There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

• Common defects and anomalies in powder bed fusion metal additive manufacturing. • Formation mechanism and practical mitigation strategies are discussed. • Defects/anomalies are classified as powder-related, processing-related, and post-processing related issues. • Properties such as mechanical behavior and corrosion resistance of defective parts are discussed. • Current challenges, gaps, and future trends are discussed. Metal additive manufacturing is a disruptive technology that is revolutionizing the manufacturing industry. Despite its unrivaled capability for directly fabricating metal parts with complex geometries, the wide realization of the technology is currently limited by microstructural defects and anomalies, which could significantly degrade the structural integrity and service performance of the product. Accurate detection, characterization, and prediction of these defects and anomalies have an important and immediate impact in manufacturing fully-dense and defect-free builds. This review seeks to elucidate common defects/anomalies and their formation mechanisms in powder bed fusion additive manufacturing processes. They could arise from raw materials, processing conditions, and post-processing. While defects/anomalies in laser welding have been studied extensively, their formation and evolution remain unclear. Additionally, the existence of powder in powder bed fusion techniques may generate new types of defects, e.g., porosity transferring from powder to builds. Practical strategies to mitigate defects are also addressed through fundamental understanding of their formation. Such explorations enable the validation and calibration of models and ease the process qualification without costly trial-and-error experimentation. 

Defects and anomalies in powder bed fusion metal additive manufacturing

Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still significant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certified and builds where the quality of the resulting components is unknown. This makes the approach cost efficient, particularly in scenarios where part certification is costly and time consuming. The study specifically analyses Laser Powder-Bed Fusion (L-PBF) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as ‘faulty’ or ‘acceptable’. Using a variety of approaches (Receiver Operating Characteristic (ROC) curves and 2-fold cross-validation), it is shown that, despite utilising a fraction of the available certification data, the semi-supervised approach can achieve results comparable to a benchmark case where all data points are labelled. The results show that semi-supervised learning is a promising approach for the automatic certification of AM builds that can be implemented at a fraction of the cost currently required.

Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning

While Laser powder bed fusion (L-PBF) machines have greatly improved in recent years, the L-PBF process is still susceptible to several types of defect formation. Among the monitoring methods that have been explored to detect these defects, camera-based systems are the most prevalent. However, using only photodiode measurements to monitor the build process has potential benefits, as photodiode sensors are cost-efficient and typically have a higher sample rate compared to cameras. This study evaluates whether a combination of photodiode sensor measurements, taken during L-PBF builds, can be used to predict measures of the resulting build quality via a purely data-based approach. Using several unsupervised clustering approaches build density is classified with up to 93.54% accuracy using features extracted from three different photodiodes, as well as observations relating to the energy transferred to the material. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density with a RMS error of 3.65%. The study, therefore, shows the potential for machine-learning algorithms to predict indicators of L-PBF build quality from photodiode build measurements only. This study also shows that, relative to the L-PBF process parameters, photodiode measurements can contribute to additional information regarding L-PBF part quality. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantification in machine-learnt predictions of L-PBF build quality.

/pdf/automatic-quality-assessments-of-laser-powder-bed-fusion-kb95w7gyt2.pdf

Automatic Quality Assessments of Laser Powder Bed Fusion Builds from Photodiode Sensor Measurements

/pdf/automatic-quality-assessments-of-laser-powder-bed-fusion-41xqpy8xb0.pdf

A Real-Time Porosity Prediction Model for Laser Powder Bed Fusion Builds

Abstract Recent studies in additive manufacturing (AM) monitoring techniques have focussed on the identification of defects using in situ monitoring sensor systems, with the aim of improving overall AM part quality. Much work has focussed on the use of of camera-based monitoring systems; however, limitations such as the slow response rates of the sensors (1-10kHz) and the post-processing requirements of the collected images make it difficult to apply these developmental monitoring methods on production systems in real-time. Furthermore, the replication of results from camera-based monitoring systems (often obtained using deep learning models) in a production environment is limited by the need for specialised hardware with high computational capacity (e.g GPUs). Focussing specifically on laser powder bed fusion ( PBF-L/M ), photodiodes, with fast data collection rates (50–100kHz) and providing data that is relatively easy to process are potentially better suited to real-time monitoring systems. The current study, therefore, focuses on using data collected from photodiodes to identify defects in PBF-L/M builds. A predictive model with real-time potential is proposed that, having been validated on data from computer tomography (CT) images, can be used to locate porosity within layers of PBF-L/M builds. 

Predicting gas pores from photodiode measurements in laser powder bed fusion builds

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Papers

Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning

Automatic Quality Assessments of Laser Powder Bed Fusion Builds from Photodiode Sensor Measurements

Automatic Quality Assessments of Laser Powder Bed Fusion Builds from Photodiode Sensor Measurements

A Real-Time Porosity Prediction Model for Laser Powder Bed Fusion Builds

Predicting gas pores from photodiode measurements in laser powder bed fusion builds