Customizable Screwdriver Holder - Honeycomb pattern design

This customizable screwdriver holder is created using Python script, Honeycomb Designer, available on GitHub.

The script helps you to generate a honeycomb pattern with fully adjustable hole shapes, sizes, spacing, and quantities, making it easy to create a holder tailored to your specific tools.

Ready-to-print models: Few completed screwdriver holder models with various dimensions are included as examples. They are available for direct download and can be printed without using the Python script. However, these are just sample configurations and may not perfectly match your tools or requirements. For a fully customized design, I recommend using the Honeycomb Designer Python script to generate a hole pattern tailored to your specific needs.

Printing recommendations:
Please keep in mind that printing the honeycomb grid can be quite time-consuming, especially for larger models or designs with many holes. Before generating your model, carefully consider the hole shapes, sizes, spacing, and overall dimensions. It is worth planning your design in advance to avoid unnecessarily long print times.

Recommended print settings:

• Material: PLA is sufficient

• Layer height: 0.24 mm

• Supports: Not required

• Infill: 20% (you can experiment with lower infill values to reduce print time and material consumption)

These are recommended settings, not strict requirements. Feel free to adjust them to suit your printer and needs.

Please note:The script only generates a customizable hole pattern, which must be exported as a Python script and executed in Autodesk Fusion to create the base 3D model. Additional modeling and finishing are required to make the design ready for 3D printing. Basic knowledge and experience with Autodesk Fusion are recommended.

For download, instructions and more information about the script, visit:
https://github.com/matuskolcun/honeycomb_generator

AI Disclaimer:AI was used to assist in developing the Python script, not to generate the 3D model itself. The model is generated using a parametric algorithm, not generative AI.