XL Fossil Display

Table of Contents

Summary

This small project covers my process of converting commercially available, extra deep, shadow-boxes to display boxes capable of supporting large and heavy fossils. The particular fossils used are from a recent fossil hunting trip to The Ernst Quarries1, aka Sharktooth Hill, near Bakersfield, CA on May 2, 2020! The motivation for this project comes from the fact that some of those fossils (such as the whale vertebrae) were too large to fit into normal sized, wall-mounted, shadow-boxes.

Materials

  1. black acrylic paint2
  2. one $\frac{3}{16}$“x3"x36” balsa slat3
  3. cyanoacrylate glue
  4. two extra deep shadow-boxes4
  5. one adhesive black foam sheet5
  6. foam paint brush6
  7. Razor Saw7
  8. High grit sanding block or paper (>= 220)
  9. 30lb Hangers8

Below you’ll find pictures with captions (if clicked) detailing the salient features of each step in the build process.

Happy hunting!


  1. http://sharktoothhillproperty.com/ ↩︎

  2. https://www.michaels.com/diy-home-decor-acrylic-paint-by-artminds-3.2oz/M10515573.html ↩︎

  3. https://www.michaels.com/revel-36in-balsa-wood/M20000183.html?dwvar_M20000183_size=3%2F32%22%20x%203%2F32%22 ↩︎

  4. https://www.michaels.com/black-extra-deep-shadowbox-10x10-studio-decor/10229048.html ↩︎

  5. https://www.michaels.com/9x12-adhesive-foam-sheet-by-creatology/M10032002.html ↩︎

  6. https://www.michaels.com/diy-home-foam-brush-set-by-artminds/10509418.html ↩︎

  7. https://www.michaels.com/huron-razor-saw-set/10061155.html?cm_mmc=PLASearch-_-google-_-MICH_Shopping_US_N_Craft+%26+Hobbies_N_Smart_LocalOnly_N-_-&Kenshoo_ida=&kpid=go_cmp-10192716624_adg-110379393028_ad-438740538153_pla-917599314418_dev-c_ext-_prd-10061155&gclid=Cj0KCQjw3ZX4BRDmARIsAFYh7ZKedBMd3mMpH42eMjzIPoMJgzdVLlppm7C7hsZIzm36rw_gsuTJWB4aAoyjEALw_wcB ↩︎

  8. https://www.homedepot.com/p/OOK-30-lb-Picture-Hanger-Value-Pack-40-Pack-534281/301942179?MERCH=REC-_-pipsem-_-100175124-_-301942179-_-N ↩︎

Software Engineer

My research interests include computer vision and deep learning.