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

Okaro, Ikenna Anthony; Jayasinghe, Sarini; Sutcliffe, Christopher; Black, Kate; Paoletti, Paolo; Green, Peter L.

doi:10.1016/j.addma.2019.01.006

Cited by 120 publications

(67 citation statements)

References 48 publications

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“…The algorithm is working, but before it can be used as in-situ monitoring environment, its classification accuracy needs to be improved. Okaro et al [33] used photodiode measurements to develop a semi-supervised ML algorithm. The accuracy of the algorithm was at 77% and reduced experimental data are needed for training compared to supervised ML.…”

Section: Related Workmentioning

confidence: 99%

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

et al. 2020

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Additive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal parameter settings are usually detected after the process. To detect these defects during the printing, different process monitoring techniques such as melt pool monitoring or off-axis infrared monitoring have been proposed. In this work, we used a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures, to detect printing defects. For the network training, a k-fold cross validation and a hold-out cross validation were used. With these techniques, defects such as delamination and splatter can be recognized with an accuracy of 96.80%. In addition, the model was evaluated with computing class activation heatmaps. The architecture is very small and has low computing costs, which means that it is suitable to operate in real time even on less powerful hardware.

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Section: Related Workmentioning

confidence: 99%

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

et al. 2020

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“…characteristics co-ordinates of melt-pools within close [11] melt-pools where proximity were utilsed the signal is fluctuating to detect defective within a scan vector Of the aforementioned work, [8,9,10,11] utilised photodiode sensors. Photodiodes are spatially integrated single-channel detectors that provide a voltage corresponding to the amount of light collected by the detector at each focal point.…”

Section: Plume Identifying Positionmentioning

confidence: 99%

“…Bisht et al [8] studied the correlation between sensor readings, taken from an off-axial photodiode, and tensile properties of L-PBF builds (ultimate tensile strength and plastic elongation). The work described in [9] explored the ability of two co-axial photodiode sensors to predict the ultimate tensile strength of L-PBF builds via a semi-supervised machine learning algorithm. The paper [10] analysed the ratio between two co-axial photodiode readings (ADC-1/ADC-2), that capture different nearinfrared wavelengths, under different process conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Jayasinghe¹,

Paoletti²,

Sutcliffe³

et al. 2020

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This study evaluates whether a combination of photodiode sensor measurements, taken during laser powder bed fusion (L-PBF) builds, can be used to predict the resulting build quality via a purely data-based approach. We analyse the relationship between build density and features that are extracted from sensor data collected from three diﬀerent photodiodes. The study uses a Singular Value Decomposition to extract lower-dimensional features from photodiode measurements, which are then fed into machine learning algorithms. Several unsupervised learning methods are then employed to classify low density (< 99% part density) and high density (≥ 99% part density) specimens. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density. Using the unsupervised clustering approaches, applied to features extracted from both photodiode sensor data as well as observations relating to the energy transferred to the material, build density was predicted with up to 93.54% accuracy. With regard to the supervised regression approach, a Gaussian Process algorithm was capable of predicting the build density with a RMS error of 3.65%. The study shows, therefore, that there is potential for machine learning algorithms to predict indicators of L-PBF build quality from photodiode build-measurements. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantiﬁcation in machine-learnt predictions of L-PBF build quality.

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“…Machine learning (ML) techniques can carry out complex pattern recognition and regression analysis without constructing and solving the underlying physical models. This method is widely used in modeling, prediction, and analyzing the interaction of parameters in different industries, such as manufacturing, aerospace, and biomedicine [32,33]. Among ML algorithms, artificial neural networks (ANNs), which are mathematical models mapping an input space to an output space, are the most extensively used techniques because of their strong computational power and sophisticated architectures [34].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

2020

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Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V alloy—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and various line and surface roughness parameters for the top, upskin, and downskin surfaces are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed to account for the five L-PBF processing parameters at five levels each. The significance and contribution of the individual processing parameters on each response are analyzed using the Taguchi method. Then, the simultaneous contribution of two processing parameters on various responses is presented using RSM quadratic modeling. A multi-objective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. The Taguchi, RSM, and ANN models are used to predict the responses of unseen data. The results show that with the same amount of available experimental data, the proposed ANN model can most accurately predict the response of various properties of L-PBF components.

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

Cited by 120 publications

References 48 publications

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

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

Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

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