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. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

Machine learning in additive manufacturing: State-of-the-art and perspectives

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

/pdf/a-review-on-machine-learning-in-3d-printing-applications-1fsr2i1b6p.pdf

A review on machine learning in 3D printing: applications, potential, and challenges

This article provides a database of the mechanical properties of additively manufactured polymeric materials fabricated using material extrusion (e.g., fused filament fabrication (FFF)). Mechanical...

Process–Structure–Properties in Polymer Additive Manufacturing via Material Extrusion: A Review

Additive manufacturing, also known as 3D printing, is a new technology that obliterates the geometrical limits of the produced workpieces and promises low running costs as compared to traditional manufacturing methods. Hence, additive manufacturing technology has high expectations in industry. Unfortunately, the lack of a proper quality monitoring prohibits the penetration of this technology into an extensive practice. This work investigates the feasibility of using acoustic emission for quality monitoring and combines a sensitive acoustic emission sensor with machine learning. The acoustic signals were recorded using a fiber Bragg grating sensor during the powder bed additive manufacturing process in a commercially available selective laser melting machine. The process parameters were intentionally tuned to invoke different processing regimes that lead to the formation of different types and concentrations of pores (1.42 ± 0.85 %, 0.3 ± 0.18 % and 0.07 ± 0.02 %) inside the workpiece. According to this poor, medium and high part qualities were defined. The acoustic signals collected during processing were grouped accordingly and divided into two separate datasets; one for the training and one for the testing. The acoustic features were the relative energies of the narrow frequency bands of the wavelet packet transform, extracted from all the signals. The classifier, based on spectral convolutional neural network, was trained to differentiate the acoustic features of dissimilar quality. The confidence in classifications varies between 83 and 89 %. In view of the narrow range of porosity, the results can be considered as promising and they showed the feasibility of the quality monitoring using acoustic emission with the sub-layer spatial resolution.

Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks

Monitoring and processing signal applied in machining processes – A review

Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closed-loop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a non-intrusive diagnostic and prognostic tool for FDM machine maintenance and process control.

In situ monitoring of FDM machine condition via acoustic emission

Machine condition monitoring is considered as an important diagnostic and maintenance strategy to ensure product quality and reduce manufacturing cost. However, currently, most additive manufacturing (AM) machines are not equipped with sensors for system monitoring. In this paper, a real-time lightweight AM machine condition monitoring approach is proposed, where acoustic emission (AE) sensor is used. In the proposed method, the original AE waveform signals are first simplified as AE hits, and then segmental and principal component analyses are applied to further reduce the data size and computational cost. From AE hits, the hidden semi-Markov model (HSMM) is applied to identify the machine states, including both normal and abnormal ones. Experimental studies on fused deposition modeling (FDM), one of the most popular AM technology, show that the typical machine failures can be identified in a real-time manner. This monitoring method can serve as a diagnostic tool for FDM machines.

Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model

A novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed. The grinding signals are collected by an acoustic emission (AE) sensor. A preprocessing method is presented to identify the grinding period signals from raw AE signals. Root mean square and variance of each decomposition level are designated as the feature vector using discrete wavelet decomposition. Various grinding experiments were performed on a surface grinder to validate the proposed classification system. The results indicate that the proposed monitoring system could achieve a classification accuracy of 99.39% with a cut depth of 10 μm, and 100% with a cut depth of 20 μm. Finally, several factors that may affect the classification results were discussed as well.

Zhonghua Yu

Papers

In situ monitoring of FDM machine condition via acoustic emission

Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model

Grinding wheel wear monitoring based on wavelet analysis and support vector machine

Application of Hilbert–Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process

Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission