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Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes

Chowdhury, Sushmit
http://orcid.org/0000-0002-8092-7003
http://rave.ohiolink.edu/etdc/view?acc_num=ucin147982071583238

Abstract Details

2016, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Additive manufacturing (AM) processes involve the fabrication of parts in a layer wise manner. The layers of material are deposited using a variety of established methodologies, the most popular of which involve either the use of a powerful laser to sinter/melt successive layers of metal/alloy/polymer powders or, the deposition of layers of polymers through a heated extrusion head at a controlled rate. The thermal nature of these processes coupled with the varying contours of the part at different heights, causes the development of temperature gradients throughout the part and as a result, the part undergoes irregular deformations. These deformations ultimately lead to dimensional inaccuracies in the manufactured part. An Artificial Neural Network (ANN) based methodology is proposed in this research to make the required compensations to the part’s geometric design, which will help to counter the thermal deformations in the manufactured part. In this methodology, a feed-forward ANN model is trained using an error backpropagation algorithm to study part deformations resulting in the part during the AM process. The trained network is subsequently implemented on the part Stereolithography (STL) file to effect the required geometrical compensations. Two case studies are presented to illustrate the implementation of the proposed methodology. A novel approach to evaluate the final part profile resulting from the AM process, with respect to the original part CAD model profile has also been developed. This metric is used to quantify the performance of the proposed methodology. The results of the case studies show substantial improvement in the part accuracy and thus validate the ANN based geometric compensation approach.
Sam Anand, Ph.D. (Committee Chair)
Michael Alexander-Ramos, Ph.D. (Committee Member)
Jing Shi, Ph.D. (Committee Member)
51 p.
Mechanical Engineering
Additive Manufacturing; Thermal Deformation; Compensation; Artificial Neural Network

Recommended Citations

Citations

  • Chowdhury, S. (2016). Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin147982071583238

    APA Style (7th edition)

  • Chowdhury, Sushmit. Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes. 2016. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin147982071583238.

    MLA Style (8th edition)

  • Chowdhury, Sushmit. "Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes." Master's thesis, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin147982071583238

    Chicago Manual of Style (17th edition)

ucin147982071583238
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This open access ETD is published by University of Cincinnati and OhioLINK.