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Systematic Feature Extraction and Feature-based Manufacturing Process Selection for Hybrid Manufacturing

Jha, Smriti
http://orcid.org/0000-0002-9352-342X
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1660817377523143

Abstract Details

2022, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
With so many different manufacturing processes around, the industry has started combining different processes to combine their respective advantages and counter the disadvantages. Hybrid manufacturing processes, a combination of additive and subtractive methods, are being explored to increase the overall efficiency and part quality. There is a need for the selection of the optimal set of processes based on the part geometry and part material. The part features, in turn, directly affect the selection of the optimal sequence of processes. This thesis explores the idea of evaluating the STL models of a part based on Design for Manufacturing (DFM) and Design for Additive Manufacturing (DfAM) rules to select the optimal combination of subtractive and additive processes for manufacturing the part. The metrics are extracted directly from the features of the STL model by performing a slice-by-slice analysis of the part to determine the combinations of the geometric demarcation point between various processes. For additive processes, the list of metrics extracted includes the volume of material to be added, staircase error, sharp corner, and support structure volume. The metrics considered for subtractive processes are the volume of material removal, tool inaccessibility, part geometry complexity, and sharp internal corners that may be difficult to machine. For each part geometry and build orientation, the overall final score is calculated for subtractive and additive process metrics, and a decision is made on the optimal combination and demarcation points for using the various processes for manufacturing the part. Several case studies of varying complexity have been presented for calculating the metrics and determining the optimal process plans.
Sam Anand, Ph.D. (Committee Member)
Xinyi Xiao, Ph.D. (Committee Member)
Manish Kumar, Ph.D. (Committee Member)
86 p.
Mechanical Engineering
Hybrid Manufacturing; Decision making; Process selection; Design for Manufacturing; Design for Additive Manufacturing; Process planning

Recommended Citations

Citations

  • Jha, S. (2022). Systematic Feature Extraction and Feature-based Manufacturing Process Selection for Hybrid Manufacturing [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1660817377523143

    APA Style (7th edition)

  • Jha, Smriti. Systematic Feature Extraction and Feature-based Manufacturing Process Selection for Hybrid Manufacturing. 2022. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1660817377523143.

    MLA Style (8th edition)

  • Jha, Smriti. "Systematic Feature Extraction and Feature-based Manufacturing Process Selection for Hybrid Manufacturing." Master's thesis, University of Cincinnati, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1660817377523143

    Chicago Manual of Style (17th edition)

ucin1660817377523143
114
© 2022, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.