PySLM 0.5

PySLM 0.5 has had a long incarnation. It has been waiting in anticipation for the past year and delayed due to challenges with the coding and ensuring cross-compatibility. It is an exciting release and a testament to the relative maturity of the project. Already, it is fantastic to observe that it is providing a great positive contribution and benefit to the research in the Additive Manufacturing community. Once again, I wish to personally thank everyone’s support developing this along the way.

The highlight of the 0.5 release is the addition of the new Support Module. The Support Module provides the building-blocks and the infrastructure to identify and extract support volumes, and generate their support structures based on meshes provided as input. The module has been in development in the background for over two-three years and finally, reaching a level of maturity that was in a position to release into the public.

The tools include the usual and standard technique of extracting overhang surfaces, edges and points based on the on their facial connectivity which was discussed in a previous post. These surfaces are used as the input in a ray-tracing approach for identifying precise volumetric block support structures as shown above. Unlike most implementations available externally, these conform to the boundaries of the part, utilising a new boolean CSG library PyCork, which provides a cross-platform Python implementation of the Cork Library. Due to limitations in existing CSG approaches available, a fast GPU based ray-trace approach is utilised to project identified support surfaces and create a high-resolution projection height-map to locate self-intersections like below. Furthermore, these provide additional flexibility to create alternatives approaches, such as those suitable for other manufacturing processes e.g. point-support structures (e.g. in SLA) or tree like support structures.

GPU Generated Depth Projection Maps

Each support surface identifies self-intersections with the original part and those with the build platform. The regions are segmented using an image processing technique based on an overhang tolerance and transformed into polygon boundaries. Simplification is necessary and use a combination of the Douglas-Peucker algorithm within scikit image’s approximate_polygon function and b-spline fitting tool available in Scipy. The boundary simplification is useful to alleviate issues when encountering sharp features extracted from jagged edges in the support regions.

These volumetric regions provide the foundational elements for constructing sophisticated support structures, especially those used within SLM systems. To maximise productivity, provide greater control over controlling distortion due to residual stress, grid-truss based support structures have been utilised for over a decade in SLM. Unfortunately, I have yet an to come across a known implementation that exists both in literature nor open-source code to generate these structures. Below is an example of conformal grid-truss geometry generated for a complex topology optimised bracket component.

In the implementation, the grid truss structure is generated by taking cross-sections throughout the support volumes and using a geometric polygon operations offered by the ClipperLib. The truss is formed by generating hatch lines that are offset and union to create a truss. This approach provides flexibility to design different structures. Afterwards, 3D triangular meshes are generated from the polygon boundaries which are mapped back onto the original support volume. Doing this efficiently is challenging given the potential size and number of support structures that can be generated.

Under own testing, the implementation is reliable for most geometries, although there are few known cases where the algorithm will not work. It is acknowledged that the support module is not intended to be a direct replacement for commercial software, rather, provide a working reference that researchers and general users can understand, adapt and utilise in their own work/research or part of a pipeline.

An example script for generating a support structure can be found on the Github repository in examples/

The installation of PySLM 0.5 has soft-dependencies for using the support module due to the additional algorithms required. Please, ensure that these are all installed and that there is a working OpenGL 2.1 installation (via Vispy and PyQt5) on your system. The core functionality offered in PyQt5 may be utilised without these extra dependencies for those wanting a simplified installation.

Opportunities to explore:

There are many opportunities that are available to investigate using the new functionality available: e.g. lattice based support structures, alternative approaches support structures, novel scan strategies suitable for SLM. Parametric and optimisation of the support structure design – e.g. automated support generation. The tool will aid those working in modelling and simulation: optimisation of designs prior to printing to account for distortion, control of thermal history, globally optimise parts for build-cost-time models. It would be great to hear from anyone on their experience using this functionality.

Further improvements to PySLM

The remainder of the release has a few improvements and fixes to the core functionality and its documentation. It is important to highlight the analysis module for predicting build times – accounting for scan vector jump delays, jump speed, point exposure delays that have an incremental impact on the overall build time. Additionally, the release has been tested across all platforms (Windows, Linux, Mac OS X) and further testing and maturity of libSLM‘s translators continue: including a working implementation of EOS .sli format.

The full release log for PySLM 0.5 may be found in Changelog.MD

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