Trends of machine learning in additive manufacturing

Baumann, Felix W.; Sekulla, André; Hassler, Michael; Himpel, Benjamin; Pfeil, Markus

doi:10.1504/ijrapidm.2018.10016883

Cited by 6 publications

(2 citation statements)

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“…In recent years, machine learning has become a viable option in the additive manufacturing domain as a means for building highly flexible models describing complex relationships between variables. One of the latest reviews on trends of machine learning in additive manufacturing [7] describes five different categories of machine learning application: process parameters, quality enhancement, process monitoring and control, digital security and additive manufacturing in general. The main focus is set on application of ANN, genetic algorithms (GA), support vector machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

“…Fewer articles used deep neural networks, principal component analysis (PCA) and particle swarm optimization (PSO) [8][9][10]. While ANN is used to optimize process parameters, predict mechanical properties and porosity of the object, deep learning techniques were already applied in order "to identify styles of 3D models" based on 2D images rendered from digital 3D models [7]. This paper investigates applicability of two neural network models, namely Multi-layer perceptron (MLP) and Convolution Neural Network(CNN), for predicting scaling ratio for each additively manufactured part separately.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Methods to Improve Dimensional Accuracy in Additive Manufacturing

Baturynska

Semeniuta

Wang

2018

Advanced Manufacturing and Automation VIII

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Adoption of additive manufacturing for producing end-use products faces a range of limitations. For instance, quality of AM-fabricated parts varies from run to run and from machine to machine. There is also a lack of standards developed for AM processes. Another limitation is inconsistent dimensional accuracy error, which is often out of the standard tolerancing range. To tackle these challenges, this work aims at predicting scaling ratio for each part separately depending on its placement, orientation and CAD characteristics. Recent attention to machine learning techniques as a tool for data analysis in additive manufacturing shows that such methods as classical artificial neural networks (ANN), such as multi-layer perceptron (MLP), and convolutional neural networks (CNN) have a great potential. For the data collected on polymer powder bed fusion system (EOS P395), MLP outperformed CNN based on accuracy of prediction and mean squared error. The predicted scaling ratio can be used to adjust size of the parts before fabrication.

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Section: Introductionmentioning

confidence: 99%