BlankLocalizationCore

This repository contains the reference implentation for the multi operation blank localization technique described in our paper Multi-operation optimal blank localization for near net shape machining. The paper is available is here: https://www.sciencedirect.com/science/article/pii/S0007850623000884.

@article{cserteg:2023_MultioperationOptimalBlank,
  title = {Multi-Operation Optimal Blank Localization for near Net Shape Machining},
  author = {Cserteg, Tamás and Kovács, András and Váncza, József},
  year = {2023},
  month = jun,
  journal = {CIRP Annals},
  issn = {0007-8506},
  doi = {10.1016/j.cirp.2023.04.049},
}

Blank localization briefly

The goal of multi operation blank localization is to align the CNC machining code for the rough (e.g. cast, 3D printed, etc.) parts. When doing so, one must consider two important factors:

  • leaving enough material to be removed by the tool (machining allowance)
  • respecting the dimensional tolerances between features (defined on the part drawing)

Our paper proposes a method, that ensures a proper machining allowance (minimum requirement), while trying to optimize to the center of the tolerance fields between features.

The documentation goes through a detailed example of the process while showing how to use the package.

The package is registered in the general registry, so it can be installed via running:

] add BlankLocalizationCore

For the exaplanation on how the package works, please read through the Example page.

Acknowledgements

This package couldn't have been created without the great people behind the following projects (as well as the whole Julia ecosystem):

Our work has been published in several papers, one of them is still in print. The list (in chronological order):

  • Digital twin assisted workpiece referencing for compensating the stock deviation of casted parts: link to paper
  • Multi-operation optimal blank localization for near net shape machining: link to paper
  • Multi-operation blank localization with hybrid point cloud and feature-based representation: link to pre-print paper