Statistical analysis of dimensional accuracy in additive manufacturing considering STL model properties

Baturynska, Ivanna

doi:10.1007/s00170-018-2117-4

Cited by 38 publications

(28 citation statements)

References 14 publications

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“…The PCC's value will result in the interval [-1, +1], where negative values indicate an inverse relationship between the parameters while a positive value indicates a direct relationship. A value of 1 represents the best correlation while 0 value will indicate no correlation at all [28,29].…”

Section: Methodsmentioning

confidence: 99%

Mode I Fracture Toughness of Polyamide and Alumide Samples obtained by Selective Laser Sintering Additive Process

2020

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Selective Laser Sintering is a flexible additive manufacturing technology that can be used for the fabrication of high-resolution parts. Alongside the shape and dimension of the parts, the mechanical properties are essential for the majority of applications. Therefore, this paper investigates dimensional accuracy and mode I fracture toughness (KIC) of Single Edge Notch Bending samples under a Three Point Bending fixture, according to the ASTM D5045-14 standard. The work focuses on the influence of two major aspects of additive manufacturing: material type (Polyamide PA2200 and Alumide) and part orientation in the building environment (orientations of 0°, 45° and 90° are considered). The rest of the controllable parameters remains constant for all samples. The results reveal a direct link between the sample densities and the dimensional accuracy with orientation. The dimensional accuracy of the samples is also material dependent. For both materials, the angular orientation leads to significant anisotropic behavior in terms of KIC. Moreover, the type of material fundamentally influences the KIC values and the fracture mode. The obtained results can be used in the development of additive manufactured parts in order to obtain predictable dimensional tolerances and fracture properties.

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Section: Methodsmentioning

confidence: 99%

Mode I Fracture Toughness of Polyamide and Alumide Samples obtained by Selective Laser Sintering Additive Process

2020

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“…However, there is a limited number of studies that have attempted to predict mechanical properties based on the part positioning in the build chamber. Similar research was performed for the investigation of dimensional accuracy for PA12 based on the part positioning in the build chamber, part orientation, and STL model properties (number of mesh triangles, volume, and surface) [11]. Using a similar strategy would contribute to the development of the schematic approach of positioning parts in the build chamber based on their requirements, and thus an increasing area of build chamber utilization, which would lead to cheaper and more sustainable production.…”

Section: Introductionmentioning

confidence: 96%

“…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%

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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“…However, there is a limited number of studies that have made an attempt to predict mechanical properties based on the part positioning in the build chamber. Similar research was performed for investigation of dimensional accuracy for PA12 based on the part positioning in the build chamber, part orientation and STL model properties (number of mesh triangles, volume and surface) [11]. Using similar strategy would contribute to development of the schematic approach of positioning parts in the build chamber based on their requirements, and thus, increasing area of build chamber utilization, which would lead to cheaper and more sustainable production.…”

Section: Introductionmentioning

confidence: 96%

“…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 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%

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

Baturynska¹

2019

Preprint

Self Cite

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Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the 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). 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 have prediction accuracy higher than 80% only 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 material properties, these models need to be improved in the future based on additional experimental work.

show abstract

Statistical analysis of dimensional accuracy in additive manufacturing considering STL model properties

Cited by 38 publications

References 14 publications

Mode I Fracture Toughness of Polyamide and Alumide Samples obtained by Selective Laser Sintering Additive Process

Mode I Fracture Toughness of Polyamide and Alumide Samples obtained by Selective Laser Sintering Additive Process

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

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

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