Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework

Baturynska, Ivanna; Semeniuta, Oleksandr; Martinsen, Kristian

doi:10.1016/j.procir.2017.12.204

Cited by 133 publications

(57 citation statements)

References 24 publications

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“…These techniques are chosen for the reason that machine learning methods have an ability to find hidden patterns in the data that could be structured or unstructured (which is not possible with classical methods like DOEs), and then use those patterns to make predictions for unknown problems [9]. Therefore, data presented in different studies can be used together with data collected from practical experiments.…”

Section: Discussionmentioning

confidence: 99%

“…The current state-of-the-art [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] describes the importance of part orientation, powder morphology, and machine process parameters as a means towards the control and management of variation in polymer powder bed fusion system. Among the most investigated additive manufacturing (AM) machine process parameters are laser power, scan speed, hatch distance, scan strategy, beam speed, melting temperature, and powder bed temperature [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques have an ability to find hidden patterns in the data, and then accumulated information can be transformed to the new unknown problems [9]. In addition, machine learning techniques do not require a structured way of data collection, and thus they have a benefit over other classical methods when it comes to the conditions similar to real-time manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Baturynska

2019

Applied Sciences

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Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Baturynska

2019

Applied Sciences

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“…Lately, Powder Bed Fusion (PBF) AM technology are used in manufacturing of end-use parts. To realize quality final product using PBF technology, there is a need for higher requirements (Baturynskaa et al, 2018). Baturynskaa et al (2018) study proposed that the process parameters can be optimized using Finite Element Method (FEM) and ML techniques to evaluate and optimized AM process parameters.…”

Section: Recent Applications Of Machine Learning With Big Data In Addmentioning

confidence: 99%

A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

Alabi¹,

Botef²

2018

American Journal of Engineering and Applied Sciences

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Additive Manufacturing (AM) which is also known as 3D printing technology; is recognized as a new paradigm for manufacturing industry. Additive manufacturing is rapidly expanding across different sectors such as healthcare, electronics, automotive, science and engineering, education, dental, etc. Machine Learning and Big Data are both emerging technologies which are becoming popular and gaining more attention from the industries and academic. Machine Learning is a growing field of Artificial Intelligence (AI) that allows systems to learn from data, identify patterns and make decisions with very little human involvement. On the other hand, Big Data is referred to as datasets whose size is more than the capacity of what a conventional database software tools can capture, store, manage and analyze. Lately, Machine Learning techniques and Big Data Analytics are being applied to various applications of additive manufacturing to monitor building process and enhance decisions making using data generated through different sensors or cameras. This paper explores recent applications of Machine Learning with Big Data in the field of additive manufacturing, for instance, application of machine learning in detecting defect or anomaly during build process in additive manufacturing/3D printing machine.

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“…Many studies report that AM is already used to produce end-user products, but quality still remains an issue. For example, dimensional accuracy is still an issue for such AM processes as powder bed fusion AM [2][3][4][5]. As compared to the tolerance requirements defined in the DIN 16742:2013 standard [6] for injection molding process, dimensional accuracy error of AM exceeds the defined ranges [4].…”

Section: Introductionmentioning

confidence: 99%

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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Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework

Cited by 133 publications

References 24 publications

Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

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

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