of applications of ML techniques in various domains of AM production practices from the most recent literatures as well as our perspectives on some impactful research directions that are still on-going or may occur in the future. To clearly elucidate the benefits of using ML in AM, we broadly classify the applications into 3 categories, namely design for additive manufacturing (DfAM), AM process and AM production, as illustrated in Fig. 2. This is to reflect a logical sequence from design, process optimization and in-process monitoring, to manufacturing planning, product quality control and data security that are closely linked to overall production concerns.
Section snippets
Machine learning in design for additive manufacturing
DfAM significantly differs from the design principles commonly practised in conventional manufacturing due to its boundless design freedom. Here, the applications of ML in DfAM will be elucidated in two aspects, namely material design, and topology design.
Process parameter optimization
Traditionally, process parameter development and optimization are implemented by design of experiment or simulation methods to additively manufacture new materials. Nevertheless, the design of experiment approach usually involves trial-and-error, which is time-consuming and costly, particularly for metal AM [4,[66], [67], [68]]. The physical-based simulation can reveal the underlying mechanism for the formation of specific features during processing, e.g. melt pool geometry, keyhole,
Additive manufacturing planning
As AM is still considered as an expensive manufacturing process in the current stage, high yield is essential to many end-users. A delicate pre-manufacturing plan for the AM production chain starting from CAD design to final product quality control is needed. Hence, some works have adopted ML to assist in AM planning.
In pre-manufacturing, the manufacturability of a part can be determined with the help of ML, as for example conducted by Tang et al. [116] for FDM-printed lattice structures. In
Summary and perspectives
The recently established applications of the ML-based methods in the DfAM, AM process, and AM production were comprehensively reviewed in the above-mentioned sections. It can be observed that the vast majority of the current applications of ML in AM research fields are intensively concentrated on processing-related processes such as parameter optimization and in-process monitoring. However, we can expect to see the overwhelming ML research efforts paid on new materials, rational manufacturing
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgements
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.
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